Getting Personal: Omics of the Heart

Jane Ferguson:                  Hi there. Welcome to Getting Personal: Omics of the Heart, the podcast from Circulation: Genomic and Precision Medicine. I'm Jane Ferguson, and this is Episode 36 from February 2020.                                                 First up, we have “Identification of Circulating Proteins Associated with Blood Pressure Using Mendelian Randomization” from Sébastien Thériault, Guillaume Paré, and colleagues from McMaster University in Ontario. They set out to assess whether they could identify protein biomarkers of hypertension using a Mendelian randomization approach. They analyzed data from a genome-wide association study of 227 biomarkers which were profiled on a custom Luminex-based platform in over 4,000 diabetic or prediabetic participants of the origin trial.                                                 They constructed genetic predictors of each protein and then used these as instruments for Mendelian randomization. They obtained systolic and diastolic blood pressure measurements in almost 70,000 individuals, in addition to mean arterial pressure and pulse pressure in over 74,000 individuals, all European ancestry with GWAS data, as part of the International Consortium for Blood Pressure.                                                 Out of the 227 biomarkers tested, six of them were significantly associated with blood pressure traits by Mendelian randomization after correction for multiple testing. These included known biomarkers such as NT-proBNP, but also novel associations including urokinase-type plasminogen activator, adrenomedullin, interleukin-16, cellular fibronectin and insulin-like growth factor binding protein-3. They validated all of the associations apart from IL-16 in over 300,000 participants in UK Biobank. They probed associations with other cardiovascular risk markers and found that NT-proBNP associated with large artery atherosclerotic stroke, IGFBP3 associated with diabetes, and CFN associated with body mass index.                                                 This study identified novel biomarkers of blood pressure, which may be causal in hypertension. Further study of the underlying mechanisms is required to understand whether these could be useful therapeutic targets in hypertensive disease.                                                 The next paper comes from Sony Tuteja, Dan Rader, Jay Giri and colleagues from the University of Pennsylvania and it's entitled, “Prospective CYP2C19 Genotyping to Guide Antiplatelet Therapy Following Percutaneous Coronary Intervention: A Pragmatic Randomized Clinical Trial”.                                                 They designed a pharmacode genomic trial to assess effects of CYP2C19 genotyping on antiplatelet therapy following PCI. Because loss of function alleles in CYP2C19 impair the effectiveness of clopidogrel, the team were interested in understanding whether knowledge of genotype status would affect prescribing in a clinical setting. They randomized 504 participants to genotype guided or usual care groups and assessed the rate of prasugrel or ticagrelor prescribing in place of clopidogrel within each arm. As a secondary outcome, they assessed whether prescribers adhere to genotype guided recommendations. Of genotyped individuals, 28% carried loss of function alleles. Within the genotype guided group overall, there was higher use of prasugrel or ticagrelor with these being prescribed to 30% of patients compared with only 21% in the usual care group. Within genotype individuals carrying loss of function alleles, 53% were started on prasugrel or ticagrelor, demonstrating some adherence to genotype guided recommendations.                                                 However, this also meant that 47% of people whose genotype suggested reduced effectiveness were nevertheless prescribed clopidogrel. This study highlights that even when genotype information is available, interventional cardiologists consider clinical factors such as disease presentation and may weight these more highly than genotype information when selecting antiplatelet therapy following PCI.                                                 The next paper is about “Deep Mutational Scan of an SCN5A Voltage Sensor and comes to us from Andrew Glazer, Dan Roden and colleagues from Vanderbilt University Medical Center. In this paper, the team aim to characterize the functional consequences of variants and the S4 voltage sensor of domain IV and the SCN5A gene using a high throughput method that they developed. SCN5A encodes the major voltage gated sodium channel in the heart and variants in SCN5A can cause multiple distinct genetic arrhythmia syndromes, including Brugada syndrome, long QT syndrome, atrial fibrillation, and dilated cardiomyopathy, and have been linked to sudden cardiac death.                                                 Because of this, there's considerable interest in understanding the functional and clinical consequences of different variants, but previous approaches were time consuming and results were often inconclusive with many variants being classified as uncertain significance. This newly developed deep mutational scanning approach allows for simultaneous assessment of the function of thousands of variants, making it much more efficient than low throughput patch clamping. The team assessed the function of 248 variants using a triple drug assay in HEK293T cells expressing each variant and they identified 40 putative gain of function and 33 putative loss of function variants. They successfully validated eight of nine of these by patch clamping data. Their study highlights the effectiveness of this deep mutational scanning approach for investigating variants in the cardiac sodium channel SCN5A gene and suggests that this may also be an effective approach for investigating putative disease variants and other ion channels.                                                 The next article is a research letter from Connor Emdin, Amit Khera, and colleagues from Mass General Hospital in the Broad Institute entitled, “Genome-Wide Polygenic Score and Cardiovascular Outcomes with Evacetrapib in Patients with High-Risk Vascular Disease: A Nested Case-Control Study”. In this study, the team set out to probe the utility of using polygenic risk scores to predict the risk of major adverse cardiovascular events within individuals already known to be at high cardiovascular risk and to assess whether genetic scores can identify individuals who would benefit from the use of a CETP inhibitor such as Evacetrapib. They analyze data from the ACCELERATE trial which had tested Evacetrapib in a high risk population, and they found no effect on the incidents of major adverse cardiovascular events overall. Within a nested case-control sample of individuals experiencing major CVD events versus no events, they applied a polygenic risk score and found that the score predicted major cardiovascular events.                                                 Patients in the highest quintile of the risk score were at 60% higher risk of a major cardiovascular event than patients in the lowest quintile. There was no evidence of any interaction between the genetic risk score and Evacetrapib. These data suggest that genetic risk scores may have utility in identifying individuals at high risk events but may not have utility in identifying individuals who may derive more benefit from CETP inhibition. The next letter concerns “Epigenome-Wide Association Study Identifies a Novel DNA Methylation in Patients with Severe Aortic Valve Stenosis” and comes from Takahito Nasu, Mamoru Satoh, Makoto Sasaki and colleagues from Iwate Medical University in Japan. They were interested in understanding whether differences in DNA methylation could underlie the risk of aortic valve stenosis. They conducted an EWAS or epigenome-wide association study of peripheral blood mononuclear cells or PBMCs from 44 individuals with aortic stenosis and 44 disease free controls.                                                 They collected samples at baseline before a surgical intervention in the individuals with aortic stenosis and collected a follow-up sample one year later. They found that DNA methylation at a site on chromosome eight mapping to the TRIB1, or tribbles homolog one gene, was lower in the aortic stenosis group than in the controls at baseline. They replicated the association in an independent sample of 50 cases and 50 controls. TRIB1 MRNA levels were higher in the aortic stenosis group than the controls. When they looked at methylation status one year after aortic valve replacement or a transcatheter aortic valve implantation in patients with stenosis, they found that DNA methylation had increased in the cases while TRIB1 MRNA decreased. These data suggests that methylation status of TRIB1 and expression of TRIB1 may relate to the disease processes in aortic stenosis such as hemodynamic dysregulation and they can be reversed through surgical intervention. Changes in the methylation status of TRIB1 could be a novel biomarker of response to aortic valve replacement.                                                 The next letter comes from Niels Grote Beverborg, Pim van der Harst, and colleagues from University Medical Center Groningen and is entitled, “Genetically Determined High Levels of Iron Parameters Are Protective for Coronary Artery Disease”. Their study addresses the conflicting hypotheses that high iron status is either deleterious or protective against cardiovascular disease. The team constructed genetic predictors of serum iron status using 11 previously identified snips and tested the genetic association with CAD in UK Biobank data from over 408,000 white participants. Overall, the genetic score for higher iron status was associated with protection against CAD. Ten of the snips suggested individual neutral or protective effects of higher iron status on CAD, while one iron increasing snip was associated with increased risk of disease but this was thought to be likely through an iron independent mechanism. Overall, these data suggest that a genetic predisposition to higher iron status does not increase risk of CAD and is actually protective against disease.                                                 The final letter is entitled, “Confidence Weighting for Robust Automated Measurements of Popliteal Vessel Wall MRI” and comes from Daniel Hippe, Jenq-Neng Hwang, and colleagues from the University of Washington. They were interested in assessing whether images of popliteal artery wall incidentally obtained during knee MRI as part of an osteoarthritis study could be used to study the development and progression of atherosclerosis. They developed an automated deep learning based algorithm to segment and quantify the popliteal artery wall in images obtained over 10 years in over 4,700 individuals. Their approach, which they named FRAPPE, or fully automated and robust analysis technique for popliteal artery evaluation, was able to reduce the average time required for segmentation analysis from four hours to eight minutes per image. They applied weights based on confidence for each segment to automatically improve the accuracy of aggregate measurements such as mean wall thickness or mean lumen area. Their data suggest that this automated method can rapidly generate useful information on atherosclerosis from MRI images obtained as part of other studies. When combined with other data. This approach may facilitate novel discovery in secondary analyses of existing studies in an efficient and cost effective way.                                                 And that's all for issue one of 2020. Come back next time for more of the latest papers from Circulation: Genomic and Precision Medicine. Speaker 2:                           This podcast is copyright American Heart Association 2020.  

Jane Ferguson:                  Hi, everyone. Welcome to episode 35 of Getting Personal: Omics of the Heart, the podcast from Circulation: Genomic and Precision Medicine. I'm Jane Ferguson, an assistant professor of medicine at Vanderbilt University Medical Center, and an associate editor at Circulation: Genomic and Precision Medicine. This episode is first airing in December 2019. Let's see what we published this month.                                                 Our first paper is an “Integrated Multiomics Approach to Identify Genetic Underpinnings of Heart Failure and Its Echocardiographic Precursors: The Framingham Heart Study” from Charlotte Anderson, Ramachandran Vasan and colleagues from Herlev and Gentofte Hospital, Denmark and Boston University.                                                 In this paper, the team investigated the genomics of heart failure, combining GWAS with methylation and gene expression data, to prioritize candidate genes. They analyzed four heart failure related and eight echocardiography related phenotypes in several thousand individuals, and then identified SNPs, methylation markers, and differential gene expression associated with those phenotypes. They then created scores for each gene, based on the rank of statistical significance, aggregated across the different omics analysis.                                                 They examined the top ranked genes for evidence of pathway enrichment, and also looked up top SNPs for PheWAS associations in UK Biobank, and examined tissue specific expression in public data. While their data cannot definitively identify causal genes, they highlight several genes of potential relevance to heart failure pathogenesis, which may be promising candidates for future mechanistic studies.                                                 The next paper is “Genetic Determinants of Lipids and Cardiovascular Disease Outcomes: A Wide-Angled Mendelian Randomization Investigation” and comes from Elias Allara, Stephen Burgess and colleagues, from the University of Cambridge and the INVENT consortium. While it has been established, therapies to lower LDL cholesterol and triglycerides lead to lower risk of coronary artery disease, it remains less clear whether these lipid lowering efforts can also reduce risk for other cardiovascular outcomes. The team set out to address this question using Mendelian randomization. They generated genetic predictors of LDL cholesterol and triglycerides using data from the Global Lipids Genetics Consortium, and then assessed whether genetically predicted increased LDL and triglycerides associated with risk of cardiovascular phenotypes using UK Biobank data. Beyond CAD, they found that higher LDL was associated with abdominal aortic aneurysm and aortic valve stenosis. High triglyceride levels were positively associated with aortic valve stenosis and hypertension, but inversely associated with venous thromboembolism and hemorrhagic stroke.                                                 High LDL cholesterol and triglycerides were also associated with heart failure, which appeared to be mediated by CAD. Their data suggests that LDL lowering may have additional cardiovascular benefits in reducing aortic aneurism and aortic stenosis, while efforts to lower triglycerides may reduce the risk of aortic valve stenosis, but could result in increased thromboembolic risk.                                                 Next up is a paper from Steven Joffe, G.L. Splansky and colleagues, from the University of Pennsylvania and Boston University, on “Preferences for Return of Genetic Results Among Participants in the Jackson Heart Study and Framingham Heart Study”. There has been increasing discussion and concern about how to handle genetic data, and whether genetic results should be returned to participants, and under which circumstances. In this study, the teams that had to assess what participants themselves think. They query participants in the Jackson Heart Study, the Framingham Heart Study and the FHS Omni cohort, presenting them with potential scenarios that varied by five factors including phenotype severity, actionability, reproductive significance and relative of the absolute risk of the phenotype.                                                 Across all scenarios, 88 to 92% of respondents said that they would definitely or probably want to learn their result. In Jackson Heart Study respondents, factors increasing the desire for results included a positive attitude towards genetic testing, lower education, higher subjective numeracy, and younger age. The five pre-identified factors did not affect desire to receive results in Jackson Heart Study. Among Framingham Heart Study respondents, desire for results was associated with higher absolute risk, presentability, reproductive risk and positive attitudes towards genetic testing. Among FHS Omni respondents, desire for results was associated with positive attitudes towards genetic testing and younger age. Overall, these data show that across a variety of studies, there a high level of interest in receiving genetic results and that these are not necessarily linked to the phenotype or clinical significance of the results themselves.                                                 The next paper concerns “Peripheral Blood RNA Levels of QSOX1 and PLBD1 Are New Independent Predictors of Left Ventricular Dysfunction after Acute Myocardial Infarction” and this comes from Martin Vanhaverbeke, Peter Sinnaeve and colleagues, from University Hospital Leuven. They were interested in understanding whether they could identify subsequent left ventricular dysfunction in patients who suffered an acute myocardial infarction. They obtained blood and performed RNA-Seq at multiple time points in 143 individuals, following acute MI, to identify transcripts that were associated with subsequent LV dysfunction. They validated candidate gene transcripts in a validation sample of 449 individuals, confirming that expression of QSOX1 and PLBD1 at admission, were associated with LV dysfunction at follow-up. Adding QSOX1 to a model, consisting of clinical variables and cardiac biomarkers, including NT proBNP, had an incremental predictive value. They took their findings to a pig model and found that whole blood expression of both genes was associated with neutrophil infiltration in these ischemic myocardium. This study suggests that expression of QSOX1 and PLBD1 following MI, may have utility in predicting development of LV dysfunction and may be markers of cardiac inflammation.                                                 The next paper is a research letter from Hanna Hanania, Denver Sallee and Dianna Milewicz, from the University of Texas Health Science Center, and Emory University School of Medicine. Who set out to answer the question, “Do HCN4 Variants Predisposed to Thoracic Aortic Aneurysms and Dissections?” Previous work has suggested that rare variants in HCN4 associated with thoracic aortic disease, including ascending aortic dilation, left ventricular noncompaction cardiomyopathy, and sinus bradycardia. However, the evidence for disease segregation was relatively weak. The team set out to explore these potential associations using exome sequencing data from 521 individuals, from 347 unrelated families with heritable thoracic aortic disease, as well as 355 individuals with early onset sporadic aortic dissections, but no family history of disease. They identified a missense variant G482R, which segregated with disease in four unrelated families, was absent from the nomad database and was predicted to disrupt protein function and have deleterious effects. Their data support the evidence that HCN4 rare variants can cause heritable thoracic aortic disease with left ventricular noncompaction cardiomyopathy and bradycardia.                                                 Our final paper is a white paper from H. Li, X. J. Luo and colleagues, from the National Heart, Lung and Blood Institute at the NIH, and will likely interest anybody who applies for NIH grants, which I'm assuming is most of you listening to this podcast. Their paper on, “Portfolio Analysis of Research Grants in Data Science Funded by the National Heart, Lung, and Blood Institute”, delves into the type of data science research funded by NHLBI between fiscal year 2008 and fiscal year 2017. They identified 630 data science focused grants, funded by NHLBI, using keywords for bioinformatics and computational biology. They then analyzed the distribution of these grants across different disease areas and compared the results to data science grants funded by other NIH institutes or centers. Around 64% of funded grants were for cardiovascular disease with 22% in lung and airway disease, 12% in blood disease and 2% in sleep.                                                 NHLBI's investment in data science research grants averaged about 1% of its overall research grant investment, and this remained constant over the 10-year period. However, this proportion does not include other large scale investment by NHLBI in building data science platforms through other mechanisms. Of relevance to our listeners across all institutes, most funded data science research grants were related to genomics and other omics data. In this paper they include lots of graphs breaking down grant distributions across different categories, so it's worth a look as you plan your next grant application.                                                 That's all for December and the final episode of 2019. Thanks for listening and happy holidays to all who celebrate. I'm excited to be back in 2020, to kick off the next decade of exciting advances in genomic and precision cardiovascular medicine.                                                 This podcast was brought to you by Circulation: Genomic and Precision Medicine, and the American Heart Association Council on Genomic and Precision Medicine. This program is copyright American Heart Association 2019.  

Jane Ferguson:                  Hi there. Welcome to the November 2019 issue of Getting Personal: Omics of the Heart. I'm Jane Ferguson. This is your podcast from Circulation: Genomic and Precision Medicine. Let's get started.                                                 First up from Eric Curruth, Christopher Haggerty and colleagues from Geisinger, we have a paper entitled, “Prevalence and Electronic Health Record-based Phenotype of Loss-of-function Genetic Variance in Arrhythmogenic Right Ventricular Cardiomyopathy-associated Genes”. In this study, the team set out to understand the phenotypic consequences of variants and desmosome genes which has been associated with a arrhythmogenic right ventricular cardiomyopathy or ARVC. In clinical genetic testing, secondary findings of pathogenic or likely pathogenic variants in desmosome genes are recommended for clinical reporting. However, relatively little is known about the phenotypic consequences of these variants in a general clinical population.                                                 The team obtained whole exome sequencing data for over 61,000 individuals from the DiscovEHR cohort, part of the Geisinger MyCode Community Health Initiative. They then screened individuals for a putative loss of function variants in PKP2, DSC2, DSG2, and DSP. They evaluated ARVC diagnostic criteria using previously conducted ECG and echocardiograms and performed a phenom-wide association study or PHeWAS using EHR derived phenotypes. They found 140 people with an ARVC variant in one of the four genes, none of whom had an existing diagnosis of ARVC in the EHR.                                                 Further, there were no measurable differences in their ECG or echocardiogram findings compared with matched controls. There were also no associations with any heart disease phenotypes as assessed by PHeWAS. Overall, they report a prevalence of ARVC loss of function variants of around one in 435 in a general clinical population of predominantly European descent, but they did not find evidence that these variants associated with specific phenotypes. Thus, the clinical relevance of putative loss of function variants in desmosome genes still remains to be determined.                                                 The next paper is titled, “MRAS Variants Cause Cardiomyocyte Hypertrophy in Patients-specific iPSC-derived Cardiomyocytes”. Additional evidence for MRS as a definitive Noonan syndrome susceptibility gene. This comes from Erin Higgins, Michael Ackerman, and colleagues from the Mayo Clinic. They were interested in understanding whether a recently identified Noonan syndrome variant in the MRS gene was necessary and sufficient to cause Noonan syndrome with cardiac hypertrophy. They generated induced pluripotent STEM cell or IPS C lines from patient derived cells carrying the glycine 23 veiling variant and MRS. In addition to isogenic control cells where the pathogenic variant was corrected back to wild-type using CRISPR CAS nine gene editing, they also created a disease model cell line by introducing the MRS variant into unrelated control cells.                                                 They then comprehensively characterized the phenotypes of the three cell lines using a variety of approaches including microscopy, immunofluorescence, single cell RNA seek, Western blot, qPCR, and live cell calcium imaging. Both the patient derived and the disease model IPS cardiomyocytes were larger than control cells and demonstrated changes in gene expression and intracellular pathway signaling characteristic of cardiac hypertrophy. The patient and disease model cells also displayed impaired calcium handling. Through in-vitro phenotyping, the team was able to demonstrate that the glycine 23 veiling MRS variant elicits a cardiac hypertrophy phenotype and IPSC cardiomyocytes, that strongly suggests that this variant is responsible for the observed Noonan syndrome associated cardiac hypertrophy in the effected patients.                                                 Next up is a review from Christopher Lee, Iftikhar Kullo, and colleagues also from the Mayo Clinic on “New Case Detection by Cascade Testing in Familial Hypercholesterolemia: A Systematic Review of the Literature”. In this review they set out to systematically assess cascade testing programs for familial hypercholesterolemia, a disease which has a prevalence of about one and 250 but is estimated to be diagnosed in under 10% of patients. They identified published studies across the world which had conducted cascade testing and had reported the number of index cases and number of relatives tested and had also specified their methods of contacting relatives and testing.                                                 Using these criteria, they identified 10 studies for inclusion spanning several European countries, South Africa, New Zealand, Australia, and Brazil. The team calculated the proportion of relatives testing positive and the number of new cases per index case to facilitate comparison between studies. The mean number of programs was 242 with an average of 826 relatives per study. The average yield was 45%, ranging from 30 to 60%. the mean new cases per index case was 1.65 with a range of 0.22 to 8.0. Studies that use direct contact versus indirect contact for relatives and those that tested beyond first degree relatives had a greater yield. Further, active sample collection versus collection at clinic and using genetic testing versus biochemical testing was similarly associated with a higher yield. Despite differences between the United States and other countries, applying these strategies when establishing new cascade testing programs in the US may help promote success of these programs.                                                 Our next paper concerns “Randomization of Left-right Asymmetry and Congenital Heart Defects: The Role of DNAH5 in Humans and Mice”. And this was conducted by Tabea Nöthe-Menchen, Heymut Omran, and colleagues from University Children's Hospital Muenster and the PCD study group. They were interested in understanding the relationship between congenital heart defects and laterality defects where internal organs are atypically positioned, such as in a mirror image as occurs in situs inversus. Ciliary dyskinesia is thought to play a role in situs inversus and the most frequently mutated gene in primary ciliary dyskinesia is DNAH5. The team does hypothesize that DNAH5 mutations may play a role in congenital heart disease. They characterized phenotypes in 132 patients with primary ciliary dyskinesia carrying disease causing DNAH5 mutations and also studied left right access establishment using a DNAH5 mutant mouse model.                                                 66% of patients in their study had laterality defects, 88% of whom presented with situs inversus totalis and 6% presented with congenital heart disease. In the mass model, they observed immotile cilia, impaired flow with the left right organizer and randomization of nodal signaling with normal reversed or bilateral expression of key molecules. Their study thus demonstrates that mutation of DNAH5 is associated with congenital heart defects and they further highlight the ciliary mechanisms underlying defects and development of left right positioning during embryogenesis. Consideration of celiopathy related symptoms may be warranted when examining patients with congenital heart defects.                                                 Next up, we have a research letter from William Goodyear, Marco Perez and colleagues from Stanford University on “Broad Genetic Testing in a Clinical Setting Uncovers a High Prevalence of Titan Loss-of-Function Variants in Very Early-Onset Atrial Fibrillation”. They were interested in understanding genetic determinants of atrial fibrillation and hypothesized that causal genetic variants would be enriched in individuals with very early onset AF, who are diagnosed with AF under the age of 45 with no other significant comorbidities. They identified 25 families comprising 23 unrelated patients with very early onset AF who had been evaluated and received genetic counseling at Stanford between 2014 and 2018.                                                 The mean age of AF diagnosis was 27.2 years and 76% of patients were male. 40% of patients had a first or second degree relative with very early onset AF, while 36% at first or second degree relatives with either early onset idiopathic cardiomyopathy, unexplained sudden death or strokes. 85% of patients were identified as having at least one rare variant in a cardiomyopathy associated gene. Six patients carried actionable pathogenic or likely pathogenic variants, four of which were in the titan gene.                                                 A subset of individuals were further evaluated by MRI or computed tomography on average 817 days after their first presentation and this revealed high rates of cardiac abnormalities including reduced ventricular function, chamber enlargement, borderline LV non compaction, or late gadolinium enhancement. These were not noted on echocardiogram at presentation, suggesting there may have been subsequent disease development or progression. Overall, this study highlights a high rate of familial disease and implicates an association between very early onset AF and rare variants in titan before the clinical onset of cardiomyopathy.                                                 The final letter this month comes from Yu Xia, Shaoxian Chen, Ping Li, Jian Zhuang and colleagues from Guangdong Academy of Medical Sciences and is entitled, “A Novel Mutation in MYH6 in Two Unrelated Chinese Han Families with Familial Atrial Septal Defect”. They report on two unrelated families who presented with secundum atrial septal defect or ASD2. Whole exome sequencing revealed a novel variant and the MYH6 gene in both families, with the same variant present in all effected individuals but not in unaffected family members or unrelated controls. Because other variants in MYH6 have been reported to effect myofibril formation. The team studied the effect of the novel variant on the myofibrillar organization through transient transfection of CTC 12 cells. The MYH6 E526K variant was associated with a reduced striated I pattern and increased non-striated patterning. There was no effect on ATPase activity.                                                 Protein modeling suggested a variant of the effective position would reduce hydrogen bonding between alpha helices in the actin interface two region, increasing the volume of the cavity between the alpha helices and promoting the exposure of the alkaline side chain in the actin binding region. This could impair the interaction between the myosin motor head and actin. What these data suggests are that this novel MYH6 heterozygous variant may underlie ASD2 in two unrelated Chinese Han families by impairing myofibrillar organization.                                                 That's all for November 2019. Thank you for listening and I look forward to being back in December for the final episode of 2019.                                                 This podcast was brought to you by Circulation: Genomic and Precision Medicine and the American Heart Association council on genomic and precision medicine. This program is copyright American Heart Association 2019.

Jane Ferguson:                Hello. Welcome to episode 33 of Getting Personal: Omics Of The Heart, your podcast from Circulation: Genomic and Precision Medicine. I'm Jane Ferguson. This episode is from October 2019. Let's get started.                                            First up is a paper from Sébastien Thériault, Yohan Bossé, Jean-Jacques Schott and colleagues from Laval University, Quebec and INSERM in Mont. They published on genetic association analyses, highlight IL6, ALPL and NAV1 as three new susceptibility genes underlying Calcific Aortic Valve Stenosis.                                            In this paper, they were interested in finding out whether they could identify novel susceptibility genes for Calcific Aortic Valve Stenosis, or CAVS, which is a severe and often fatal condition with limited treatment options other than surgical aortic valve replacement. They conducted a GWAS meta-analysis across four European ancestry cohorts comprising over 5,000 cases and over 354,000 controls. They identified four loci at genome-wide significance, including two known loci in LPA and PALMD as well as two novel loci, IL6 which encodes the interleukin six cytokine, and ALPL, which encodes an alkaline phosphatase. They then integrated transcriptomic data from 233 human aortic valves to conduct the transcriptome wide association study and find an additional risk locus associated with higher expression of NAV1 encoding neuron navigator one. Through fine mapping, integrating conservation scores, and methylation peaks, they narrowed down the putative causal variants at each locus identifying one snip in each of PALMD and IL6 as likely causal in addition to two candidates snips at ALPL and three plausible candidate snips in NAV1.                                            Phenome-Wide Association Analysis, or PheWAS of the top candidate functional snips found that the IL6 risk variant associated with higher eosinophil count, pulse pressure and systolic blood pressure. Overall, this study was able to identify novel loci associated with CAVS potentially implicating inflammation and hypertension in CAVS etiology. Additional functional studies are required to further explore these potential mechanisms.                                            Next up is a paper from Elisavet Fotiou, Bernard Keavney and colleagues from the University of Manchester. Their paper entitled Integration of Large-Scale Genomic Data Sources With Evolutionary History Reveals Novel Genetic Loci for Congenital Heart Disease explored the genetic etiology of sporadic non syndromic congenital heart disease using an evolution informed approach. Ohnologs are related genes that have been retained following ancestral whole genome duplication events which occurred around 500 million years ago. The authors hypothesized that ohnologs which were retained versus duplicated genes that were lost were likely to have been under greater evolutionary pressure due to the need to maintain consistent gene dosage. For example, as could occur when the resulting proteins form complexes that require stochiometric balance.                                            Thus, ohnologs may be enriched for genes that are sensitive to dosage. The group analyzed copy number variant data from over 4,600 non syndromic coronary heart disease patients as well as whole exome sequence data from 829 cases of Tetralogy of Fallot. Compared to control data obtained from public databases, there was evidence for significant enrichment in CHD associated variants in ohnologs but not in other duplicated genes arising from small scale duplications. Through this and various other filtering steps to prioritize likely variants, the group was able to identify 54 novel candidate genes for congenital CHD highlighting the utility of considering the evolutionary origin of genes in the search for disease relevant biology.                                            Next, we have a clinical letter entitled Pathological Overlap of Arrhythmogenic Right Ventricular Cardiomyopathy and Cardiac Sarcoidosis from Ashwini Kerkar, Victoria Parikh and colleagues at Stanford University. They describe a case of a 50 year old woman previously healthy and a long distance runner who presented with tachycardia. She was found to have normal left ventricular size but severe right ventricular enlargement and systolic dysfunction. Genetic testing using an Arrhythmogenic Right Ventricular Cardiomyopathy or ARVC panel identified a variant in DSG2. through cascade testing it was found that two of the patient's three children also carried this variant. The patient experienced worsening RV failure and subsequently underwent heart transplantation at age 55. Pathology of the heart showed evidence of cardiac sarcoidosis. There have been some previous reports of overlap in ARVC and cardiac sarcoid pathology but not in cases with a high confidence genetic diagnosis such as this one.                                            This case raises the possibility of shared disease mechanisms underlying ARVC and cardiac sarcoidosis and suggests that therapies aimed at immune modulation may also have utility in ARVC. However, further work is required to test this hypothesis. Our next paper is a perspective piece from Babken Asatryan and Helga Servatius from Bern University Hospital. In Revisiting the Approach to Diagnosis of Arrhythmogenic Cardiomyopathy: Stick to the Arrhythmia Criterion!, they outline the challenges in defining diagnostic criteria for a Arrhythmogenic Right Ventricular Cardiomyopathy or ARVC, given the variable presentation of the disease. Given recent advances in knowledge, particularly in recognizing disease overlap with Arrhythmogenic Left Ventricular Cardiomyopathy or ALVC and Biventricular Arrhythmogenic Cardiomyopathy, a new clinical perspective was warranted. The Heart Rhythm Society updated their recommendations this year to introduce a new umbrella term that better encompasses the spectrum of disease, Arrhythmogenic Cardiomyopathy or ACM. This recommends the arrhythmia criterion Should be used as a first line screening criteria for ACM.                                            This is a broad criteria and a definitive diagnosis of ACM requires exclusion of systemic disorders such as sarcoidosis, amyloidosis, mild carditis, Chagas disease, and other cardiomyopathies. Implementation of this new approach to diagnosis may require more extensive investigation of arrhythmias including the use of ambulatory ECG monitors or cardiac loop recorders. These changes may also affect who's referred for genetic testing, potentially shifting diagnoses towards genotype rather than phenotype based disease classifications. Despite challenges and adopting new approaches, it is hoped that these changes will ultimately serve to improve risk stratification and allow for improved disease management and intervention to prevent sudden cardiac death.                                            We end with a scientific statement chaired by Sharon Cresci and co-chaired by Naveen Pereira with a writing group representing the AHA Councils on Genomic and Precision Medicine, Cardiovascular and Stroke Nursing and Quality of Care and Outcomes Research entitled Heart Failure in the Era of Precision Medicine: A Scientific Statement From the American Heart Association. This paper provides a comprehensive overview of the current state of omics technologies as they relate to the development and progression of heart failure and considers the current and potential future applications of these high throughput data for precision medicine with respect to prevention, diagnosis and therapy of heart failure. They discuss advances in genomics, pharmacogenomics, epigenomics, proteomics, metabolomics, and the microbiome, and integrate the findings from this rapidly developing field as they pertain to new methods to diagnose, treat, and prevent heart failure.                                            And that's it for October. I hope to see many of you at AHA Scientific Sessions in Philadelphia in November and look forward to bringing you more of the best new science next month. Thanks for listening. This podcast was brought to you by Circulation: Genomic and Precision Medicine and the American Heart Association Council on Genomic and Precision Medicine. This program is copyright American Heart Association 2019.  

Jane Ferguson:                Hi, everyone. Welcome to Getting Personal: Omics of the Heart, the monthly podcast from Circulation: Genomic and Precision Medicine. I'm Jane Ferguson, an assistant professor of medicine at Vanderbilt University Medical Center and an associate editor at CircGen. This is episode 32 from September 2019. Starting off this month, we have a paper on Genetic Mosaicism in Calmodulinopathy brought to us by Lisa Wren, Alfred George and colleagues from Northwestern University. They were interested in exploring the disease phenotypes that result from variation in the calmodulin genes, CALM1, 2 and 3.                                            Mutations in calmodulin are known to associate with congenital arrhythmia, but the group hypothesized that there may be a broader range of phenotypes associated with calmodulin mutations. They report on four unrelated families all with pro bands exhibiting symptoms of prolonged QTC interval and documented ventricular arrhythmia. They conducted targeted exome sequencing in these individuals and in their families and identified mutations in calmodulin genes, including two novel mutations. In one family with multiple occurrences of intrauterine fetal demise, there was evidence for sematic mosaicism in both parents.                                            The team studied the two novel mutations and found that the variants led to alterations in a calcium binding site resulting in impaired calcium binding. In human induced pluripotent stem cell derived cardiomyocytes, the team showed that the mutations impaired calcium dependent inactivation of L-type calcium channels and prolonged action potential duration. Their study not only demonstrates that mutations in calmodulins can cause dysregulation of L-type calcium channels, but that parental mosaicism maybe a factor in families with unexplained fetal arrhythmia or fetal demise.                                            Our next paper come from Wan G Pang, Christiana Kartsonaki, Michael Holmes and Zing Min Chen from the University of Oxford and Peking University Health Science Center and is entitled Physical Activity, Sedentary Leisure Time, Circulating Metabolic Markers, and Risk of Major Vascular Diseases. In this study, the authors were interested in finding out whether circulating metabolites are associated with the relationship between physical inactivity or sedentary behavior and increased risk of cardiovascular disease. They identified over 3000 cases of incident CVD from the China Kadoorie Biobank and included over 1400 controls without CVD. They measured 225 different metabolites and baseline plasma samples using NMR.                                            They used measures of self-reported physical activity and sedentary leisure time to associate physical activity with circulating metabolites, and then they ran analysis to relate the metabolites to CVD. Physical activity and sedentary leisure time were associated with over 100 metabolic markers. In general, the patterns of associations were similar using either activity measure. Physical activity was inversely related to very low and low density HDL particles, but positively related to large and very large HDL particle concentrations. Physical activity was also inversely associated with alanine, glucose, lactate, acetoacetate, and glycoprotein acetyls.                                            When they examined the associations of these same metabolites with CVD, the directions were generally consistent with expectation, going on the premise that physical activity is protective, and that sedentary behavior is a risk factor for CVD. Their analyses suggests that metabolite markers could explain about 70% of the protective associations of physical activity and around 50% of the risk associations of sedentary leisure time with cardiovascular disease. Next up, we have a paper on Biallelic Variants in ASNA1, Encoding a Cytosolic Targeting Factor of Tail-Anchored Proteins, Cause Rapidly Progressive Pediatric Cardiomyopathy, coming from Judith Verhagen, Ingrid van de Laar and colleagues from University Medical Center Rotterdam.                                            Their focus was on pediatric cardiomyopathies, which are both clinically and genetically heterogeneous. They had identified a family where two siblings had died during early infancy of rapidly progressive dilated cardiomyopathy. Through exome sequencing, they identified variants in the ASNA-1 gene and established that the children were compound heterozygotes for the variants. This highly conserved gene encodes an ATPase, which is required for post-translational membrane insertion of tail-anchored proteins. The team looked at expression of this protein in patient samples and then followed this up with functional analyses using cells and zebrafish. They found that one of the variants was predicted to result in a premature stop codon.                                            In support of this, they observed decreased protein expression in myocardial tissue and skin fibroblasts. The other variant caused a missense mutation, and the team found that this resulted in protein misfolding, as well as less effective tail-anchored protein insertion. In zebrafish, knock out of the ASNA1 gene resulted in reduced cardiac contractility and early lethality, which could not be rescued by either version of the variant mRNA. This translational study highlights the importance of the ASNA1 gene as a cardiomyopathy susceptibility gene and further reveals the importance of tail-anchored membrane protein insertion pathways in cardiac function.                                            The next paper from Karni Moshal, Gideon Koren and colleagues from Brown University is entitled LITAF Regulates Cardiac L-Type Calcium Channels by Modulating NEDD 4-1 Ubiquitin Ligase. In this paper, the authors report on the role of ubiquitination as a crucial component in cardiac ion channel turnover and action potential duration. Previous genome wide association studies of QT interval had identified snips in or near genes regulating protein ubiquitination, particularly the LITAF or lipopolysaccharide-induced tumor necrosis factor gene. Using zebrafish, the team performed optical mapping in hearts to identify calcium and found that knocked down of LITAF resulted in an increase in calcium transients.                                            They studied intracellular calcium handling and rapid derived cardiomyocytes and found that over expression of LITAF caused a decrease in L-type calcium channel current and abundance of the L-type calcium channel alpha1c sub unit or Cava1c, whereas LITAF knocked down increased calcium channel current and Cava1c protein. LITAF downregulated total and surface pools of Cava1c via increased Cava1c ubiquitination and lysosomal degradation in tsA201 kidney cells. There was evidence of colocalization between LITAF and L-type calcium channel, or LTCC, in the tsA201 kidney cells and in cardiomyocytes. In the tsA201 cells, NEDD4-1 protein increased Cava1c ubiquitination, but a catalytically inactive form of NEDD4-1 had no effect.                                            Cava1c ubiquitination was further increased by co-expressed LITAF NEDD4-1, but not the inactive version of NeNEDD4-1. NEDD4-1 knockdown abolished the negative effect of LITAF on L-type calcium channel current and Cava1c levels in three week old rapid cardiomyocytes. Taken together, these data show that LITAF acts as an adapter protein promoting NEDD4-1 mediated ubiquitination and subsequent degradation of LTCC, highlighting LITAF as a novel regulator of cardiac excitation. Rounding out this issue is a review on the Gut Microbiome and Response to Cardiovascular Drugs from Sony Tuteja and Jane Ferguson from the University of Pennsylvania and Vanderbilt University Medical Center.                                            Since that last author is me, I'm sure I have a biased view of the importance of the topic, but the increasing awareness of the microbiome in every aspect of health has also led to increased awareness of the role of commensal microbiota in drug metabolism, including in the metabolism of drugs used to treat cardiovascular diseases. In this article, we aim to review what is currently known about how the gut microbiome interacts with cardiovascular drugs and to summarize some of the mechanisms whereby gut microbiota might affect drug metabolism. Early evidence suggests that the gut microbiome modulates response to statins and antihypertensive medications, but there may be many other drugs that are susceptible to interaction with microbiota.                                            Drug metabolism by the gut microbiome can result in altered drug pharmacokinetics and pharmacodynamics or in the formation of toxic metabolites which can interfere with drug response. While we are still in a relatively early stage in this field, we suggest that a better understanding of the complex interactions of the gut microbiome, host factors and response to medications will be important for the development of novel precision therapeutics in cardiovascular disease prevention and treatment. That's all for the September issue of Circulation: Genomic and Precision Medicine. Come back next month for the next installment. Thanks for listening.                                            This podcast was brought to you by Circulation: Genomic and Precision Medicine and the American Heart Association Council on Genomic and Precision Medicine. This program is copyright American Heart Association 2019.  

Jane Ferguson:                Hello, and welcome to Getting Personal, Omics of the Heart, your monthly podcast from Circulation: Genomic and Precision Medicine. I'm Jane Ferguson. It is August, 2019, and this is episode 31. Let's get started.                                            Our first paper comes from Freyja van Lint and Cynthia James, from University Medical Center Utrecht, and is entitled Arrhythmogenic Right Ventricular Cardiomyopathy-Associated Desmosomal Variants Are Rarely De Novo, Segregation and Haplotype Analysis of a Multinational Cohort. In this study, the team was interested in exploring variants that are associated with arrhythmogenic right ventricular cardiomyopathy or ARVC. ARVC is often attributable to pathogenic variants in genes encoding cardiac desmosomal proteins, but the origin of these variants had not been comprehensively studied.                                            The investigators identified ARVC probands meeting 2010 task force criteria from three ARVC registries in the United States and Europe and who had undergone sequencing of desmosomal genes. All 501 probands, 322 of them, or over 64%, carried a pathogenic or likely pathogenic variant in the desmosomal genes PKP2, DSP DSG2, DSC2, and JUP. The majority of these, over 75%, we're not unique with these variants occurring in more than one proband.                                            The team performed cascade screening and were able to identify the parental origin of almost all of the variants. However, they identified three de novo variants, including two whole gene deletions. They conducted haplotype analysis for 24 PKP2 variants across 183 seemingly unrelated families and concluded that all of these variants originated from common founders.                                            This analysis sheds light on the origin of variants in desmosomal genes and suggests that the vast majority of these ARVC variants originate from ancient founders with only a very small proportion of de novo variants. These data can inform clinical care particularly concerning genetic counseling and cascade screening of relatives.                                            The next paper continues a theme of cardiomyopathy and comes from Derk Frank, Ashraf Yusuf Rangrez, Corinna Friedrich, Sven Dittmann, Norbert Frey, Eric Schulze-Bahr and colleagues from University Medical Center Schleswig-Holstein. In this paper, Cardiac α-Actin Gene Mutation Causes Atrial-Septal Defects Associated with Late-Onset Dilated Cardiomyopathy, the team was interested in understanding the genetics of familial atrial-septal defect. They studied large multi-generational family with 78 family members and mapped a causal variant on chromosome 15q14, which caused nonsynonymous change in exon 5 of the ACTC1 gene.                                            In silico tools predicted this variant to be deleterious. Analysis of myocardial tissue from an affected individual revealed sarcomeric disarray, myofibrillar degeneration, and increased apoptosis. Proteomic analysis highlighted extracellular matrix proteins as being affected. The team over-expressed the mutation in rats and found structural defects and increased apoptosis in neonatal rat ventricular cardiomyocytes and confirmed defects in actin polymerization and turnover which affected contractility. These data implicate the variant in ACTC1 as causing atrial-septal defects and late-onset cardiomyopathy in this family and revealed the underlying molecular mechanisms affecting development and contractility.                                            The next paper is entitled Characterization of the CACNA1C-R518C Missense Mutation in the Pathobiology of Long-QT Syndrome Using Human Induced Pluripotent Stem Cell Cardiomyocytes Shows Action Potential Prolongation and L-Type Calcium Channel Perturbation, and it comes from Steven Estes, Michael Ackerman and colleagues at the Mayo Clinic. They set out to use patient-derived human induced pluripotent stem cells to understand the pathogenicity of a variant in the CACNA1C gene in Long-QT Syndrome.                                            They obtained cells from dermal punch biopsy from an individual with long-QT and a family history of sudden cardiac death who carried an R518C missense mutation in CACNA1C. Starting with fibroblasts, they reprogrammed the cells into iPSCs and then differentiated these into cardiomyocytes. They corrected the mutation back to wild type using CRISPR/Cas9 and then compared the cardiomyocytes carrying the original patient mutation with isogenic corrected cardiomyocyte controls. They found significant differences in action, potential duration, and in calcium handling.                                            Patch clamp analysis revealed increased L-type calcium channel window current in the original mutation-carrying cells in addition to slow decay time and increased late calcium current compared with the isogenic corrected control human iPSC cardiomyocytes. These data strongly suggest that CACNA1C is a long-QT susceptibility gene and demonstrate the potential in using patient-derived iPSCs and CRISPR/Cas9 to understand underlying mechanisms linking variants to disease.                                            The final paper this month is Blood Pressure-Associated Genetic Variants in the Natriuretic Peptide Receptor-1 Gene Modulate Guanylate Cyclase Activity and comes from Sara Vandenwijngaert, Chris Newton-Cheh and colleagues on behalf of the CHARGE+ Exome Chip Blood Pressure Consortium, the CHD Exome+ Consortium, the Exome BP Consortium, the GoT2D Consortium, the T2D-GENES Consortium, and the UK Biobank CardioMetabolic Consortium Blood Pressure Working Group.                                            This team wanted to understand how variants in the NPR-1 gene affect the function of the atrial natriuretic peptide receptor-1. They performed a meta-analysis across over 491,000 unrelated individuals, including both low frequency and rare variants in NPR-1 to identify their association with blood pressure. They identified three nonsynonymous variants associated with altered blood pressure at genome-wide significance and examined the function of these variants in vitro.                                            Using cells expressing either wild type NPR-1 or one of the three identified variants, they explored the impact of the variants on the ability of cells to catalyzes the conversion of guanosine triphosphate to cyclic 3′,5′-guanosine monophosphate in response to binding of atrial or brain natriuretic peptide. Increased levels of cyclic GMP are known to decrease blood pressure by inducing by natriuresis, diuresis, and vasodilation.                                            Two variants which associated with high blood pressure in the population meta-analysis were associated with decreased cyclic GMP in response to ANP or BNP in vitro, while one variant which associated with lower blood pressure in humans was associated with higher cyclic GMP production in vitro. These data show that variants affecting loss or gain of function in guanylate cyclase activity could have downstream effects on blood pressure at the population level.                                            That's it for this month. Thank you for listening. We will be back with more next month. This podcast was brought to you by Circulation: Genomic and Precision Medicine and the American Heart Association Council on Genomic and Precision Medicine. This program is copyright American Heart Association 2019.  

Jane Ferguson:                Hi everyone. Welcome to Getting Personal: Omics of the Heart, the podcast from Circulation: Genomic and Precision Medicine. I'm Jane Ferguson and this is episode 30 from July 2019.                                            First up we have a paper, the Subtype Specificity of Genetic Loci Associated With Stroke in 16664 cases and 32792 Controls, from Matthew Trailer and colleagues on behalf of the NINDS Stroke Genetics Network and the International Stroke Genetics Consortium.                                            They were interested in understanding whether genetic loci previously found to be associated with stroke have distinct associations with stroke subtypes, specifically ischemic and hemorrhagic stroke. They compiled data sets through an international consortium to analyze 16664 stroke cases and 32792 controls, all of European ancestry. The cases were subtyped using two different stroke classification systems: the Trial of ORG 10172 in Acute Stroke Treatment, or TOAST system, and the Causative Classification of Stroke, or CCS system.                                            They selected genetic loci for consideration based on previous association with stroke in general or stroke subtypes in the MEGASTROKE consortium, which had included a large number of the subjects included in the present study. They used a Bayesian multinomial logistic regression approach to evaluate the association of snips at each locus with stroke subtypes identified under the TOAST and CCS classifications, giving five different case groups compared with a set of controls.                                            16 loci were taken forward for further analysis. There were seven loci which associated with both ischemic and hemorrhagic strokes subtypes, four which clearly associated with either ischemic or hemorrhagic stroke, with the rest showing less consistent effects. One locus, EDNRA, showed opposite affects for ischemic and hemorrhagic stroke. Overall, the findings indicate a large degree of genetic heterogeneity, but some overlap, suggesting common underlying pathophysiological pathways in different stroke subtypes, potentially related to small vessel disease. More detailed phenotyping and further analysis in large samples is required to fully understand genetic mechanisms underlying the risk of different stroke subtypes.                                            And, just to add, this paper was previously submitted to the pre-print server Bio Archive. We support open science and are always happy to consider papers that have been submitted to pre-print servers. So, if you have a particularly cool paper on Bio Archive that fits our scope, do feel free to send it our way.                                            Next up, we have a paper from Fabiola del Greco, Cristian Pattaro, Peter Pramstaller, Alessandera Rossini, and colleagues, from Eurac Research Institute for Biomedicine. This paper, entitled Lipidomics, Atrial Conduction, and Body Mass Index, Evidence from Association, Mediation, and Mendelian Randomization Models, aims to investigate the mechanisms underlying associations between circulating lipids and atrial conduction. They used mass spectrometry measurement of 151 sphingo- and phospholipids in plasma or serum from individuals who had undergone electrocardiogram measurements to ascertain P-wave duration.                                            They first looked for associations in 839 individuals from the micro islets in South Tyrol, or MICROS study, based in Italy, and replicated in 951 participants of the Orkney Complex Disease Study, ORCADES, based in Scotland. They identified and replicated an association between levels of phosphatidylcholine 38-3 and P-wave duration, which was independent of cholesterol, triglycerides, and glucose levels.                                            However, the association was mediated by BMI, and suggested that increased BMI may cause both increased levels of PC38-3 and longer P-wave duration, suggesting a role for body mass in altered lipids in atrial electrical activity.                                            The next paper is a research letter from Hana Bangash, Iftikhar Kullo, and colleagues from the Mayo Clinic on Use of Twitter to Promote Awareness of Familial Hypercholesterolemia.                                            Scientists and health professionals are increasingly using Twitter to communicate. This team wondered whether organized awareness campaigns, including Twitter events like Tweetathons, really make a different. They analyzed Twitter activity related to familial hypercholesterolemia in September 2018, during national cholesterol education month, which included an international familial hypercholesterolemia awareness day and Tweetathon. They also analyzed tweets from August and October 2018, where there was no formal awareness campaign and compared the FH Twitter activity with that of colorectal cancer, which did not have any formal awareness campaigns at that time.                                            In September, FH-related tweets increased by 152.9% compared to August, and then declined by over 58% in October. The topic reach for familial hypercholesterolemia was 11.1 million in August, and increased over 250% in September to 37.7 million. The reach declined by over 71% in October to just over 10 million. In comparison, the reach for colorectal cancer declined from 453 million in August to 300 million in September and then increased to 677 million in October, which happened to be breast cancer awareness month.                                            These data suggest that awareness campaigns like national cholesterol education month do lead to an increase in Twitter activity. However, this increase isn't necessarily sustained during the following month, and it remains unclear whether Twitter activity actually translates into a wider awareness amongst providers or patients, which could translate into clinical benefits. Nonetheless, as the use of Twitter increases, this may be a promising avenue to promote awareness and to disseminate knowledge.                                            And, of course, I have to take this opportunity to mention that Circulation: Genomic and Precision Medicine is on Twitter and you can follow us @Circ_Gen to keep up with what's going on at the journal.                                            Next up, we have a letter entitled B-iallelic Mutations in NUP205 and NUP210 Are Associated with Abnormal Cardiac Left-Right Patterning from WeiCheng Chen, Yuan Zhang, Sunhu Yang, Xiangyu Zhou, and colleagues from Tongji University.                                            They set out to understand the genetic underpinnings of cardiac left-right patterning and to probe why individuals with situs inversus totalis, or SIT, where the chest organs are in a complete mirror image to typical, have almost no symptoms or complications, while individuals with heterotaxy, who have abnormal organ arrangement that is not a mirror image, typically have severe phenotypes including congenital heart disease.                                            They performed whole exome and whole genome sequencing in 61 family trios with SIT or heterotaxy and identified ballielic missense mutations in nucleoporins NUP205 and NUP210. Nucleoporins comprise the main components of the nuclear pore complex in eukaryotic cells. The team generated induced pluripotent sense cells from peripheral blood cells of an affected patient and a healthy control, and found that there were impairments in protein interactions in the variant cells, particularly interactions with another crucial nucleoporin, NUP93.                                            In zebra fish, NUP205 knockdown resulted in left-right assymetry and defects in heart looping formation in a subset of fish embryos. Knockdown of both NUP205 and NUP93 resulted in impairments in cilia and human retinal pigment epithelial cells. Gene expression analysis revealed affects in known cilia genes NEC2 and NEC3.                                            Overall, this study provides evidence that mutations in nucleoporins NUP205 and NUP210 may cause defects in cardiac left/right patterning, potentially through effects on ciliary function.                                            This issue closes with a letter and response conversation around a recent article on missense mutations in the FLNC gene, causing familial restrictive cardiomyopathy. Hisham Ahamed and Muthiah Subramanian from Amrita Institute of Medical Scientists write to share a case of a woman presenting with features of heart failure and muscular weakness consistent with distal myopathy who was found to carry a deletion in exome 37 of the FLNC gene. This case adds to the previous evidence published by Alvaro Roldan Sofia and Julian Palomino-Doza in March 2019 in our journal, Highlighting Mutations in the FLNC Gene in Cardiomyopathy.                                            That's all for this month. Come back in August for your roundup of the next issue. Thanks for listening!                                            This podcast was brought to you by Circulation: Genomic and Precision Medicine, and the American Heart Association Council on Genomic and Precision Medicine. This program is copyright American Heart Association, 2019.  

Jane Ferguson:                Hi, everyone. Welcome to episode 29 of Getting Personal: Omics of the Heart, the podcast from Circulation: Genomic and Precision Medicine. I'm Jane Ferguson from Vanderbilt University Medical Center and an associate editor at Circ: Genomic and Precision Medicine. Let's dive in and see what's new in the June issue.                                            First up, Validation of Genome-Wide Polygenic Risk Scores for Coronary Artery Disease in French Canadians from Florian Wünnemann, Guillaume Lettre and colleagues from the University of Montreal. Polygenic scores have the potential to be used to predict disease risk, but have not been broadly validated in different populations. This team was interested in whether polygenic risk scores that have been found to predict coronary artery disease in European ancestry subjects in the UK Biobank would also predict disease in French Canadians. They calculated two different polygenic risk scores in over 3600 cases and over 7000 controls and tested their ability to predict prevalent, incident and recurrent CAD.                                            Both scores predicted prevalent CAD, but did not perform as well in predicting incident or recurrent disease. This maybe because the majority of subjects were on statant treatment. Overall, the study confirms that polygenic risk scores for CAD developed in European ancestry can be used in other populations of European ancestry. However, further work is needed to develop and validate polygenic risk scores in other ancestries and to explore whether well performing risk scores can be developed to predict incident or recurrent disease.                                            Our next paper comes from Farnaz Shoja-Taheri, Michael Davis and colleagues from Emory University and is entitled Using Statistical Modeling to Understand and Predict Pediatric Stem Cell Function. Stem cell therapy is emerging as a potential therapeutic option for treating pediatric heart failure, which otherwise can only be cured through heart transplantation. The success of stem cell therapy depends on many variables, including the reparative ability of the infused cells. In this paper, the author set out to test whether they could predict the behavior of c-kit+ progenitor cells or human CPCs using RNA seq and computational modeling.                                            They obtained CPCs from 32 patients, including eight neonates whose cells are thought to have the highest reparative capacity, and they performed RNA sequencing. The team had previously developed regression models that could link gene expression data from sequencing to phenotypes in the cells, and they tested these models in the CPC cell lines. They tested seven neonate cell lines in vitro and found that cellular proliferation and the chemotactic potential of condition media matched what was predicted by the RNA seq-based model.                                            They used pathway analysis to identify potential mechanisms regulating CPC performance and identified several genes related to immune response, including interleukins and chemokines. They further confirmed the presence of cytokines at the protein level that were associated with well performing cells showing that at least one of the outcomes could be functionally predicted using an ELISA ASA. This type of approach may prove useful to inform ongoing clinical trials to stem cell therapy in congenital heart disease.                                            The next paper, Systems Pharmacology Identifies an Arterial Wall Regulatory Gene Network Mediating Coronary Artery Disease Side Effects of Antiretroviral Therapy comes to us from Itziar Frades, Johan Björkegren, Inga Peter and colleagues from the Icahn School of Medicine at Mount Sinai. They were interested in understanding mechanisms whereby antiretroviral therapy for HIV leads to increased risk for coronary artery disease. They analyzed the transcriptional responses to 15 different antiretroviral therapy or ART drugs in human cell lines and cataloged the common transcriptional signatures.                                            They then cross-referenced these against gene networks associated with CAD and CAD related phenotypes. They found that 10 of 15 ART response networks were enriched for differential expression and connectivity in an atherosclerotic arterial wall of regulatory gene network identified as causal for CAD. They used cholesteryl ester loaded foam cells in an in vitro model to validate their findings and found that ART treatment increased cholesteryl ester accumulation in foam cells which was prevented when the key network regulator gene, PQBP1, was silenced.                                            Their study highlights a gene network which is altered in response to ART and which promotes foam cells formation, highlighting a mechanistic link between HIV treatment and CAD. Targeting this network potentially through PQBP1 maybe a way to reduce the risk of CAD in individuals treated with antiretroviral drugs. The next paper comes from Brooke Wolford, Whitney Hornsby, Cristen Willer, Bo Yang and colleagues from the University of Michigan and is entitled Clinical Implications of Identifying Pathogenic Variants in Individuals With Thoracic Aortic Dissection. They were interested in whether exome sequencing in individuals with thoracic aortic dissection could identify disease associated variance.                                            They conducted exome sequencing in 240 patients and 258 controls and screened 11 genes for potentially pathogenic variance. They identified 24 variance in six genes across 26 cases with no potentially pathogenic variance identified in the controls. They found that carriers of pathogenic variance had significantly earlier age of onset of dissection, higher rates of root aneurysm and greater incidents of aortic disease in family members, while patients without identified variance had more hypertension and a higher rate of smoking.                                            Their study suggests that genetic testing should be considered in patients with thoracic artery dissection particularly in individuals with early age of onset before age 50 and no hypertension with the possibility of cascade screening to follow to identify at risk family members before onset of dissection and possible death. Our next paper is a research letter from Seyedeh Zekavat, Pradeep Natarajan and colleagues from Harvard Medical School, Investigating the Genetic Link Between Arterial Stiffness and Atrial Fibrillation. They aimed to investigate whether arterial stiffness is causal for atrial fibrillation using Mendelian randomization to probe genetic causality.                                            They calculated the genetic component of the arterial stiffness index or ASI, a noninvasive measure of arterial stiffness, in over 131,000 individuals in the UK Biobank. They then assessed whether the genetic predictors of ASI defined as the top six independent variance were also associated with atrial fibrillation in over 225,000 participants in the UK Biobank and in over 588,000 individuals from a multi-ethnic GWAS. They found that the ASI genetic risk score was significantly associated with incident atrial fibrillation in both the UK Biobank and the multi-ethnic AF GWAS.                                            The association held true even after adjustment for age, sex, smoking status, prevalent heart failure, prevalent hypertension, prevalent CAD, prevalent hypercholesterolemia, prevalent diabetes, heart rate, alcohol intake and exercise frequency in the UK Biobank participants. Because some people have hypothesized that atrial fibrillation may actually precede and cause arterial stiffness, the team did the reverse Mendelian randomization experiment and tested whether genetic predictors of AF were associated with the arterial stiffness index. They found no association suggesting that AF does not cause arterial stiffness.                                            In summary, this paper provides genetic evidence supporting arterial stiffness as a causal contributor to atrial fibrillation and suggests that future randomized controlled studies would be warrantied to assess whether methods to reduce arterial stiffness could be protective against atrial fibrillation. The next research letter comes from Scott Damrauer, Kara Hardie, Reed Pyeritz and colleagues from the University of Pennsylvania and is entitled FBN1 Coding Variants and Nonsyndromic Aortic Disease. In this study, the authors were interested in characterizing the frequency of variance associated with Marfan syndrome in the general population.                                            They analyzed data from the Penn Medicine BioBank looking at 12 variance in the FBN1 gene all of which have been reported to associate with Marfan syndrome. Of almost 11,000 individuals who underwent exome sequencing, they identified 70 individuals who were carriers of one of the 12 preselected FBN1 variance. These individuals ranged in age from age 28 to 87 years and 56% of them were male. They combed through clinical data from the participant's electronic health records, including office notes, diagnostic tests and imaging studies.                                            Two individuals had a clinical diagnosis of Marfan syndrome while 21 individuals had evidence of cardiovascular phenotypes related to Marfan syndrome including mitral valve disease, dilated sinus of valsalva, dilated ascending aorta, descending thoracic or abdominal aneurysms or dissections or had undergone surgical procedures involving the mitral valve or thoracic aorta. Compared to age and sex matched controls without known or suspected pathogenic FBN1 variance, the FBN1 variant carriers were significantly more likely to have Marfan syndrome related cardiovascular disease.                                            Although the majority of individuals carrying FBN1 variance did not have documented cardiovascular disease in this study, the data were somewhat limited, meaning that some affected individuals could have been missed. Thus, while the penetrance of these variance appears to be variable, the severe consequences of these FBN1 variance observed in some individuals suggests that clinical screening for carries of these variance is important. To round up this month's issue, we have a scientific statement led by Ferhaan Ahmad and Elizabeth McNally on Establishment of Specialized Clinical Cardiovascular Genetics Programs: Recognizing the Need and Meeting Standards.                                            This statement comes from the American Heart Association Council on Genomic and Precision Medicine, the Council on Arteriosclerosis, Thrombosis and Vascular Biology, the Council on Basic Cardiovascular Sciences, the Council on Cardiovascular and Stroke Nursing, the Council on Clinical Cardiology and the Stroke Council. In this statement, the writing group lays out the importance of establishing specialized centers of care for individuals affected by inherited cardiovascular diseases. As cardiovascular genetics as a field continues to grow and as genomic medicine becomes part of practice, it is essential for programs to evolve to include this new knowledge and specialization.                                            There are significant challenges in interpreting genetic test results and in evaluating counseling and managing the care of genetically at risk family members who have inherited pathogenic variance, but not yet shown signs of disease. Establishing specialized programs to combine cardiovascular medicine and genetics expertise is an effective way to allow for the integration of multiple types of clinical and genetic data and to improve diagnosis, prognostication and cascade family testing in affected individuals and their families.                                            Training individuals in genetic cardiology will allow for improved care and management of risk in affected or at risk individuals and potentially pave the way for genotype specific therapy. This important and timely scientific statement outlines current best practices for delivering cardiovascular genetic evaluation and care in both the pediatric and the adult settings with a focus on team member expertise and conditions that most benefit from genetic evaluation.                                            That's all for this month. Thank you as always for listening and come back next month for the next installment of papers in Genomic and Precision Medicine. This podcast was brought to you by Circulation: Genomic and Precision Medicine and the American Heart Association Council on Genomic and Precision Medicine. This program is copyright American Heart Association 2019.  

Jane Ferguson:  Hi, everyone. Welcome to Getting Personal: Omics of the Heart, the podcast from Circulation: Genomic and Precision Medicine. It's May 2019, and this is episode 28. So let's see what papers we have in the journal this month.                              First up, a paper from Mengyao Yu, Nabila Bouatia-Naji and colleagues from the Inserm Cardiovascular Research Center in Paris, entitled GWAS-Driven Gene-set Analyses, Genetic and Functional Follow-Up Suggest Glis1 as a Susceptibility Gene for Mitral Valve Prolapse.                              In this paper, they set out to characterize the genetic contributions to mitral valve prolapse, or MVP, to better understand the biological mechanisms underlying disease. They applied the gene-set enrichment analysis for QWAS tool and the pathway enrichment tool DEPICT to existing GWAS for MVP in a French sample to identify gene sets associated with MVP. They find significant enrichment of genes involved in pathways of relevance to valve biology and enrichment for gene expression in tissues of relevance to cardiovascular disease.                              They zeroed in a Glis family zinc finger gene Glis1 with consistently strong pattern of evidence across the GWAS enrichment and transcription analyses. They replicated the association between Glis1 and MVP in a UK biobank sample. They found that Glis1 is expressed in valvular cells during embryonic development in mice, but is mostly absent at later times. They targeted two Glis1 orthologs in zebrafish and found that knockdown of Glis1 B was associated with a significant increase in the incidence of severe atrioventricular regurgitation. These data highlight Glis1 as a potential regulator of cardiac valve development with relevance for risk of mitral valve prolapse.                              Next up is a paper from Gina Peloso, Akihiro Namuro, Sek Kathiresan and colleagues from Boston University, Kanazawa University, and Mass General Hospital. In their paper, Rare Protein Truncating Variance in APOB, Lower LDL-C, and Protection Against Coronary Heart Disease, the team was interested in understanding whether protein truncating variance in APOB underlying familial hypobetalipoproteinemia confer any protection against coronary heart disease.                              They sequenced the APOB gene in 29 Japanese families with hypobetalipoproteinemia as well as in over 57,000 individuals, some with early onset CHD and some without CHD. They found that presence of an APOB truncating variant was associated with lower LDL cholesterol and lower triglycerides, and also with significantly lower risk for coronary heart disease. This study confirms that variance in APOB, leading to reduced LDL and triglycerides are also protective against coronary heart disease. :                            The next paper entitled Mortality Risk Associated with Truncating Founder Mutations in Titin comes to us from Mark Jansen, Dennis Dooijes, and colleagues from University Medical Center Utrecht. They analyzed the effect of titin truncating variance on mortality in Dutch families. Titin truncating variants are associated with dilated cardiomyopathy, but have a very variable penetrance.                              In this study, the authors looked at three titin truncating variants, established to be founder mutations, and traced the pedigrees back to 18th century ancestors. They looked at 61 individuals on the transmission line and 360 of their first-degree relatives. They find no evidence for excess mortality in variant carriers overall. However, when they restrict it to individuals over 60 years of age, they did find a significant difference in mortality, which was also observed in individuals born after 1965. What these data tell us is that these titin truncating variants have a relatively mild phenotype with effects on mortality only manifesting later in life in many carriers. Given increases in life expectancy over the past several decades, the prevalence of morbidity and mortality attributable to titin truncating variants may increase. Genetic screening may identify genotype-positive, phenotype-negative individuals who would benefit from preventative interventions.                              Continuing on the theme of genetic variance, we have a paper from John Giudicessi, Michael Ackerman, and colleagues from the Mayo Clinic, Assessment and Validation of a Phenotype-Enhanced Variant Classification Framework to Promote or Demote RYR2 Missense Variants of Uncertain Significance. In this paper, they aim to find a better way to classify variants of unknown significance, of VUS, in the RYR2 gene. Variants in this gene are commonly associated with catecholaminergic polymorphic ventricular tachycardia, or CPVT.                              They examined 72 distinct variants in 84 Mayo Clinic cases and find that 48% were classified as VUS under ACMG guidelines. The rate was similar in a second sample from the Netherlands, with 42% of variants originally classified as VUS. They developed a diagnostic scorecard to incorporate a pretest clinical probability of CPVT, which included various clinical criteria, including symptoms and stress test results. Application of the phenotype enhanced ACMG criteria brought the VUS rate down to 7% in Mayo Clinic and 9% in the Dutch samples. The majority of VUS were reclassified as likely pathogenic.                              This study highlights how incorporation of disease-specific phenotype information can help to improve variant classification and reduce the ambiguity of reporting variants of unknown significance.                              We also have a number of research letters in the journal this month. From Karine Ngoyen, Gilbert Habib, and coauthors from Marseilles, we have a paper entitled Whole Exome Sequencing Reveals a Large Genetic Heterogeneity and Revisits the Causes of Hypertrophic Cardiomyopathy, Experience of a Multicentric study of 200 French Patients. In this study, they examined the genetic contributions to hypertrophic cardiomyopathy, or HCM, in 200 individuals as part of the HYPERGEN study and compared the benefits of whole exome sequencing compared with targeted sequencing of candidates' sarcomeric genes. All subjects had HCM documented by echocardiography.                              In the whole exome sequencing data, they first looked for mutations within 167 genes known to be involved in cardiomyopathies or other hereditary diseases. Of these 167 virtual panel genes, they find variants in 101 genes. Following whole exome sequencing, over 87% of the patients had an identified pathogenic, or likely pathogenic, mutation compared with only 35% of patients who only had targeted sequencing of sarcomeric genes.                              This highlights the generic heterogeneity of HCM and suggests that whole exome sequencing has utility in identifying variants not covered by sarcomeric gene panels.                              The next letter is from Wouter Te Rijdt, Martin [Vandenberg] and colleagues from University Medical Center Groningen and states that [dissynchronopathy] can be a manifestation of heritable cardiomyopathy. They hypothesized that left bundle branch block, also designated as dissynchronopathy, may be a manifestation of familial cardiomyopathy.                              They analyzed patients from a database of cardiac resynchronization therapy and identified super-responders whose left ventricular dysfunction was normalized by therapy. They carried out targeted sequencing in 60 known cardiomyopathy genes in 16 of these super-responder individuals and identified several variants, including a pathogenic variant in troponin T in one individual and variants of unknown significance in nine individuals. Pedigree analysis identified multiple family members with dilated cardiomyopathy.                              This study highlights that dissynchronopathy can be a manifestation of DCM, but that affected individuals may still benefit from cardiac resynchronization therapy.                              The next letter entitled Targeted Long-Read RNA Sequencing Demonstrates Transcriptional Diversity Driven by Splice-Site Variation in MYBPC3 comes from Alexandra Dainis, Euan Ashley, and colleagues from Stanford University. They set out to understand whether transcriptome sequencing could improve the diagnostic yield over genome sequencing in patients with hypertrophic cardiomyopathy. In particular, they hypothesized that long-read sequencing would allow for identification of alternative splicing linked to disease variance. They used long-read RNA and DNA sequencing to target the MYBPC3 gene in an individual with severe HCM who carried a putative splice-site altering variant in the gene. They were able to obtain heart tissue for sequencing and included several HCM and control subjects in addition to the patient with the MYBPC3 variant.                              They identified several novel isoforms that were only present in the patient sample, as well as some additional isoforms, including retained introns, extended exons, and an additional cryptic exon, which would not have been predicted based on the DNA variant. While the effects on protein function is not known, the transcripts are predicted to be translated.                              This analysis highlights the effect of a rare variant on transcription of MYBPC3 and provides additional evidence to link the variant to disease. This is a really nice approach, which could be used to probe causality and mechanisms, not only for cardiovascular disease, but for other rare variants in many disease settings.                              We finish with a perspective piece from Nosheen Reza, Anjali Owens, and coauthors from the University of Pennsylvania entitled Good Intentions Gone Bad, The Dangers of Sponsored Personalized Genomics. They present a case of a 23-year-old woman who presented for genetic counseling and evaluation after discovering she carried a likely pathogenic MYH7 variant associated with cardiomyopathy. She had no significant medical history, but had participated in employer-sponsored genetic testing motivated to identify potential variants related to cancer given a family history of cancer.                              After receiving her results, she experienced considerable anxiety and stopped exercising out of fear of cardiac complications. She visited an ER after experiencing chest pain, something she had not experienced previously. There was no appropriate counseling available at her institution for her genetic test results, leading her to seek out the additional counseling. Thus, while she was initially motivated to complete genetic testing because her employer offered it free of change, she ended up incurring costs related to the followup evaluation and counseling. Ultimately, she had no significant clinical findings. Although the variant had been listed as likely pathogenic, other sources consider it to be of unknown significance.                              This story highlights the psychological and financial impact that genetic testing can have on individuals, particularly when carried out without any pretest counseling or accessible post-test support when variants are identified.                              Despite the considerable promise of personalized medicine, there are many complexities to be considered, particularly with direct-to-consumer testing and employer-sponsored testing. This perspective highlights the ethical considerations and urges caution to maintain the best interests of patients.                              That's all for this month. Thanks for listening. I look forward to bringing you more next month.                              This podcast was brought to you by Circulation Genomic and Precision Medicine and the American Heart Association Council on Genomic and Precision Medicine. This program is copyright American Heart Association 2019.  

Jane Ferguson:                Hello and welcome to Getting Personal: Omics of the Heart, your podcast from Circulation: Genomic and Precision Medicine. I'm Jane Ferguson from Vanderbilt University Medical Center, and this is episode 27 from April 2019.                                            This month, I talk to Riyaz Patel, the first author on not one, but two articles published this issue, presenting analyses from the GENIUS-CHD consortium. But before we get to the interview, let's review what else was published this month.                                            First up, we have a paper from Tamiel Turley, Timothy Olson and colleagues from the Mayo Clinic, entitled Rare Missense Variants in TLN1 Are Associated With Familial and Sporadic Spontaneous Coronary Artery Dissection. In this study, the authors were interested in identifying novel susceptibility genes for spontaneous coronary artery dissection or SCAD, which predominantly affects young women who appeared otherwise healthy. They conducted whole exome sequencing in a family with three affected family members and found a rare missense variant in the TLN1, or talin 1, gene. This gene encodes the talin protein which is part of the integrin adhesion complex linking the actin cytoskeleton to the extracellular matrix. This gene and protein is highly expressed in coronary arteries. They went on to sequence additional sporadic cases of SCAD, and they found additional talin 1 variants in these individuals. While there was evidence for incomplete penetrance, these data implicate TLN1 as a disease-associated gene in both familial and sporadic SCAD.                                            The next paper comes from Miroslaw Lech, Jane Burns, and colleagues from UCSD School of Medicine and Momenta Pharmaceuticals and is entitled Circulating Markers of Inflammation Persist In Children And Adults With Giant Aneurysms After Kawasaki Disease. Kawasaki disease is the most common cause of acquired pediatric heart disease, but disease progression can vary a lot, and it's likely modulated by complex gene-environment interactions. Coronary artery aneurysms occur in about 25% of untreated patients, but early treatment with intravenous immunoglobulin or aspirin reduces the risk for these aneurysms to 5%, suggesting an important role for inflammation. In this study, the authors applied shotgun proteomics, transcriptomics, and glycomics on eight pediatric Kawasaki disease patients at the acute, subacute, and convalescent time points. They identified inflammatory profiles characterizing acute disease which resolved during the subacute and convalescent time points, except for in the patients who went on to develop giant coronary artery aneurysms. They went on to carry out proteomics on nine Kawasaki disease adults with giant coronary artery aneurysms and matched healthy controls, and they confirmed the inflammatory profiles in the adult samples.                                            In particular, calprotectin, which is composed of S100A8 and S100A9, was elevated in the plasma of patients with CAA, an association they confirmed in additional samples of pediatric and adult Kawasaki disease patients and healthy controls. These data suggest that calprotectin may serve as a biomarker of ongoing inflammation in Kawasaki disease patients following acute illness, and may be able to identify individuals at increased risk of aneurysms.                                            Next up, we have a research letter, Heart BioPortal: An Internet-of-Omics for Human Cardiovascular Disease Data, from Bohdan Khomtchouk, Tim Assimes, and colleagues from Stanford University. They had noticed that, in contrast to the field of cancer research, there were no open access platforms for cardiovascular disease data that offered users the ability to visualize and explore high quality data. They set out to fix this and developed the Heart BioPortal, which is accessible at www.heartbioportal.com. This portal allows the user to integrate existing CDD related omics data sets in real time and provides intuitive visualization and analyses in addition to data downloads. The primary goals are to support gene, disease, or variant-specific request, and to visualize the search results in a multi-omics context.                                            They currently collate gene expression, genetic association, and ancestry allele frequency information for over 23,000 human genes and almost 6,000 variants across 12 broadly defined cardiovascular diseases spanning 199 different research studies. And this is just the start, they're hoping to add more studies, more data, and functionality for querying CDD drug targets, along with lots more. This is a really great resource which will no doubt be of real value to the community. I urge you to go online, check it out, put in your favorite gene, and see what you find.                                            Riyaz Patel, Folkert Asselbergs, and many, many collaborators published Subsequent Event Risk in Individuals With Established Coronary Heart Disease: Design and Rationale of the GENIUS-CHD Consortium and Association of Chromosome 9p21 with Subsequent Coronary Heart Disease Events: A GENIUS-CHD Study Of Individual Participant Data. These papers present the design of the genetics of subsequent coronary heart disease, or GENIUS-CHD consortium, which was established to facilitate discovery and validation of genetic variants and biomarkers for risk of subsequent CHD events in individuals with established CHD. The consortium currently includes 57 studies from 18 countries, recruiting over 185,000 participants with either acute coronary syndrome, stable CHD, or a mixture of both at baseline. All studies collected biological samples and followed up study participants prospectively for subsequent events. Enrollment into the individual studies took place between 1985 to the present day, and the duration of follow-up ranges from nine months to 15 years. Participants have mostly European ancestry, are more likely to be male, and were recruited between 40 to 75 years of age.                                            In their first analysis using these data, they investigated whether the established 9p21 locus associated with subsequent events in individuals with established coronary heart disease. Confirming previous smaller studies, they showed that while genotype at 9p21 is associated with coronary disease when compared to healthy controls, 9p21 genotype is not associated with a risk of future events in people who already have coronary disease. Dr. Patel joins me to tell me more about the GENIUS-CHD consortium and the analyses described in these papers.                                            Today, I'm joined by Dr. Riyaz Patel, who's an associate professor at University College London and a cardiologist at the Barts Heart Centre in London. Dr. Patel, thank you so much for joining me. Dr. Riyaz Patel:                Pleasure to be on, thanks. Jane Ferguson:                So, as we're going to discuss, you are the lead author on two back-to-back publications that were published in Circ Gen this month exploring genetic predictors of coronary heart disease as part of the GENIUS-CHD consortium. Before we delve fully into them, could you tell us a little bit about your background and how you got into this research field? Dr. Riyaz Patel:                Yes. I'm an academic cardiologist, as you know, and I first got into genetics of coronary disease about 12-13 years ago, now, around the time that genome wide association studies were about to take off, or were taking off. I studied, I worked at Emory University, in fact, in Atlanta, in the US. We had a very big cohort of patients who had coronary disease, who were undergoing coronary angiography. At that time, we were doing quite a lot of genetic association studies and biomarker work in patients with heart disease. One of the key problems we often encountered was sort of looking for replication cohorts and trying to do things at a bigger scale than what we had available. So that kind of really was the initial driver for trying to bring together a bigger collaboration to take that sort of work to the next level. Jane Ferguson:                It sounds like you've got valuable expertise, because looking at the author list for these papers, I think it's one of the longest author lists I've ever seen. It's a huge endeavor. I'd love to hear more about how that got started and how you managed to build this consortium, and you know, and tell us what the consortium actually is. Dr. Riyaz Patel:                Yeah, it's been a labor of love. And essentially, I started when I returned back to the UK and we were looking to develop this further. We had already collaborated with several colleagues in the US and abroad from my time at Emory. So, we pulled together a small group of people who we were already working together with and then we did predicts of systematic searches of literature to identify cohorts who were also doing similar things. Again, investigating people with heart disease and looking at subsequent event risk. So, we did that and then we systematically approached, very much, as many people as we could find and over the course of the last, maybe 3 or 4 years, we've brought together a small community of collaborators around the world, and as you rightly said, it's a very long list. In total, we're counting around 180 or so investigators. But, in a way, that also speaks to how this consortium is not just a collection of studies. It is a collection of people and a lot of expertise was brought to the table because of that. People have been thinking about these questions for many, many years and this platform essentially is an opportunity for everyone to share that knowledge. Dr. Riyaz Patel:                So that's kind of how the consortium started and is being pulled together. We operate on a sort of loose memorandum of understanding where every member of the consortium is free to participate in studies as they wish. We run analysis in a federated way which means that [inaudible 00:10:50] scripts are shared and people standardize their data and then they run analyses locally and they only share summary level data so that obviously overcomes the big governance hurdle. So, that's pretty much how the consortium works at moment. Jane Ferguson:                Yeah. I'm sure there was probably a lot of challenges along the way in figuring this out and getting scripts that work for everybody, dealing with all the people, so how do you do this? Do you have regular phone calls with 180 people on it? Do you have lots and lots of emails? Dr. Riyaz Patel:                (laughs) Jane Ferguson:                How's it actually working? Dr. Riyaz Patel:                So, we have a steering committee which is represented by at least one person from each study. So, that limits the number of people down to about, a more manageable number, about 50 or 60. And we do have regular teleconferences, particularly in the early days when we were still pulling everything together. Now, we try and meet at least once a year, if not twice at year at the major conferences, at the European Site of Cardiology and one of the big American meetings, ACC or AHA, so that's usually a good face to face meeting that we have with everyone and then as with all consortia, we have regular email lists and contact through that means. Jane Ferguson:                So, now that you've got everybody together, you have over 185,000 participants as part of this from 18 different countries. So, how have you been able to use all of these different data and harmonize the different phenotypes and sort of put everything together to actually run the analyses. Dr. Riyaz Patel:                The way we started off is by asking everyone to share almost an inventory of what they have collected. We then sought to try and standardize all of the core variables: age, sex, smoking and so forth. Once we were happy about the key variables had been standardized, units were the same and so forth, we then created, effectively a GENIUS-CHD data set that each cohort had curated. So, this was the main way of harmonizing the data set. Now, obviously, there are a lot of other differences between each of these studies. So, we have within the consortium a combination of different studies. We have randomized clinical trials, we have cohort studies, we have nested cohorts from larger population studies and we try and, in all of the analyses, we have pre specified subgroup analyses to try and look out and check for any heterogeneity that is introduced because of all of this. But the biggest, sort of, difference that we have factored in is that each of these studies collects patients with different types of coronary heart disease. Dr. Riyaz Patel:                So, there are about ... 40% or so are acute coronary syndrome recruited patients, where these people are recruited at the time or after their acute event. And a similar proportion are recruited when they're much more stable. So, in all of our analyses we do try and factor in the differences in terms of the type of CHD patients are enrolled with but everything else, as best as we can, we have tried to standardize including all of the outcomes. So, for example, we share the ICD codes that would define a particular type of outcome across all the different cohorts, so even if you're in a different country, they will generally be reasonably well standardized. Jane Ferguson:                Mm-hmm (affirmative), yeah, yeah. I think it's important and I can see the pros and the cons, you know, you have more diversity and you're representing a broader spectrum of disease by including everybody but then, of course, it's hard to figure it out, but I'd say it gives you a lot of versatility with the types of analyses you can do. Jane Ferguson:                As we mentioned, there's two papers so people can go online and read those two papers. And the first one, is sort of the design and goes really into detail of how you guys set this up and I think is a really nice, sort of, example of, if anybody else was trying to (laughs) do something like this, of how to follow it. But then you also did, sort of, an initial analysis, right, to show what this consortium can actually do. I looked at 9p21, so I'd love to hear more about those analyses. Dr. Riyaz Patel:                Yeah, so 9p21 is one of the most reproduced variants with coronary disease across the world. And it's remarkable how well replicated it's been in all sorts of settings in different countries. But the key thing is that it's been associated mostly in case controlled studies or in first event type of studies. And when we looked at this question some years ago now, at whether a variation of chromosome 9p21 is also associated with subsequent events, IE., we could test in people who've already had a heart attack or coronary disease, does it predict a worse outcome for them. We found that it hadn't. Dr. Riyaz Patel:                [inaudible 00:16:06] was in the literature metro analyses and, sort of, all the caveats that come with that. So, we thought that as a feasibility analysis within the consortium, "why don't we also look at 9p21," which we did and this time around, we were able to identify that 93,000 people with coronary heart disease who had our primary endpoint of coronary heart disease death or MI subsequent to other index events. Again, we confirmed our previously met analyses findings that in this particular setting, 9p21 doesn't seem to associate with risk of subsequent events. And that sort of fits with our understanding of 9p21 so far. And interestingly, in one of our analyses, we identified that it does associate with risk of repeat revascularization. And from what we know about 9p21 so far, it seems to associate with risk of atheroma development or progression as opposed to perhaps plaque vulnerability or rupture which might give you an acute coronary event. Dr. Riyaz Patel:                So, it's been a good example, I think, and really an illustration of how this consortium can work at scale. We have a lot of flexibility in terms of different subgroups that we can look at. And we really drilled down in this paper at all the possible reasons why a neutral finding may have occurred. We've looked at selection bias, we've looked at all the different subgroups which was can do because of the scale of the analysis. So, yeah, so that's kind ... it's really, the findings are not particularly novel in their own right but it is a very good example of feasibility of a consortium. Jane Ferguson:                Yeah, I agree. Because it is, so often, if you get, sort of, a negative finding, you keep wondering, "Well, was it just the power? Do we not ... are we not able to find it?" But, I think, with the scale that you have, you're really able to drill down and say, "Look, we really think there's nothing here. It's a true negative finding." You know, 9p21 is not associated with subsequent events, although, I think the revascularization is interesting and that can, sort of, inform, I guess, more basic research into the the mechanisms of 9p21. Dr. Riyaz Patel:                Exactly. Exactly. Jane Ferguson:                So, what's next? I'm sure there's a lot more papers and analyses that are, sort of, to come out of this. So, can you give us, sort of, a sneak peek of what you're working on now? Dr. Riyaz Patel:                Yeah, so, like with 9p21, we did have a selection of variants to answer important questions. So, for example, we were looking at the role of PCSK9 variation to try and see how that relates in this particular setting, given that trials have already reported on the effective drug. And similarly, we're also looking at interlinking six receptor blockade as a, sort of, similar sort of [inaudible 00:19:11] randomization study to look at the validity of a drug target in a secondary prevention setting. Dr. Riyaz Patel:                Beyond that, we are looking at genome wide association studies and, hopefully, once that is done, the consortium will be in a position to do lots of quick look-ups or all sorts of different questions in genetic variation to inform drug target analyses. So, those are immediate priorities, but we are also, in parallel, looking at non-genetic analyses, so, once again, there are lots of standard clinical risk factors that we need to explore a bit more thoroughly in this setting. So as you're aware, there are various paradoxes that keep creeping up in studies where patients have coronary heart disease already, so the obesity paradox is a good example. And what we're hoping to do, is we're hoping to drill down into many of these observational findings in this particular setting, which hasn't really been done, simply again, because the lack of available resources of anything at this scale. Jane Ferguson:                It's exciting and it sounds like you have a really powerful set of different data sets to be able to ask a lot of interesting questions. So, I'm excited to see what's gonna come out next. Dr. Riyaz Patel:                The other key thing we're working on is also about risk prediction. So, again, one of the things we're missing in the clinical community is good risk prediction tools for subsequent event risk among patients with heart disease. We are working with various colleagues to try and develop better risk prediction algorithms for people who've survived coronary event or have coronary disease. Jane Ferguson:                Alright, that's really interesting and that feeds in really nicely then to, sort of, the precision medicine approach. Well, congratulations on building this. I think that's a huge effort in itself and then also in these two papers that were published this month. I think it's really, really, really great work. Dr. Riyaz Patel:                Well thank you. And a key message here is that we want to build and expand this community of investigators around the world who are looking at risk question because individually, I think, we've all struggled with various, sort of, issues. But collectively, I think we have so much more potential to really address some big questions. And the consortium, as I mentioned, is not just investigator led in terms of what we're doing. We're also very open to collaboration and for people wishing to replicate their own findings and are looking for similar cohorts or larger scale validation opportunities so that is also another key advantage in benefit or risk consortium. Jane Ferguson:                Well, that's wonderful. So, if anyone has either data sets that they want to contribute, are you still, sort of, accepting new investigators? Dr. Riyaz Patel:                Absolutely. Very much so. I mean, in the paper, we do mention that we are limited, particularly in terms of cohorts that are enriched for female patients as well as cohorts enriched for patients who are non-Caucasian, in terms of ethnicity. Because, again, those are important patient groups that we need to address. But, generally speaking, we are absolutely open to including anyone who's interested and who meets the inclusion criteria which is collecting people with coronary heart disease, have got genotyping or examples stored for future analysis and have prospective outcomes connected. Jane Ferguson:                And is there a minimum size of sample that somebody needs to participate? Dr. Riyaz Patel:                Ideally, we'd like to, sort of, set that level at about 1,000 recruited patients. But again, if someone has a very deeply phenotyped cohort and that are interested, we'd be more than happy to discuss that and take that to the steering group. Jane Ferguson:                Okay, wonderful. So, people can just email you if they wanna contact- Dr. Riyaz Patel:                Absolutely. Jane Ferguson:                You any further. Dr. Riyaz Patel:                We also have a website, which is for the consortium, which also has contact details on there. Jane Ferguson:                Okay, perfect. Alright, so let me see. Your email is riyaz.patel@ucl.ac.uk- Dr. Riyaz Patel:                Right. Jane Ferguson:                And then the website for the consortium? Dr. Riyaz Patel:                Www.genius-chd.org Jane Ferguson:                Okay. Perfect. Thank you. So, any listeners that are interested, we'll urge them to either go to the website, read some more, go read the papers, email you directly to talk more. Thank you so much for joining me and for talking about this work. Dr. Riyaz Patel:                Thank you for having me. Jane Ferguson:                That's it for April. Come back in May for the next issue. And thank you for listening.                                            This podcast was brought to you by Circulation: Genomic and Precision Medicine and the American Heart Association Council on Genomic Precision Medicine. This program is copyright American Heart Association 2019.  

Jane Ferguson:                Hello, and welcome to episode 26 of Getting Personal: Omics of the Heart, the podcast from Circulation: Genomic and Precision Medicine. I'm Jane Ferguson. It's March 2019, and I'm ready to spring into this month's papers, and apparently make really bad seasonal related jokes. Sorry all. Okay, let's get started.                                            First up, is a paper from Oren Akerborg, Rapolas Spalinskas, Sailendra Pradhananga, Pelin Sahlén and colleagues from the Royal Institute of Technology in Solna, Sweden entitled "High Resolution Regulatory Maps Connect Vascular Risk Variants to Disease Related Pathways." Their goal was to identify non-coding variants associated with coronary artery disease, particularly those with putative enhancers and to map these to changes in gene function. They generated genomic interaction maps using Hi-C chromosome confirmation capture, coupled with sequence capture in several cell types, including aortic and ethelial cells, smooth muscle cells and LPS stimulated THP-1 macrophages.                                            They captured over 25,000 features and they additionally sequenced the cellular transcriptomes and looked at epigenetic signatures using chromatin immunoprecipitation. They looked at regions interacting with gene promoters and found significant enrichment for enhancer elements. Looking at variants previously implicated in genome-wide associated studies, they identified 727 variants with promoter interactions and they were able to assign potential target genes for 398 GWAS variants.                                            In many cases, the gene associated with a particular variant was not the closest neighbor, highlighting the importance of considering chromatin lupane when assigning intergenic variants to a gene. They identified several variants that interacted with multiple promoters, influencing expression of several genes simultaneously.                                            Overall, this paper is a great resource for the community and takes many of these GWAS hits to the next level in starting to understand their biological relevance. They have a lot of supplemental material available online so it's definitely worth checking that out and taking a look for your favorite non-coding variant or chromosomal region to see if you can get some more information on it.                                            Next up, Pierrick Henneton, Michael Frank and colleagues from the Hopital Europeen Georges-Pompidou in Paris bring us "Accuracy of Clinical Diagnostic Criteria For Patients with Vascular Ehlers-Danlos Syndrome in a Tertiary Referral Center." The authors were interested in determining the accuracy of the diagnostic criteria used to select patients for genetic testing for suspected vascular Ehlers-Danlos syndrome. This is because, despite the Villefrench criteria being recommended for diagnosis, the accuracy of the diagnostic criteria was never formally tested.                                            They selected 519 subjects, including 384 probands and 135 relatives who had been seen between 2001 and 2016. They assessed the sensitivity and specificity of the Villefrench classification. Almost 32% of tested individuals carried a pathogenic COL3A1 variant. The sensitivity of the Villefrench criteria was 79% with a negative predictor value of 87%. Symptomatic probands had the highest accuracy at 92% sensitivity and 95% negative predictive value. However, the specificity was just 60%.                                            Applying revised diagnostic criteria from 2017, it was actually less accurate because even though there was an increase in specificity, the sensitivity was reduced. Overall diagnostic performance was worst in individuals under 25 and neither set of diagnostic classifications allowed for early clinical diagnosis in individuals without a family history.                                            Our next paper is a Mendelian randomization analysis from Susanna Larsson, Stephen Burgess and colleagues from Uppsala University and the University of Cambridge. This paper entitled "Thyroid Function And Dysfunction In Relation to Sixteen Cardiovascular Diseases: A Mendelian Randomization Study" aims to understand how subclinical thyroid dysfunction relates to risk of cardiovascular diseases. They generated genetic predictors for thyroid stimulating hormone, or TSH, through a GWAS meta-analysis in over 72,000 individuals. They then analyzed the association of genetically predicted TSH with cardiovascular outcomes in large GWAS studies of atrial fibrillation, coronary artery disease, and ischemic stroke, and further assessed associations with phenotypes in the UK Biobank.                                            They found genetically decreased TSH levels and hyperthyroidism were associated with increased risk of atrial fibrillation but not other tested phenotypes. Overall, these data support a causal role for TSH and thyroid dysfunction in atrial fibrillation but not in other cardiovascular diseases.                                            The next paper is also a Mendelian randomization analysis from members of the same group, Susanna Larsson, Stephen Burgess and colleagues published "Resting Heart Rate and Cardiovascular Diseases: A Mendelian Randomization Analysis." In this letter, they describe a study of the relationship between genetically increased resting heart rate and cardiovascular diseases. They constructed genetic predictors of resting heart rate and similarly to the previous study, used that as an instrument to test for associations with coronary artery disease, atrial fibrillation, and ischemic stroke in the cardiogram, atrial fibrillation, and mega stroke consortia respectively.                                            They also looked at 13 CVD outcomes in the UK Biobank. They found that genetically predicted heart rate was inversely associated with atrial fibrillation with suggestive evidence for an inverse association with ischemic, cardioembolic, and large artery stroke. The inverse association with AF was replicated in the UK Biobank, supporting previous reports linking resting heart rate to atrial fibrillation.                                            Next up, we have a letter from Robyn Hylind, Dominic Abrams, and colleagues from Boston Children's Hospital. This study entitled "Phenotypic Characterization of Individuals with Variants in Cardiovascular Genes in the Absence of a Primary Cardiovascular Indication For Testing" describes their work to probe incidental findings for potential cardiovascular disease variants in individuals undergoing clinical genomic sequencing for non-cardiac indications.                                            They included 33 individuals who had been referred as carrying variants that were indicated as being associated with cardiovascular disease in primary or secondary findings. The variants were reclassified using the 2015 ACMG guidelines, and then were compared to the original classification report obtained at the time of sequencing.                                            Of 10 pathogenic or likely pathogenic variants, only four of these were actually considered pathogenic or likely pathogenic after reclassification under the 2015 ACMG criteria, and none of these were associated with a cardiac phenotype. None of the variants could be definitively linked to any cardiac phenotype.                                            The costs ranged from $75 to over $3700 per subject with a cost per clinical cardiac finding estimated at almost $14,000. This study highlights the relatively high cost and low yield of investigating potential cardiovascular variants and prompts consideration of how to implement strategies to ensure that variant reporting maximizes clinical return but minimizes the financial, time, and psychological burdens inherent in lengthy follow-ups.                                            The next paper is a clinical letter from Serwet Demirdas, Gerben Schaaf and colleagues from Erasmus University Rotterdam  entitled "Delayed Diagnosis of Danon Disease in Patients Presenting with Isolated Cardiomyopathy." They report on a clinical case of a 14-year-old boy presenting with cardiac arrest due to ventricular fibrillation during exercise. Echocardiography and MRI showed cardiac concentric hypertrophy, particularly in the left ventricle. The boy's mother had died at age 31 after being diagnosed with peripartum dilated cardiomyopathy.                                            Sequencing in the boy revealed a variant in the LAMP2 gene, known to be responsible for Danon disease, which typically presents as cardiomyopathy, skeletal myopathy, and intellectual disability. This same LAMP2 variant was found in preserved maternal tissue, but not in other family members. In this case, there was no evidence of muscle or intellectual abnormalities. However, sequencing had allowed for this diagnosis of Danon disease in the child and posthumously in his mother. This study demonstrates a utility of using extended gene panels in clinical sequencing to aid in diagnosis and to inform management of patients.                                            The next letter is from Alvaro Roldan, Julian Palomino-Doza, Fernando Arribas and colleagues from University Hospital of the 12th of October in Madrid and is entitled "Missense Mutations in the FLNC Causing Familial Restrictive Cardiomyopathy: Growing Evidence." This report also highlights clinical cases. In this case, two individuals with variants in the filamin C, or FLNC gene. Two unrelated individuals presenting with restricting cardiomyopathy were sequenced and found to carry two different variants in the FLNC gene, one of which had not been previously reported.                                            This expands the number of reported cases of filamin C mutations in restrictive cardiomyopathy and highlights the need for further study of the pathophysiology linking filamin C to cardiac function.                                            Finally, we have some correspondence related to a previously published article. In the letter, Christopher Chung, Briana Davies, and Andrew Krahn comment on the recently published article from Jody Ingles on concealed arrhythmogenic right ventricular cardiomyopathy in sudden unexplained cardiac death events. In that paper earlier this year, they had reported on four cases of individuals presenting with cardiac arrest or sudden cardiac death, attributable to concealed arrhythmogenic right ventricular cardiomyopathy with underlying mutations in the plakophilin-2 gene. In the letter from Chung et al, they report similar findings where individuals may first experience electrical phenotypes before manifesting structurally detectable disease. Indeed, in their response to this letter, Ingles et al report identification of an additional case since publication of their original article.                                            Taken together, this further strengthens the case for development of additional strategies to identify at risk individuals and predict and prevent disease events.                                            That's all for the papers for March 2019. Go online to check them out and follow us on Twitter @Circ_Gen to see new papers as they are published online. Thanks for listening. Until next month everyone. This podcast was brought to you by Circulation Genomic and Precision Medicine and the American Heart Association Council on Genomic and Precision Medicine. This program is copyright American Heart Association 2019.  

Jane Ferguson:                Hi everybody. Welcome to Episode 25. I'm Jane Ferguson. This is Getting Personal: Omics of the Heart, the podcast from Circulation: Genomic and Precision Medicine, and it is February 2019. Let's get started.                                            The first paper this issue is a concurrent publication and comes to us from 29 different editors-in-chief of 27 major cardiovascular journals, led by Joseph Hill, editor-in-chief of Circulation. This editorial, entitled Medical Misinformation: Vet the Message! gives a pointed reminder of the real life risks of misinformation that spreads rapidly through social media and influences people who are making crucial decisions about healthcare for themselves and their families. Quoting directly from the paper they say, "We, the editors-in-chief of the major cardiovascular scientific journals around the globe, sound the alarm that human lives are at stake. People who decline to use a statin when recommended by their doctor, or parents who withhold vaccines from their children, put lives in harm’s way."                                            In this editorial they call on those in the media to do a better job of taking responsibility for the information they disseminate. In particular, in evaluating content before disseminating it, and avoiding false equivalencies where overwhelming scientific evidence favors one side of the so called "debate." I'll add to that that those of us who are medical or scientific professionals need to do our best to take the time to explain our science to those around us. The science underlying most of medicine is complex and hard to explain and sometimes incomplete, but we do a disservice to people if we don't at least try. Let's all join the editors in calling everyone to vet information and hold those with power in the media accountable for the spread of misinformation they enable.                                            Next up this issue, a paper from Jody Ingles, Birgit Funke, and co-authors from the University of Sydney, Harvard Medical School and others, entitled Evaluating the Clinical Validity of Hypertrophic Cardiomyopathy Genes. As panels for clinical genetic testing expands to include more genes, there are more and more variants that are detected and reported to patients, but do not necessarily have underlying evidence to support or disprove pathogenicity. This group aimed to systematically assess the validity of potential gene disease associations with hypertrophic cardiomyopathy and left ventricular hypertrophy by curating variants based on multiple lines of genetic and experimental evidence.                                            They categorized genes based on the strength of evidence of disease causation and reviewed HCM variant classification in the ClinVar variant and phenotype repository. They selected 57 genes to study based on those which were frequently included on test panels or had previous reports of association with HCM. Of HCM genes, only 24% were characterized as having definitive evidence for disease causation, 10% of the genes had moderate evidence, while 66% had limited or no evidence for disease causation. Of syndromic genes, 50% were definitively associated with left ventricular hypertrophy. Of over 4,000 HCM variants in ClinVar, 31% were in genes that, on review, had limited or no evidence for association with disease.                                            What this study shows is that many genes that are included on panels for diagnostic testing for HCM actually have little evidence for any relationship to disease. Systematic curation is required to improve the accuracy of information being acquired and reported to patients and families with HCM.                                            Moving on to the next paper. This manuscript describes the international Triadin Knockout Syndrome Registry: The Clinical Phenotype and Treatment Outcomes of Patients with Triadin Knockout Syndrome. It comes from Daniel Clemens, Michael Ackerman and colleagues from the Mayo Clinic. So, Triadin Knockout Syndrome is a rare inherited arrhythmia syndrome and it is caused by recessive null mutations in the cardiac triadin gene. To improve the ability to study this rare syndrome, this group established the International Triadin Knockout Syndrome Registry, with the goal of including patients across the world with homozygous or compound heterozygous triadin null mutations. The registry currently includes 21 patients from 16 families who have been carefully phenotyped and many of whom exhibit T wave inversions and have transient QTC prolongation.                                            The average age for first presentation with cardiac arrest or syncope was three years of age. Despite a variety of treatments, the majority still have recurrent breakthrough cardiac events. These data highlight the importance of conducting testing for triadin mutations in patients, particularly young children presenting with cardiac arrest, and as this registry grows it will enable a better understanding of the disease and hopefully pave the way for future triadin gene therapy trials.                                            The next paper comes from Daiane Hemerich, Folkert Asselbergs and colleagues from Utrecht University, and is entitled Integrative Functional Annotation of 52 Genetic Loci Influencing Myocardial Mass Identifies Candidate Regulatory Variants and Target Genes. They were interested in whether variants that have been associated with myocardial mass may exert their influence through regulatory elements. They analyze the hearts of hypertrophic cardiomyopathy patients and non-disease controls and ran ChIP-seq in 14 patients and 4 controls and RNA-seq in 11 patients and 11 controls.                                            They selected 52 loci that have been associated with electric cardiogram defined abnormalities in amplitude and duration of the QRS complex and looked specifically at these gene regions. They found differential expression of over 2,700 different genes between HCM and control. They further found differential acetylation over 7,000 regions. They identified over 1000 super enhancers that were unique to the HCM samples. They found significant enrichment for differential regulation between disease and control hearts within the loci previously associated with HCM, compared with loci not associated with HCM. They analyzed regions where putative causal SNPs overlapped regulatory regions, and identified 74 co-localized variants within 20 loci, with particular enrichment for SNPs in differentially expressed promoters. They confirmed associations with 18 previously implicated genes, as well as identifying 14 new genes. Overall, what this study demonstrates is that by looking at regulatory features that differ in affected tissues between disease and healthy individuals, we can learn more about the underlying mechanisms of disease.                                            Moving on, we have a paper entitled Interleukin-6 Receptor Signalling and Abdominal Aortic Aneurysm Growth Rates from Ellie Paige, Marc Clément, Daniel Freitag, Dirk Paul, Ziad Mallatt and colleagues from the University of Cambridge. They aimed to investigate a specific SNP in the Interleukin-6 receptor rs2228145, which has been associated with abdominal aortic aneurysms. Inflammation is thought to be a contributor to aneurism progression. The authors hypothesized that the IL-6 receptor's SNP may affect aneurysm growth. They use data from over 2,800 subjects from nine different prospective cohorts and examine the effect of genotype on annual change in aneurysm diameter. Although there was a significant association between genotype and baseline aneurysm size, there was no statistically significant association with growth over time. It appeared that growth was less in minor allele carriers, but the effect if true, was small and the analyses were not powered for small effect sizes.                                            Sample sizes are limited for cohorts with abdominal aortic aneurysms and the authors already used all available worldwide data. In complimentary experiments in mice, they examined the effect of blocking the IL-6 receptor pathway. They found that selective blockage of the IL-6 trans-signaling pathway mediated by soluble IL-6 receptor was associated with improved survival in two different mouse models. However, blocking the classical membrane-bound IL-6 signaling pathway in addition to the trans-signaling pathway did not lead to improved survival. Although the severe lack of enough subjects for well powered genetic analyses is a major limitation for the study of abdominal aortic aneurism and humans, this paper demonstrates the potential relevance of the IL-6 trans-signaling pathway and aneurysm growth, and suggests that further interrogation of this pathway may be informative in figuring out new ways to prevent aneurysm progression and rupture.                                            Next, we have the first of two research letters this issue. The letter on Common Genetic Variation in Relation to Brachial Vascular Dimensions and Flow-Mediated Vasodilation comes to us from Marcus Dorr, Renate Schnabel and co-authors from several institutions including University Heart Center in Hamburg. They were interested in gaining a better understanding of the genetics underlying vascular function. They ran a meta-analysis of brachial artery diameter, maximum brachial artery diameter adjusted for baseline diameter, and flow-mediated dilation in over 17,000 individuals of European ancestry from six different GWA studies. They sought to replicate findings in over 9,500 newly genotyped individuals. They identified two novel SNPs for baseline brachial artery diameter, but no SNPs reached significance or replication from maximum brachial artery diameter or flow-mediated dilation. One of the significant SNPs was located in the insulin-like growth factor binding protein 3, or IGFBP-3 gene. They analyzed plasma IGFBP-3 protein levels in 1,400 individuals and found a significant association with brachial artery diameter.                                            The second SNP they identified is located within the AS3MT gene for arsenite methyltransferase, and this SNP appears to be an eQTL for AS3MT expression in monocytes and arterial tissue. Along with identifying these two genes with potential involvement in baseline brachial artery diameter, this study also supports a low genetic component to flow-mediated dilation, indicating that environmental factors may be or more influential in FMD.                                            The final research letter comes from Alexis Williams, Craig Lee and colleagues from the University of North Carolina and is entitled CYP2C19 Genotype-Guided Antiplatelet Therapy and 30-Day Outcomes After Percutaneous Coronary Intervention. It is known that loss of function variants in CYP2C19 effect bioactivation of clopidogrel, and CYP2C19 genotyping is increasingly used to guide antiplatelet therapies. The authors were interested in whether genotype-guided therapy is effective in reducing major adverse cardiovascular events in the short term, specifically in the 30 days following percutaneous coronary intervention, when most MACE occurs. They followed over a thousand individuals undergoing PCI and CYP2C19 testing and looked at atherothrombotic and bleeding outcomes. Consistent with implementation of genotype-guided therapy, individuals carrying loss of function alleles were less likely to be prescribed clopidogrel.                                            However, out of loss of function carriers, those who did take clopidogrel had significantly higher risk of MACE with no difference in bleeding risk. There was no difference by therapy in individuals without a loss of function allele. What this study shows us is that even in the 30 days following PCI, genotype-guided therapy can be effective in protecting individuals carrying loss of function CYP2C19 variants.                                            And that's it from us for February. Go online to ahajournals.org/journal/circgen to read the full papers, access videos and more, and of course to delve into the podcast archives. Thank you for listening and I look forward to bringing you more next month. This podcast was brought to you by Circulation: Genomic and Precision Medicine and the American Heart Association Council on Genomic and Precision Medicine. This program is copyright American Heart Association 2019.  

Jane Ferguson:  Hello, everyone. Welcome to Episode 23 of Getting Personal, Omics of the Heart, the podcast from Circulation: Genomic and Precision Medicine. It's December 2018. I'm Jane Ferguson. So let's get started.                              This month I talked to Dr. Merlin Butler from Kansas University Medical Center about an interesting clinical case he described recently in the Journal of Pediatric Genetics, concerning cardiac presentations in a case of classic Ehlers-Danlos syndrome with COL5A1 mutations.                              Keep listening for that interview, but first, let's talk about the papers in this month's issue of the Journal.                              Our first paper, entitled "Effects of Genetic Variance Associated With Familial Hypercholesterolemia on LDL Cholesterol Levels and Cardiovascular Outcomes in the Million Veteran Program." Comes from Yan Sun, Peter Wilson and co-authors on behalf of the V.A. Million Veterans Program. They were interested in the relatively between variants in LDLR, APOB and PCSK9, and LDL cholesterol in the general population. Low-frequency variants in these genes have been identified to underlie the greatly elevated LDL cholesterol seen in cases of familial hypercholesterolemia, but the effects of the population level are unknown.                              Using data from the Million Veterans Program, the team analyzed the associations between putatively pathogenic variants and the maximum recorded LDL cholesterol level, as measured repeatedly over a 15-year period, in over 330,000 participants. They restricted analysis to variants that were present in at least 30 people and found that eight of the 16 variants tested were associated with significantly higher LDL cholesterol. Through phenome-wide association analysis, they found that carriers had a higher likelihood of a diagnosis of hypercholesterolemia or coronary heart disease, but not of other diagnoses. Even though individuals carrying risk variants generally reduce their LDL cholesterol through statin treatment, they still had residual risk, suggesting that even earlier initiation of treatment may be required in individuals with genetic risk of high HDL.                              Continuing the theme, the next paper comes from Laurens Reeskamp, Merel Hartgers, Kees Hovingh and colleagues from the University of Amsterdam, and is entitled, "A Deep Intronic Variant in LDLR in Familial Hypercholesterolemia: Time to Widen the Scope?" This team had encountered a family with familial hypercholesterolemia, who did not carry a coding mutation in LDLR, APOB or PCSK9, and they wanted to figure out what was causing the elevated LDL cholesterol in this family. They conducted whole-genome sequencing in nine family members, five affected and four unaffected. They found a variant in an intron in LDLR, which resulted in an insertion of 97 nucleotides, leading to a frame shift in premature stop codon in exon 15 of LDLR. They confirmed the disease segregation in a second family, and found a frequency of over 0.2% in additional FH cases without a confirmed mutation.  This study highlights the need to consider more than just exons when looking for causal variants, particularly in families where no coding mutations are identified.                              Next up, from Kathryn Siewert and Ben Voight from University of Pennsylvania, a paper reporting that "Bivariate Genome-Wide Association Scan Identified 6 Novel Loci Associated With Lipid Levels and Coronary Artery Disease." This paper started with a premise that, because heritable plasma lipids are genetically linked to coronary artery disease, we would have greater power to detect variants contributing to both traits by conducting joint GWAS analysis, rather than independent analyses for lipids or coronary disease, as has been done traditionally. Using data from over 500,000 individuals for CAD and over 180,000 individuals from the Global Lipid Genetics Consortium, they conducted a bivariate GWAS and identified six previously unreported loci that associated with CAD and either triglycerides, LDL cholesterol or total cholesterol. Many of these loci also had signals for effects on gene expression of genes in the region, suggesting that these novel loci may affect lipid levels and CAD risk through modulation of gene expression. Interestingly, for some of the newly-identified loci, there were multiple potential regulatory targets, suggesting that these loci may affect lipids and CAD through separate mechanisms. Overall, for closely-linked traits such as lipids and CAD, this joint GWAS approach gives additional power to detect novel variants.                              The next article comes from Terry Solomon, John-Bjarne Hansen and colleagues from University of California-San Diego and the Arctic University of Norway. Their paper concerns the "Identification of Common and Rare Genetic Variation Associated With Plasma Protein Levels Using Whole-Exome Sequencing and Mass Spectrometry." They were interested in identifying genetic variants that associate with plasma protein levels, both to understand genetic regulation and to identify potential sources of bias, where a genetic variant affects the assay used to quantify the protein, without necessarily altering biological components of the protein. Using data from 165 participants of the Tromsø Study, they quantified 664 proteins in plasma by tandem mass tag mass spectrometry and genotypes by whole-exome sequencing. They identified 109 proteins or peptides associated with genotype, and of these identified 49 that appeared to be technical artifacts based on genotype data.  Of the rest, many of the genetic variants affected protein level by modulation of RNA, but some appeared to directly affect protein metabolism. Their method of quantifying multiple peptides from each protein and sequencing exons allowed them to identify spurious associations that would often be missed, and highlights the large number of artifacts that could be present in protein quantitative trait locus studies. At the same time, they show that over half of the pQTLs are real, with genetic variants affecting circulating proteins through diverse mechanisms.                              Our last of the full-length original research articles also applied proteomics. "Proteomic Analysis of the Myocardium in Hypertrophic Obstructive Cardiomyopathy" comes from Caroline Coats, Perry Elliott and coauthors from University College, London. They obtained myocardial samples from 11 patients with hypertrophic cardiomyopathy and measured over 1500 proteins using label-free proteomic analysis. They compared protein expression to six control samples from healthy hearts. They identified 151 proteins that were differentially expressed in HCM hearts, compared with control, and they validated a subset of these using an additional 65 myocardial samples from healthy and diseased subjects. Of eight validated differentially expressed proteins, they represented pathways in metabolism, muscle contraction, calcium regulation and oxidative stress. Of particular interest, they highlighted lumican as a novel disease protein, and showed the potential of proteomics to identify mechanisms underlying HCM.                              We have two research letters this month, the first from Hisato Suzuki, Kenjiro Kosaki and coauthors from Keio University School of Medicine at Tokyo. It's titled, "Genomic Comparison With Supercentenarians Identifies RNF213 as a Risk Gene for Pulmonary Arterial Hypertension." In this letter, they were interested in identifying genetic variants underlying pulmonary arterial hypertension. They hypothesized that individuals with extremely long lifespan would be less likely to carry potentially pathogenic variants. They performed whole-exome sequencing in 76 individuals with PAH and compared them to 79 supercentenarians who had lived for over 110 years. They report variants in RNF213 and TMEM8A that were present in PAH but not in the controls, suggesting these genes may be important in the pathophysiology of PAH.                              The second research letter comes from Tessa Barrett, Jeffrey Berger and colleagues from New York University School of Medicine, and is entitled, "Whole-Blood Transcriptome Profiling Identifies Women With Myocardial Infarction With Nonobstructive Coronary Artery Disease: Findings From the American Heart Association Go Red for Women Strategically Focused Research Network." Most of the 750,000 acute MIs occurring in the U.S. each year are caused by obstructive coronary artery disease, but around 15% of the acute MIs occur in individuals whose arteries have less than 50% stenosis and are defined as unobstructed. These individuals are more likely to be female and of higher morbidity and mortality. In this AHSAFRM-funded project, the team sequenced whole-blood RNA from 32 women who presented with an MI with or without CAD, or controls. They report several thousand transcripts differing between groups on conducted pathway analysis, which highlighted several pathways, most notably estrogen signaling. This suggests that estrogen may be a novel component in MIs occurring in the absence of obstructive disease.                              We also have two clinical letters this month. The first, "Desmoplakin Variant-Associated Arrhythmogenic Cardiomyopathy Presenting as Acute Myocarditis," is brought to us by Kaitlyn Reichl, Chetan Shenoy and colleagues from University of Minnesota Medical School. They report a case of a 24-year-old man presenting with acute myocarditis, who was found to have a pathogenic variant in desmoplakin underlying arrhythmogenic cardiomyopathy, also present in his father and one brother. This case highlights myocarditis as a possible initial presentation of arrhythmogenic cardiomyopathy, which requires cardiac MRI and genetic testing for full evaluation.                              The second clinical letter comes from Judith Verhagen, Marja Wessels and co-authors from University Medical Center, Rotterdam, and is entitled, "Homozygous Truncating Variant in PKP2 Causes Hypoplastic Left Heart Syndrome." They report on a family with consanguineous parents, where two children were affected with left ventricular hypoplasia, leading to intrauterine death in one child and death at day 19 of life in a second child. Sequencing identified a variant in PKP2, which encodes plakophilin 2. Both parents were heterozygous for the mutation, and their affected children were homozygous for the mutation. This mutation resulted in disorganization of the sarcomere and affected localization of other proteins affecting gap junctions. The case highlights PKP2 variants as causal in hypoplastic left heart syndrome.                              Dr. Merlin Butler is a professor at Kansas University Medical Center and Director of their Division of Research and Genetics. Dr. Butler joined me to discuss an interesting case of Ehlers-Danlos Syndrome in a father and son, with heart failure in the father. This case is in press in the Journal of Pediatric Genetics, and the prepublication version is available online, published on the 13th of October 2018. We'll tweet out a link to that paper, if you're interested in viewing the full case, but here's Dr. Butler, who joined me to discuss it now. Dr. Butler:          ... I'm a clinical geneticist here at University of Kansas Medical Center, and I see both adult and pediatric patients, but one of the more common reasons for referral to my adult side clinical genetic services is connective tissue disorders. And that's how we were involved with this particular family, a son and father, that led to my interest in looking at the question about genetics of cardiac transplantation of those patients that present for cardiology services because of heart failure and worked up and ultimately end up as a candidate for transplantation.                              And that transpired in this particular family, which the patient was a 13-year-old boy who was referred into the clinic because of connective tissue disorder. Actually the primary care wanted to rule out Ehlers-Danlos Syndrome. And so we evaluated the 13-year-old boy in the clinic setting, and then we ordered comprehensive connective tissue and next-generation DNA sequencing panel, and lo and behold, he had a mutation of the classical gene that causes classic Ehlers-Danlos, the collagen 5A1 gene. The gene variant was classified as unknown clinical significance, which is often the case as we know with this technology, next-generation sequencing. Regardless of the condition we're looking at, we find about 10% of time, the panel of tests, the panel of genes that come back that are tested. 10% of the time we find no variants, no spelling errors, no mutations. 10% of the time the results come back from the commercial laboratory ... these are clinic patients, so it's done in commercially-approved laboratories, clinically-approved laboratories ... and we find that about 10% is pathogenic, which means it's disease-causing. The gene variant or mutation has been reported before. There is information in the literature that we know that it causes disease, Ehlers-Danlos, whatever type.                              About 80% of the time, the results come back as unknown clinical significance, and this is related to connective tissue. You probably order a test in cardiology or any other service and you'll find the same area. Most of the variants come back as unknown. What is meant by that is they haven't been reported previously in the literature, and therefore we don't know ... They may be disease-causing, that particular change, but we don't know that. We as geneticists, we have to then figure out whether that gene variant is a mutation or background noise. So we go through a process by where we try to characterize that particular gene finding to see whether it could be causative in that particular patient we see, or if it looks like it's probably tolerated and is just background noise, and it has really probably no apparent phenotypic change resulting from that particular gene variant.                              So this particular gene variant that we found, the collagen 5A1, did meet the criteria. We looked for computer programs and silica prediction to see if it was tolerated or damaging. We looked at how common that gene variant is seen in the general population, looking at exact various types of genome databases at the laboratories used to search for that variant in the population that's been serviced by genetic services, to see how rare it is or how common it is. We also check to see if it's a missense change, missense variant that is, one amino acid got switched for a different amino acid. There are five classes of amino acids, so if they stay within the same class, that change one amino acid to the next probably doesn't have much meaning, but if it changes to an entirely different class, like positive to negative, hydrophilic to hydrophobic, that could make a big change at the protein translation level, and therefore impact on protein development and function.                              And then we looked to see if it's conserved in evolution. The laboratories that we use, they look at approximately 80 different animals, mammals, vertebrates, primates, non-mammal vertebrates, to see if that particular spelling change is conserved throughout evolution. If it is, if C is always that position 205 in the coding sequence of that gene throughout evolution, that means you need to have C at that position, not A, G or T, because that would be conserved and impact that we don't want to change that, because it's conserved through evolution.                              So those kind of criteria, how common it is in the population, how conserved it is, what the amino acid change might be and what the computer programs predict that change might relate to the function of the protein. So we used those criteria, found this gene variant, although it hadn't been reported before ... well, it hasn't been characterized as pathogenic. In this particular family, 13-year-old son and 55-year-old father, they both had the classical features of classic Ehlers-Danlos, so that gene variant, we know at this point is informative. Dr. Ferguson:    That's a really helpful introduction to how you go about looking at variants and screening them and picking the ones of most importance. So you had this 13-year-old patient who came in and then you tested the patient, and then did you also test both parents? Other family members? Dr. Butler:          Well, the mother was no longer in the loop, so the primary care, the pediatrician, referred this 13-year-old boy because of joint laxity. He had experienced multiple spontaneous knee dislocations, beginning around nine years of age. He was 13 when I saw him in clinic. He had a history of knee pain, generalized joint hypermobility, loose skin, excessive bruising and poor scarring. And he had that history coming in, and we certainly could identify those findings on this patient. In fact, we reported this patient in the literature. The title of the paper is "Classic Ehlers-Danlos Syndrome in a Son and Father with a Heart Transplant Performed in the Father," published in Journal of Pediatric Genetics, but during a genetics clinic visit, we assessed a hypermobility Beighton scale, that we used to determine the degree of hypermobility, hyperflexibility, and we recorded a score of eight out of nine. Nine is the maximum number. And what we use as kind of a cut-off, this score is five or more, five out of nine or more, then that would indicate that probably there is some kind of joint issues, connective tissue disorder in the way.                              He had no heart murmur detected, normal rate and rhythm, but a previous echocardiogram showing he had no valvular problems but he had aortic root dilation. He also had skin marbling, atrophic scars, particularly on the lower leg, and increased pigment secondary to easy bruising. He had asymmetry of the anterior body wall, pretty classical findings that we recognize in Ehlers-Danlos. Dr. Ferguson:    So the reason we're talking to you about this is actually less related to the son, right? And then related to what you found in the father. Dr. Butler:          The father, right. So the father was 55 years old when we saw him. So we did testing on the son, based on his examinations, and then we obtained DNA and we found out, had the sequencing. We found he had a gene variant of the collagen 5A1 gene. And the collagen 5A1 codes for collagen, low fibrils protein changes, and that's a classical finding we see in Ehlers-Danlos. So we then, on follow-up, we looked more closely at the father, based on what we found in the child, and the father is 55 years of age and he exhibited similar clinical features seen in his son, including stretchable, thin skin, poor scarring, hypermobile joints, with pain and easy bruising. He had a Beighton score of six out of nine, but due to multiple knee surgeries, we were really not able to able to assess his knee findings.                              And he had strabismus repair when he was like 12 years of age. He had surgery on his right knee due to frequent dislocations, and had bilateral foot surgeries due to flat feet, pes planus. He had a stroke at 37 years of age, but without hypertension. At 43 years of age he underwent a heart transplant because of heart failure with no known cause, such as infections or anatomical defects or metabolic problems seen. And at 54 years of age he had fusion of the lower vertebrae, correct complications, nerve compression, impacting ambulation. So he had multiple, multiple problems, and we did DNA testing on him. He also had the same gene variant of the collagen 5A1 gene, which causes classic Ehlers-Danlos Syndrome. Dr. Ferguson:    Yeah, so he essentially had been undiagnosed his entire life, I guess. Dr. Butler:          In his entire life, he just kind of lived with it. Obviously no one really picked it up because he had multiple, multiple orthopedic surgeries. Of course he had the cardiac transplant because of a very large heart size. They didn't really find out what had taken place with that. They didn't find any reason why he had heart failure. So, because of this connective tissue issue, I began to think more closely about this. Could somehow his cardiac transplantation due to no determined reason why he had heart failure, could that somehow be related to a connective tissue problem, such as classic Ehlers-Danlos?                              And classic Ehlers-Danlos is fairly common, about one in 20,000 people. As far as our concern in the field of genetics, one in 20,000 is common, because we see rare diseases. So one in 20,000 is common. There's like six different categories of Ehlers-Danlos in classic and hypermobile form, vascular form, but he had the clinical findings, he and his son, and he had mutation of a gene that causes classic Ehlers-Danlos.                              So the thrust of this communication is, could it be that there may be a group of individuals that are on a heart transplantation service, waiting to be transplanted, that might have a connective tissue disorder, such as Ehlers-Danlos or one of the other connective tissue disorders, that could be an issue and a causation of their cardiac issues? We know that there are around 70 genes being recognized that cause connective tissue, and these numbers increase all the time as we learn more about genetics and the capabilities of testing. There are over 130 recognized genes that are thought to play a role in hereditary cardiomyopathies and there are now thought to be over 230 genes that are commercially available in a comprehensive cardiovascular next-generation DNA panels, and several of those genes are collagen genes.                              So we know there are hundreds of genes that play a role with cardiac health, I guess. Disturbance of those genes, several of those could be connective tissue. Obviously there's others involved, too ... myopathies and conduction issues. But the question I would have, the focus is, could there be a group that would have a connective tissue? And why is that important? Well, not only do they have issues when it comes to these multiple surgical concerns, but they may have, obviously, concerns that might be related to complications of surgery. We know that connective tissue disorders, they have poor wound healing, scarring and other tissue involvement such as vascular anomalies, aneurysms. So they become ... whether it's for cardiac procedures or whether it's orthopedic, whatever ... they become poor candidates for surgical intervention, for surgical operation procedures, because of the complications of surgery. Connective tissue, poor wound healing, scarring.                              And because connective tissue is involved in not only the skin, but involves internal organs such as the vessels, where you're concerned about aneurysms and vascular anomalies, that could be playing a role. So there may be more complications related to the surgical procedures than your typical patient who undergoes heart transplantation.                              So I think that would be important to know, so I would encourage, for the cardiology services, for patients that are on these transplant care and services, to consider a comprehensive genetic DNA analysis to look at connective tissues, as well as other causations of cardiac disease. As I mentioned, there's over 200 different genes been recognized now on comprehensive DNA testing panels related to cardiac and connective tissue problems.                              So, I would encourage that patients that are on the transplant list, they should undergo a next-generation detailed comprehensive connective and cardiovascular panel ... they're certainly available in several laboratory settings ... that might help lead to not only the diagnosis of the cardiac issues, but might help in medical management and monitoring and the surveillance, as well as the surgical interventions and care following the surgical procedures might be taking place.                              Frequently have an arterial wall might be a little fragile and obviously clamping during surgical procedures for an extended period of time might cause some trauma, even to a normal artery, let alone an artery that might be disorganized because of connective tissue problem. Dr. Ferguson:    Yeah. Dr. Butler:          So those complications might occur as well, too, during the procedure or following the procedure. Even there may not be any aneurysms going in, there might be a weakness of the arterial wall at the clamp site that could lead to an aneurysm following the procedure, so it needs to be monitored.                              So I'm just bringing these to the medical attention that may or may not be out there, but I want to bring this to ... You know, there have been over 90,000 heart transplants been done since 1983, at least that many, and there's 23 million people worldwide that are affected with congestive heart failure, and that's about 7.5 million people in North America. Dr. Ferguson:    Yeah. Dr. Butler:          So it's out there. Some of these genetic conditions are rare, but collectively they're common. Ehlers-Danlos, one in 20,000, is probably considered rare, but yet it still is not rare to the person that has it. Dr. Ferguson:    Right, right, and maybe enriched in these patient populations. So is this something you think that could be sort of found with more careful physical exams, or do you think that [crosstalk] genomic sequencing is sort of the best way to get at this? Dr. Butler:          Well, I think that Beighton scale we just mentioned, the hypermobility scale, just to see if there's, you know, if it's pretty common. Most adults can't put their palms on the floor when they're standing up. Dr. Ferguson:    I certainly can't. Dr. Butler:          That is usually not gonna happen for multiple reasons. But maybe some of the cardiologists are, but those that aren't, maybe they should consider, just check for hyperflexibility in their adult patient. [crosstalk] Dr. Ferguson:    Yeah, that seems like an easy [crosstalk] click-and-check, right? Dr. Butler:          Right. There being loose skins, poor scarring. You can ask the patient, obviously. Easy bruisability and poor scars, and it's pretty obvious in these conditions. I mean, on a physical exam it jumps out at you, particularly the multiple scars and bruising on the lower extremities with the pigmented because of iron deposits. You'll see that pretty clear in the scarring issues.                              And they'll tell you, too. I mean, the patients, they know. "Oh, yeah. I'm very hyperflexible." So you just ask the question and the patients will tell you. They say yes, and then it might need further testing physically; that is, actually do the exam and see if they have, on this Beighton scale, what the hyperflexibility score looks like. And if it is positive ... what we consider positive is five and above, five out of nine ... then those would candidates for a comprehensive DNA testing, whether it's related to cardiomyopathies, but I think connective tissue collection genes. Like I say, there's roughly 70 of these genes out there now that we test for in the commercial clinical laboratory setting. That should be monitored, as well as adding other genes if need be. So I'd encourage that.                              Physical examination number one. If it's positive, then check into a DNA panel for these types of disorders. It could help long-term for the care and outcome of the patient. Dr. Ferguson:    Yeah. I do think that's really important from the patient perspective and then, if more of these cases start being reported, I think it's very interesting also from the research perspective to find out what are the mechanisms that are potentially linking these mutations to cardiac disorders which have- [crosstalk] Dr. Butler:          That's true, and also realize that a lot of these patients have hypotension, and that can lead to some complications before, during and after surgical intervention, too. Dr. Ferguson:    Yeah. Dr. Butler:          So that's important to realize. Dr. Ferguson:    Very important. Yeah. So thank you for telling us about this interesting case and for raising this. I think it's an important issue and I'm sure a lot of the cardiologists and clinicians listening will start to look out for connective tissue disorders in their own patients. Dr. Butler:          I think, first thing is just ask questions. Are you hyperflexible? And they'll tell you. It's something that is very obvious to the patient. It will be obvious to the physician once he or she puts their hands on the patient, examine the patient, they realize, "Oh, this patient really is quite hyperflexible, digits and arms and knees and elbows," et cetera, et cetera. But, just ask the question, are they hyperflexible? If they say no, then the connective tissue is lower. It still could be. There could still be some aneurysms, those kind of things going on because there's, like I say, there's 70 genes, and there's six types of Ehlers-Danlos, so there's many other conditions out there that kind of look like an Ehlers-Danlos, but they're not. They may have another gene involving protein that's related to connective tissue, but not in the Ehlers-Danlos group of disorders or genes. Still could play a role. Could be similar. They may [inaudible] aneurysms, and that's important to know before they get into the procedures, too. Dr. Ferguson:    Yeah, really important, really interesting. Thank you so much for joining us.                              Thanks, everyone, for listening. And I wish you all the best for the holiday season, and a very happy new year. We're looking forward to bringing you lots more in 2019.                              This podcast was brought to you by Circulation: Genomic and Precision Medicine, and the American Heart Association Council on Genomic and Precision Medicine. This program is copyright American Heart Association, 2018.  

Jane Ferguson:                Hello everyone, and happy new year. Welcome to episode 24 of Getting Personal: Omics of the Heart. It's January 2019, I am Jane Ferguson, an assistant professor at Vanderbilt University Medical Center and an associate editor at Circulation Genomic and Precision Medicine. We have a great line-up of papers this month in the journal, so let's jump right into the articles.                                            First up, a paper from Christopher Nelson, Nilesh Samani, and colleagues from the University of Lester entitled, "Genetic Assessment of Potential Long-Term On-Target Side Effects of PCSK9 Inhibitors."                                            I think most listeners are well aware of the efficacy of PCSK9 inhibition in reducing cardiovascular risk. However, as a relatively new treatment option, we do not yet have data on potential long-term side effects of PCSK9 inhibition. In this study, they utilized genetics as a proxy to understand potential long-term consequences of lower PCSK9 activity. They examined a PCSK9 variant that associates with lower LDL, as well as examining two LDL-lowering variants in HMGCR, the target of statins, which served as a positive control of sorts. They used data from over 479,000 individuals in the UK Biobank and looked for associations between the three LDL-lowering variants and 80 different phenotypes.                                            For the PCSK9 variant, the allele which is associated with lower LDL was significantly associated with the higher risk of type 2 diabetes, higher BMI, higher waist circumference, higher waist-hip ratio, higher diastolic blood pressure, as well as increased risk of type 2 diabetes and insulin use. The HMGCR variants were similarly associated with type 2 diabetes as expected.                                            Mediation analysis suggested that the effect of the PCSK9 variant on type 2 diabetes is independent of its effect on obesity. There were nominal associations between the PCSK9 variant and other diseases, including depression, asthma, chronic kidney disease, venous thromboembolism, and peptic ulcer.                                            While genetics cannot fully recapitulate the information that would be gained from long-term clinical follow up, these data suggest that like statins, PCSK9 inhibition may increase the risk of diabetes and potentially other disease. Overall, the cardiovascular efficacy of PCSK9 inhibition may outweigh these other risks, however, future studies should carefully examine these potential side effects.                                            Next up, we have a paper from Xiao Cui, Fang Qin, Xinping Tian, Jun Cai, and colleagues from Peking Uni and Medical College, on "Novel Biomarkers for the Precise Diagnoses and Activity Classification of Takayasu's Arteritis."                                            They were interested in identifying protein biomarkers of Takayasu arteritis, to improve diagnosis and understanding of disease activity in this chronic vascular disease. They ran a proteomic panel including 440 cytokines on 90 individuals, including individuals with active disease, inactive disease, and healthy controls. They found a number of candidates and validated one protein, TIMP-1, as a specific diagnostic biomarker for Takayasu arteritis. For assessing disease activity, there was no single biomarker that could be used for classification, however, the combination of eight different cytokines identified through random forest-based recursive feature elimination and [inaudible] regression, including CA 125, FLRG, IGFBP-2, CA15-3, GROa, LYVE-1, ULBP-2, and CD 99, were able to accurately discriminate disease activity versus inactivity.                                            Overall, this study was able to identify novel biomarkers that could be used for improved diagnosis and assessment of Takayasu arteritis, and may give some clues as to the mechanisms of pathogenesis.                                            Our next paper is entitled, "Familial Sinus Node Disease Caused By Gain of GIRK Channel Function," and comes from Johanna Kuß, Birgit Stallmeyer, Marie-Cécile Kienitz, and Eric Schulze-Bahr, from University Hospital Münster. They were interested in understanding novel genetic underpinnings of inherited sinus node dysfunction.                                            A recent study identified a gain of function mutation in GNB2 associated with sinus node disease. This mutation led to enhanced activation of the G-protein activated inwardly rectifying potassium channel, or GIRK, prompting the researchers to focus their interest on the genes encoding the GIRK subunits, KCNJ3 and KCNJ5. They sequenced both genes in 52 patients with idiopathic sinus node disease, and then carried out whole exome sequencing in family members of patients with potential disease variants in either gene. They identified a non-synonymous variant in KCNJ5, which was not present in the EVS or ExAC databases, and which segregated with disease in the affected family. This variant was associated with increased GIRK currents in a cell system, and in silico models, predicted the variant altered or spermine binding site within the GIRK channel. Thus, this study demonstrated that a gain of function mutation in a GIRK channel subunit associates with sinus node disease, and suggests that modulation of GIRK channels may be a viable therapeutic target for cardiac pacemaking.                                            Our next paper, "Key Value of RNA Analysis of MYBPC3 Splice-Site Variants in Hypertrophic Cardiomyopathy," comes from Emma Singer, Richard Bagnall, and colleagues from the Centenary Institute and the University of Sydney.                                            They wanted to understand the impact of variants in MYBCP3, a known hypertrophic cardiomyopathy gene, on splicing. They recruited individuals with a clinical diagnosis of hypertrophic cardiomyopathy and genetic testing of cardiomyopathy-related genes. They further examined individuals with a variant in MYBCP3 which had an in silico prediction to affect splicing. They sequenced RNA from blood or from fixed myocardial tissue and assessed the relationship between each DNA variant and gene splicing variation.                                            Of 557 subjects, 10% carried rare splice site variants. Of 29 potential variants identified, they examined 9 which were predicted to affect splicing, and found that 7 of these were indeed associated with splicing errors.                                            Going back to the families, they were able to reclassify four variants in four families from uncertain clinical significance to likely pathogenic, demonstrating the utility of using RNA analysis to understand pathogenicity in genetic testing.                                            The next paper this issue comes from Catriona Syme, Jean Shin, Zdenka Pausova, and colleagues from the University of Toronto, and is entitled, "Epigenetic Loci of Blood Pressure: Underlying Hemodynamics in Adolescents and Adults."                                            A recent large meta epigenome-wide association study identified methylation loci that associate with blood pressure. In this study, they wanted to understand more about how these loci related to blood pressure and hemodynamics. They recruited adolescents and middle-aged adults and assessed 13 CPG loci for associations with hemodynamic markers, including systolic and diastolic blood pressure, heart rate, stroke volume, and total peripheral resistance, measured over almost an hour during normal activities. Several of the loci replicated associations with blood pressure, and two of these also showed age-specific associations with hemodynamic variables. One site in PHGDH was particularly associated with blood pressure and stroke volume in adolescents, as well as with body weight and BMI, where lower methylation resulting in higher gene expression associated with higher blood pressure. A second site in SLC7A11 associated with blood pressure in adults but not adolescents, with lower methylation and consequent higher gene expression associated with increased blood pressure.                                            Overall, this study indicates that methylation mediated changes in gene expression may modulate blood pressure and hemodynamic responses in an age-dependent manner.                                            Next up is a research letter from Ben Brumpton, Cristen Willer, George Davey Smith, Bjørn Olav Åsvold, and colleagues from the Norwegian University of Science and Technology, entitled, "Variation in Serum PCSK9, Cardiovascular Disease Risk, and an Investigation of Potential Unanticipated Effects of PCSK9 Inhibition: A GWAS and Mendelian Randomization Study in the Nord-Trøndelag Health Study, Norway."                                            As we heard about from the first study this issue, the long-term side effects of PCSK9 inhibition remain unknown. In this study, they also applied a genetic approach to understand potential unanticipated consequences of PCSK9 inhibition. They analyzed phenotypes from over 69,000 participants in the Nord-Trøndelag Health Study and measured serum PCSK9 in a subset.                                            In PCSK9 GWAS of over 3,600 people, with replication in over 5,000 individuals from the twin gene study. They defined a genetic risk score for serum PCSK9 and assessed the relationship between genetically predicted PCSK9 and outcomes. They saw the expected associations between lower PCSK9 and lower LDL and coronary heart disease risk. However, there was minimal evidence for associations with other outcomes.                                            While our first study in this issue, from Nelson, et al, found that lower PCSK9 from a single genetic variant was associated with higher diabetes risk, this risk was not found here using the genetic risk score. Differences in the genetic definitions and in the populations used can perhaps explain these differences between the two studies, but overall, the studies are consistent in suggesting that long-term PCSK9 inhibition is unlikely to be associated with major adverse outcomes.                                            Our second research letter comes from Young-Chang Kwon, Bo Kyung Sim, Jong-Keuk Lee, and colleagues from Asan Medical Center in Seoul, on behalf of the Korean Kawasaki Disease Genetics Consortium. The title is, "HLA-B54:01 is Associated with Susceptibility to Kawasaki Disease," and reports on novel Kawasaki disease variants.                                            HLA genes have been previously associated with disease, and in this report, the authors sequenced selected axons in HLA-DRB1, HLA-DQB1, HLA-A, HLA-B, HLA-C, and HLA-DBP1 in 160 Kawasaki disease patients and 278 controls. They find a significant association with HLA-B, and replicated this in a sample of 618 Kawasaki disease patients, compared with over 14,000 in-house controls. They identified specific amino acid residues conferring disease susceptibility, highlighting HLA-B as a potential modulator of Kawasaki disease.                                            Our third and final research letter concerns "Serum Magnesium and Calcium Levels and Risk of Atrial Fibrillation: a Mendelian Randomization Study," and comes to us from Susanna Larsson, Nikola Drca, and Karl Michaëlsson, from the Karolinska Institute.                                            Because magnesium and calcium are known to influence atrial fibrillation, this group was interested in whether genetic predictors of serum methyls associated with disease. They constructed genetic predictors from GWAS of calcium in over 61,000 individuals, and GWAS of magnesium in over 23,000 individuals. They applied these predictors to an AF GWAS including over 65,000 cases and over 522,000 controls. Genetically predicted magnesium was inversely associated with atrial fibrillation, while there was no association with genetically predicted calcium.                                            While this study does not definitively prove causality, future studies aimed at assessing whether dietary or other strategies to raise serum magnesium are protective against AF may yield novel strategies for disease prevention.                                            And that's it from us for this month. Thank you for listening, and come back next month for more from Circulation Genomic and Precision Medicine.                                            This podcast was brought to you by Circulation Genomic and Precision Medicine and the American Heart Association Council on Genomic and Precision Medicine. This program is copyright American Heart Association, 2019.  

Jane Ferguson:                Hello, welcome to Getting Personal: Omics of the Heart, Episode 22. This is a podcast from Circulation: Genomic and Precision Medicine, and the AHA Council on Genomic and Precision Medicine. I am Jane Ferguson and it's November 2018.                                            Our first article comes from Carlos Vanoye, Alfred George and colleagues from Northwestern University Feinberg School of Medicine and is entitled, High Throughput Functional Evaluation of KCNQ1 Decrypts Variance of Unknown Significance.                                            So a major growing problem in clinical genomics is that following the identification of a variant that is potentially linked to a disease phenotype, without further interrogation, it's really hard to make sense of the functional significance of that variant. Right now, the large number of variants of unknown significance lead to confusion for patients and clinicians alike. To allow for accurate diagnoses and the best treatment plans, we need a way to be able to screen variants to assess their function in a fast and cost-effective manner.                                            In this paper, the authors decided to focus in the KCNQ1 gene, a cardiac ion channel, which can affect arrhythmias. They aim to assess whether a novel high-throughput functional evaluation strategy could identify functional mutations, as well as an in vitro electrophysiological approach. Which is effective, but expensive and time-consuming. Their approach capitalized on an existing automated electrophysiological recording platform that had originally had been developed for drug discovery essays.                                            They selected 78 variants in KCNQ1 and assessed their function using the High-Throughput platform, which coupled high efficiency, cell electroporation with automated plain or patch clamp recording. They compared the results to traditional electrophysiological essays and find a high rate of concordance between the two methods. Overall, they were able to reclassify over 65% of the variants tested, with far greater efficiency than traditional methods.                                            While this method will not work for all genes and phenotypes, the authors have demonstrated an efficient method for functional interrogation of variants. Which may greatly accelerate discovery and conditions such as Long QT or other congenital arrhythmias.                                            The next paper, Nocturnal Atrial Fibrillation Caused by Mutations in KCND2 Encoding Poor Forming Alpha Subunit of the Cardiac KV 4.2 Potassium Channel, comes from Max Drabkin, Ohad Birk, and colleagues at Soroka University Medical Center in Israel. This paper also focuses on cardiac ion channels and the role of mutations in atrial fibrillation.                                            In a family with early-onset peroxisomal AF across three generations, whole XM sequencing revealed a variant in KCND2 encoding the KV 4.2 Potassium Channel, which segregated consistent with autosomal dominant heredity. This variant resulted in a replacement of a conserved [inaudible] residue with an arginine. To investigate functional consequences of this novel variant, they conducted experiments in xenopos laevis oocytes and found that there is decreased voltage depended channel and activation and impaired formation of the KV 4.2 Homotetramer and the KV 4.2, KV 4.3 Heterotetramer.                                            Overall, this study shows that a novel mutation in a conserved Protein kinase C Phosphorylation site within the KV 4.2 Potassium Channel underlies the phenotypes observed in a family of peroxisomal atrial fibrillation. The targeting Atrial KV 4.2 might be an effective therapeutic avenue.                                            Next up, Michael Levin and Scott Damrauer and colleagues from the University of Pennsylvania published an article entitled, Genomic Risks Stratification Predicts All-Cause Mortality After Cardiac Catheterization.                                            They were interested in understanding the utility of polygenic risk scores for disease prediction. They constructed a genome Y genetic risk score for CAD and applied it to individuals from the Penn Medicine Bio-bank who had undergone Coronary angiography and genotyping.                                            They included over 139,000 variants for the 1,500 ancestry subjects who were included and classified them as high or low polygenic risk. Individuals who were classified as high polygenic risk were shown to have higher risk of All-Cause mortality than low polygenic risk individuals despite no differences in traditional risk factor profiles. This was particularly evident in individuals with high genetic risk but no evidence of angiographic CAD.                                            Adding the polygenic risk score to a traditional risk assessment model was able to improve prediction of five year All-Cause mortality. Highlighting the utility of a polygenic score and underscoring traditional risk factors do not yet fully capture mortality risk.                                            The next article entitled, "Bio-marker Glycoprotein Acetyls is Associated with the Risk of A Wide Spectrum of Incident Diseases and Stratifies Mortality Risk in Angiography Patients" comes from Johannes Kettunen, Scott Ritchie, Peter Würtz and colleagues from the University of Oulu Finland.                                            GlycA is a circulating biomarker that reflects the amount of Glycated proteins in the circulation. It has been associated with cardiovascular disease, Type 2 Diabetes, and all-cause mortality. In this paper, the authors used electronic health record data from over 11,000 adults from the finish general population previously included in the "FINRISK" and "Dilgom" studies and they tested for a associations between GlycA and 468 different health outcomes over an 8-12 year follow up. They report new associations between GlycA and multiple conditions including incident alcoholic liver disease, chronic renal failure, glomerular diseases, chronic obstructive pulmonary disease, inflammatory polyarthric disease and hypertension.                                            These associations held true even after adjusting for CRP suggesting that GlycA represents an independent biological contributor to inflammation and disease. Their findings highlight potential utility for GlycA as a biomarker of many diseases and underscore the importance future functional and mechanistic studies to understand how GlycA is linked to disease risk.                                            Our last original research article entitled, "Tissue Specific Differential Expression of Novel Jeans and Long Intergenic Non-coding RNAs in Humans with Extreme Response to Endotoxic glycemia comes from Jane Ferguson, Murdock Riley, and colleagues from Vanderbilt University, Columbia University, and the University of Pennsylvania. That first author is none other than me, so I'm not unbiased reader of this particular manuscript, but I'd like to tell you a little bit about it anyway.                                            We were interested in understanding the transcriptional changes that occur in tissues during acute inflammation. As part of the genetics of evoked responses to Niacin and Endotoxemia, or gene study, we recruited healthy individuals and performed an inpatient endotoxin challenge where we administered a low dose of LPS and looked at the systemic inflammatory response. Individuals vary greatly in the degree of their inflammatory response to LPS and we identified high and low responders, men and women, of African and European ancestry, who had responses in the top or bottom 10% for cytokines and fever.                                            We conducted RNA seek and adipose tissue in 25 individuals and CD-14 positive monosites for 15 individuals in pre and two or four hours post LPS samples. We found that the differences in transcriptional response between high or low responders are mostly explained by magnitude rather than discrete sets of genes.                                            So some core genes were altered similarly, in both groups, but overall the high responders mounted a large transcription of response to LPS or low responders rather than mounting an anti-inflammatory response actually just barely responded on the transcription level. We saw clear tissue specificity between manosites and adipose tissue we identified several long non-coding RNAs that were up or down regulated in response to LPS and validated these independent samples one of these link RNAs which we have now named Monosite LPs induced link RNA regulator vile six or Mahler Isle six, with highly regulated by LPs and monosites but not in adipose tissue.                                            We [inaudible] THP-1 monosites and find a significant effect on iOS six expression suggesting that this is a novel link RNA that regulates Isle six expression in manosites potentially through a cd-86 dependent pathway. Overall our data revealed tissue specific transcriptional of changes that correlate with clinical inflammatory responses and highlight the role of specifically incarnate and inflammatory response.                                            Next up is a research letter entitled "Reduced Sodium Current in Native Cardiomyocytes of a Regatta Syndrome Patient Associated with Beta Two Central Mutation" published by Constance Schmidt, Felix Wiedmann, Ibrahim El-Battrawy, Dierk Thomas, and co-authors from University Hospital Heidelberg. They obtained cardiomyocytes from a patient with Regatta Syndrome previous whole XM sequencing had implicated a variant in the Beta Two Syntrophin or "SNTB2" gene as potentially causal in this individual. Expression analysis showed lower SNTB2 expression and atrial tissue of the affected individual compared with controls.                                            They performed electrophysiology on the Microcytes and found reduced peak sodium density and reduced late sodium current. They co-express wild type or mutant SNTB2 in heck 293 T cells and [inaudible] with the cardiac sodium channel NAV-1.5 and found a significant effect on binding which adversely affected sodium currents. This study nicely demonstrates the functional effect of this SNTB2 mutation underlying Regatta Syndrome in this patient.                                            A second research letter comes from A.T. van den Hoven and Jolien Roos- Hesselink and colleagues from Erasmus University Medical Center in the Netherlands and is entitled "Aortic Dimensions and Clinical Outcome in Patients with SMAD three mutations, they were interested in understanding how the Aortic dilation comment individuals with SMAD three mutations compared to individuals with other syndrome and causes of Aortic dilation.                                            In 28 patients with SMAD three mutations, there were significant growth in the Sinotubular Junction the ascending Aorta on the diaphragm over an average of 10 years of follow up at reads far higher population averages but lower than might be seen in other syndromes, such as [inaudible]. Intensive management and preventive surgery and many of the patients prevented any mortality in this group.                                            Rounding out this issue is a clinical letter entitled "Concealed Arrhythmogenic  Right Ventricular Cardiomyopathy in Sudden unexplained Cardiac Death events from Jodie Ingles, Chris Semsarian, and colleagues from the University of Sydney, Australia. They report on for clinical cases where individuals presented in early adulthood with unexplained cardiac arrest, which was later found to be attributable to mutations in the PKP2 gene. PKP2 or, Plakophilin 2, encodes an integral component of the Desmosome, which is important and Cell-Cell adhesion. Further PKP2 is involved in transcriptional activation of genes controlling intracellular calcium cycling. This gene has been implicated arrhythmogenic right ventricular cardiomyopathy in individuals with cardiac structural abnormalities. These four cases where unrelated individuals were all fans to have loss of function variants and PKP2 underlying sudden cardiac death or events, despite structurally normal hearts. This prompts questions on the clinical management of such cases of concealed ARVC.                                            That's all from us for November, thanks to all of you out there listening. We'll be back in December for the final episode of 2018.                                            This podcast was brought to you by Circulation Genomic and Precision Medicine and the American Heart Association Council on Genomic and Precision Medicine. This program is copyright American Heart Association 2018.                                             

Speaker 1:           Hi, everyone. Welcome to episode 21 of Getting Personal, Omics of the Heart from October 2018. I'm Jane Ferguson, an Assistant Professor at Vanderbilt University Medical Center and an Associate Editor at Circulation: Genomic and Precision Medicine. We have a great issue this month. So, let's dive straight in. First up, an article on "Loss-of-Function ABCC8 Mutations in Pulmonary Arterial Hypertension" from Michael Bohnen, Wendy Chung and colleagues from Columbia University. In pulmonary arterial hypertension, or PAH, compromised pulmonary arterial function can raise pressure in the pulmonary artery which leads to increased pulmonary vascular resistance. This ultimately results in right heart failure. While PAH is relatively rare, it has a high rate of mortality. Some genetic underpinnings have been identified, notably the KCNK3 gene identified by the same research group where they find that mutations result in potassium channelopathy. However, here the authors hypothesized that other genetic contributors also exist and that identification of these could highlight new therapeutic targets to improve treatment and outcomes in PAH. In their study, the authors performed exome sequencing for discovery of novel disease variants in 233 PAH patients, 99 of whom had pediatric-onset and 134 with adult-onset. They sequenced a replication sample of 680 individuals with adult-onset PAH. They found a de novo missense variant in the ABCC8 gene in one patient and then found 10 more ABCC8 variants in other unrelated patients in the discovery and replication samples. Half of these were novel mutations and all were located in conserved regions and predicted to be deleterious. They screened over 33,000 subjects from the Exome Aggregation Consortium and over 49,000 from the Regeneron-Geisinger DiscovEHR study and found significant overrepresentation on rare ABCC8 variants in the PAH cases compared with population controls. ABCC8 encodes sulfonylurea receptor ... part of the potassium ATP channel. The authors determined that it is expressed in lungs in both PAH and healthy individuals and is particularly localized to alveolar macrophages and proximal pulmonary arteries. They expressed eight of the newly discovered ABCC8 mutations in COS cells, which are a monkey-derived, fiberglass-like cell line and they assessed the effects on function. They used patch-clamp experiments to assess potassium ATP channel activity and recorded efflux rates of Rubidium-86. Every mutation was associated with impairments in one or both functional assays, suggesting that mutations in ABCC8 are responsible for PAH by a modulating potassium channel function and flux. An existing drug, Diazoxide, targets ABCC8 and has anti-hypertensive and insulin-lowering effects. The authors find that all mutants were pharmacologically activated by Diazoxide in the functional assays. Now, whether this drug would be safe or effective in PAH remains unknown, but these findings open up targeting of ABCC8 as a possible treatment in PaH and highlight the importance of potassium channels in PAH. Our next paper also used whole-exome sequencing for novel discovery. Marzia de Bortoli, Alessandra Rampazza and colleagues from the University of Padua in Italy published "Whole-Exome Sequencing Identifies Pathogenic Variants in TJP1 Gene Associated With Arrhythmogenic Cardiomyopathy". Arrhythmogenic Cardiomyopathy, or ACM, is one of the most common causes of sudden unexpected death in athletes and young people. It is known to be frequently caused by mutations in genes encoding mechanical junction proteins of the intercalated disks within the cardiac muscle. However, some individuals with ACM do not have any mutations in known genes. This research group was interested in finding novel causal gene mutation and they use whole-exome sequencing to identify mutations from a single patient in Italy. They used InSilica tools to screen for potentially damaging mutations which brought their list of candidate mutations down to 52 and this was topped by a novel mutation in the TJP1 gene which was predicted to be highly deleterious using various algorithms. Using Sanger sequencing, they found that this mutation was also present in several family members. A second mutation in TJP1, also predicted to be damaging, was identified in a second Italian family. They then screened a sample of 43 Dutch and German subjects diagnosed with ACM and found that, once again, mutations in TJP1 topped the list as predicted to be damaging. The TJP1, or tight junction protein 1, encodes the intercalated disk proteins ZO1. The identified mutations may affect folding and local interactions within the protein, affecting protein-protein interactions and gap junction organization. Well, within this paper, they were not able to fully disentangle the mechanisms linking these mutations to disease, given that the prevalence of TJP1 mutations in their ACM samples was almost 5%. Screening for TJP1 mutations in ACM cohorts may identify many additional affected subjects. Further research into TJP1 is needed to identify how these variants may cause ACM. If you want to read more about this paper, you can check out the accompanying editorial from Jason Roberts ... Western University, Ontario ... in this same issue. Next up is a paper from Natsuko Tamura, Yasuhiro Maejima, Mitsuaki Isobe and colleagues from Tokyo Medical and Dental University entitled "Single-nucleotide Polymorphism of the MLX Gene Is Associated With Takayasu Arteritis". Takayasu Arteritis, or TAK, is an autoimmune disease causing aortic vasculitis that is poorly understood and disproportionately affects young Asian women. In previous genome-wide associations, study of TAK in Japanese individuals conducted by this group, indicated SNPs in the MLX gene. In this paper, the authors aim to identify mechanisms linking MLX mutations with TAK. The top GWAS SNP rs665268 is a missense mutation causing L-Glutamine Arginine substitution in the DNA binding site of MLX. They found that this SNP was associated with severity in disease in TAK. With additional copies of the risk alleles associated with more severe aortic regurgitation and greater number arterial lesions. In mice, the highest expression of MLX was found in the aortic valves. Using crystallography, they found that the missense mutation likely stabilizes a complex formed between MLX and MondoA. Immunoprecipitation experiments confirmed that the missense mutation was associated with enhanced MLX MondoA heterodimer formation and MLX transcriptional activity. This resulted in upregulation of TXNIP and higher TXNIP expression is associated with increased intracellular oxidative stress and the authors found for increased oxidative stress in cells carrying the MLX mutation. Further, additional cell experiments showed evidence of this MLX mutation reduces autophagy and stimulates inflammasome activation. Overall, through a series of really elegant experiments, the authors demonstrate that a missense mutation in MLX leads to inflammasome activation and accumulation of cells within the aorta, potentially underlying the pathophysiology seen in TAK patients and highlighting novel causal pathways that may be probed therapeutically.regular Our next paper from Danxin Wang, Wolfgang Sadee and colleagues from the University of Florida and The Ohio State University, also delves into the functional impact of disease-associated SNPs. In their paper, "Interactions Between Regulatory Variants in CYP7A1 Promoter and Enhancer Regions Regulate CYP7A1 Expression", they used a series of experiments to demonstrate how SNPs in CYP7A1 ... which have been associated with cholesterol and cardiovascular disease ... are related to gene function. CYP7A1 is a gene which coordinates a key pathway for cholesterol removal from the body because it encodes an enzyme which is rate-limiting for bioassay synthesis from cholesterol. Although several SNPs in the gene have been associated with cardiovascular phenotypes, the reported effects on gene function have been inconsistent and/or unclear. Because of the linkage disequilibrium between SNPs, it has been hard to understand which SNP or SNPs are actually functional. What this team set out to do was to systematically screen functionality of individual CYP7A1 SNPs to understand the independent effects of each functional variant. First, they used chromatin conformation capture, or 4C assay, to identify regions that associated with a CYP7A1 promoter. They found three distinct regions with evidence of enhancer function and [phonetic 00:09:05] active A>G regulation. They, next, used CRISPR Cas9 to delete each of the three regions in HepG2 cells and assess effects on CYP7A1 expression. One region had no effect, while one led to increased expression and one led to decreased expression ... thus, identifying the presence of both enhancer and repressor regions. Using reporter gene assays, they confirmed the effects seen in CRISPR experiments. Based on reported SNP associations, they narrowed down candidate functional SNPs within the regions and constructed reporter assays containing haplotypes of potential functional SNPs. They were able to identify two SNPs acting together to determine differences in CYP7A1 gene expression. Because these SNPs are in LD, but the minor alleles have effects in opposite directions, considering genotype at both SNPs is required to understand the effects on gene expression. This explains why previous studies found inconsistent results. Both during the functional experiments, they went to human samples and they assessed the combined effect of the two SNPs on clinical phenotypes. Designating people as high or low activity based on the two SNPs, they found significant differences in cholesterol and in the likelihood to reach cholesterol targets on statin, as well as in the risk of MI. This paper is a lovely example of how careful functional interrogation can tease out a complex problem and I think it highlights how much more of this type of work needs to be done for the many other genomic regions with confusing or discord in associations. The last full-length article concerns the "Effect of Ascertainment Bias on Estimates of Patient Mortality in Inherited Cardiac Diseases" and comes from Eline Nannenberg, Imke Christiaans and colleagues at the Academic Medical Center, Amsterdam. They were interested in how much ascertainment bias and the tendency to publish findings from more severe disease cases affects the mortality estimates that are used to guide clinicians and genetic counselors when helping patients understand their disease prognosis. They revisited three inherited cardiac diseases including idiopathic ventricular fibrillation associated with a mutation in DPP6, SCN5A overlap syndrome associated with SCN5A mutations, and Arrhythmogenic Cardiomyopathy caused by a founder PLN mutation. They analyzed mortality over 2-10 years of clinical screening and cascade screening and found that the median age of survival quickly increased in all three conditions. In many cases, the reason that a mutation was identified was because of severe disease in that patient or family, but as the authors highlight here, this can bias publications towards associating the variant with more severe phenotypes and higher mortality. Following up the initial findings with additional screening and tracking of affected individuals is important to subsequently give a more accurate estimation of the effect of the mutation which can be used to inform treatment plans. Moving on to this month's research letters, Catherine Hajek, Jerome Rotter and colleagues from LA BioMed and the University of South Dakota, published the results of their study, "A Coronary Heart Disease Genetic Risk Score Predicts Cardiovascular Disease Risk in Men, Not Women: The Multi-Ethnic Study of Atherosclerosis". The genetic risk scores are being increasingly applied to estimate disease risk in individuals. However, these scores are based on the GWAS discovery from specific populations which have often been disproportionately male and with individuals of European ancestry. In this letter, the authors wanted to understand whether coronary heart disease genetic risk scores performed the same in men and women of European ancestry. Using data from the MESA Study, they applied a 46 locus genetic risk score to over 2500 individuals. In men, this risk score was strongly associated with event rates. However, in women, there was no association. Given the known differences in disease pathophysiology and manifestation between men and women, this finding additionally highlights the need to conduct genetic studies in underrepresented groups so that we can design scores that accurately predict risk within specific groups. Our next letter comes from Xiao Wang and Kiran Musunru at the University of Pennsylvania ... "Confirmation of Causal rs9349379- PHACTR1 Expression Quantitative Trait Locus in iPSC Endothelial Cells". They were interested in understanding the affect of a coronary disease SNP in the PHACTR1 gene on gene expression. Previous efforts to investigate this had yielded conflicting results showing either a significant eQTL effect for PHACTR1 and vascular tissue or no effect on PHACTR1, but an effect on a distal gene EDN1 in endothelial cells. For this study, the authors used CRISPR Cas9 to introduce the SNP to iPS cells and then expanded isogenic lines at the major and minor allele homozygous and differentiated these into endothelial cells. They find that the major allele was associated with significantly higher factorial expression, but no difference in EDN1 expression. Thus, based on these experiments, it appears that PHACTR1 may indeed be the causal gene in that region underlying the GWAS signal and whether or not EDN1 is involved remains unclear. Our next letter is a clinical letter from Nosheen Raza, Anjali Owens and co-authors at the University of Pennsylvania. They report on "ACTA1 Novel Likely Pathogenic Variant in a Family With Dilated Cardiomyopathy". In this case report, they describe that the discovery of a mutation in ACTA1 in a family with dilated cardiomyopathy, but no skeletal muscle symptoms. As a gene that is predominantly expressed in skeletal muscle, ACTA1 mutations have previously been associated with skeletal muscle myopathies and would not have been expected to cause cardiac symptoms in the absence of skeletal muscle dysfunction. However, sequencing suggests that this variant is a causal mutation in this family, highlighting the need to consider potential mechanisms for cardiac muscle specifics of highly expressed skeletal muscle genes. Our second clinical letter comes from Laura Zahavich, Seema Mital and co-authors from the Hospital for Sick Children in Ontario. They report a "Novel Association of a De Novo CALM2 Mutation With Long QT Syndrome and Hypertrophic Cardiomyopathy". They report finding mutation in the calcium transporter CALM2 gene in the child who presented with hypertrophic cardiomyopathy and ultimately died from sudden cardiac death. While this patient also had some variants of un-insignificance, the CALM2 gene is highly conserved and mutations are likely to be pathogenic. The CALM2 is not on all of the clinical genetic testing panels and in this case, whole-exome sequencing was required to identify a mutation. CALM2 have been described in other individuals and together with the findings reported here, there's compelling evidence for inclusion of CALM2 on cardiomyopathy in clinical testing panels. This issue also contains a perspective article from Michael Mackley, Elizabeth Ormondroyd and colleagues from the University of Oxford entitled "From Genotype to Phenotype: Clinical Assessment and Participant Perspective of a Secondary Genomic Finding Associated with Long QT Syndrome". They describe some of the challenges arising from more widespread genetic testing including how to deal with incidental findings. A larger number of people including apparently healthy individuals are receiving sequencing results that highlight potential disease-related mutations, but with varying penetrance and uncertain effects. This perspective paper highlights the issues through case study and discusses future directions and challenges in this rapidly growing area. Finally, we ride out this issue with an AHA scientific statement on "Cardiovascular Health in Turner Syndrome: A Scientific Statement From the American Heart Association" led by Michael Silberbach and Jolien Roos-Hesselink and a group of co-authors representing the American Heart Association Council on Cardiovascular Disease in the Young; Council on Genomic and Precision Medicine; and Council on Peripheral Vascular Disease. In this statement, they discuss the cardiovascular complications that commonly occur in girls and women Turner syndrome. Cardiovascular disease contributes significantly to premature death in individuals with Turner syndrome. Because of the unique nature of the cardiac presentations in Turner syndrome, better clinical guidelines are needed to improve diagnosis and treatment of [phonetic 00:17:26] ischemia in these individuals. This statement takes a first step to outline suggestions to improve clinical practice and highlights the work that still remains to be done to inform disease management. That rounds out the October issue of Circulation: Genomic and Precision Medicine. Thanks for listening! You can go online to ahajournals.org/journal/circgen to access the latest issue and browse previous issues. As a last reminder, AHA Sessions is approaching fast and I hope to see many of you in Chicago, November 10-12. This podcast was brought to you by Circulation: Genomic and Precision Medicine and the American Heart Association Council on Genomic and Precision Medicine. This program is Copyright American Heart Association, 2018.  

Jane Ferguson: Hi everyone. Welcome to episode 20 of Getting Personal Omics of the Heart, the podcast brought to you by the Circulation: Genomic and Precision Medicine Journal and the American Heart Association Council on Genomic and Precision Medicine. I'm Jane Ferguson from Vanderbilt University. It's September 2018 and let's dive straight into the papers from this month's issue of Circulation: Genomic and Precision Medicine.                                 We're starting off with some pharmacogenomics. Bruce Peyser, Deepak Voora and colleagues from Duke University published an article entitled, "Effects of Delivering SLCO1B1 Pharmacogenetic Information in Randomized Trial and Observational Settings."                                 Although statins are generally well tolerated, 5-15% of patients taking statins for LDL lowering and cardiovascular protection end up developing statin associated muscular symptoms. Because onset of muscular symptoms associated with discontinuing statin use, as well as increased cardiovascular morbidity, there is a clear need to identify ways to prevent or reduce symptoms in these people. Variants affecting statin related myopathy have previously been discovered through GWAS, including a variant in the SLCO1B1 gene, which also has been shown to relate to statin myalgia and discontinuation of statin use. The risks appear to be greatest with simvastatin, indicating the people at risk of muscle complications may do better on either low-dose Simvastatin or another statin. However, there's still some uncertainty surrounding the risks and benefits of various statins as they pertain to risk of muscular symptoms.                                 The authors have previously shown that pharmacogenetics testing led to increased number of people reporting statin use, but effects of pharmacogenetic testing on adherence, prescribing, and LDL cholesterol had never been tested in a randomized control trial. In this study, they randomized 159 participants to either genotype informed statin therapy or usual care, and then followed them for months to eight months.                                 25% of participants were carriers of the SLCO1B1 star five genotype. The authors found that statin adherence was similar in both groups, but gene type guided therapy resulted in more new statin prescriptions and significantly lower LDL cholesterol at three months, and levels that were lower but no longer significantly different at eight months.                                 In individual's randomized to usual care who then crossed over to genotype informed therapy after the trial period ended, there was an additional decrease in LDL cholesterol. Overall, genotype informed statin therapy led to an increase in re-initiation of statins and decreases in LDL cholesterol, but did not appear to affect adherence.                                 The authors also examined the effects of commercial genetic testing for SLCO1B1 variants in an observational setting by looking at over 92000 individuals with data available in the EHR. They found the people who receive genetic testing results had a larger drop in LDL cholesterol compared to untested controls. Overall, the study indicates that carriers of the SLCO1B1 risk variant may benefit from genotype informed statin therapy, while for non-carriers receiving their results may has limited effects.                                 If you want to read more on this, Sony Tuteja and Dan Rader from UPenn wrote an editorial to accompany this article, which was published in the same issue.                                 We're staying on the topic of statins and LDL for our next paper. This article comes from Akinyemi Oni-Orisan, and Neil Risch and colleagues from the University of California and is entitled, "Characterization of Statin Low-Density Lipoprotein Cholesterol Dose-Response Utilizing Electronic Health Records in a Large Population-Based Cohort."                                 They were interested in understanding what determines variation in statin induced LDL reduction, particularly the genetic component, and they used a large EHR derived data set, the Kaiser Permanente Genetic Epidemiology research on adult health and aging cohort to address this important question. An EHR dataset does have intrinsic limitations, but also has some clear strengths, not only as a readily available and cost-effective data source for large sample sizes, but also because it reflects real world clinical care in diverse individuals, which is not always well represented within the selective constraints of a randomized trial.                                 There were over 33000 individuals who met their inclusion criteria. To account for differences in potency between different statins and doses, the authors generated a defined daily dose value, with one defined daily dose equal to 40 milligrams per day of Lovastatin. The slope of the dose response was similar across statin types and across different sex and race or ethnicity groups. But there were differences by statin type in the response independent of dose, as well as differences in absolute responses by sex, age, race, smoking, and diabetes.                                 Based on these differences, the authors revised the defined daily doses and they highlight how previously defined equivalencies between different statins may not be accurate. They found that individuals with East Asian ancestry had an enhanced response to therapy compared with individuals of European ancestry.                                 The authors identified related individuals within the data set and the estimated heritability of statin response using parent-offspring and sibling pairs. They found only modest heritability, indicating that non-genetic factors may be more important in determining variability in statin response. Overall, this large single cohort study adds to our knowledge on determinants of statin response and raises further questions on the relative effects of different statins and doses within patient subgroups.                                 Okay, so now let's talk about GWAS and Athero. Sander van der Lann, Paul de Bakker, Gerard Pasterkamp and coauthors from University Medical Center Utrecht published a paper entitled, "Genetic Susceptibility Loci for Cardiovascular Disease and Their Impact on Atherosclerotic Plaques."                                 Over the past decade, genome-wide association studies in large cohorts have been very successful in identifying cardiovascular risk loci. However, relating these to subclinical disease or two mechanisms has been more challenging. The authors were interested in understanding whether established GWAS loci for stroke and coronary disease are associated with characteristics of atherosclerotic plaque, the idea being that some of the risk loci may alter disease risk by determining the development and stability of plaque. They identified seven plaque characteristics to study and histological samples, including intraplaque fat, collagen content, smooth muscle cell percentage, macrophage percentage, calcification, intraplaque hemorrhage, and intraplaque vessel density.                                 They selected 61 known loci and examined association of those SNiPA with black phenotypes in over 1400 specimens from the athero express biobank study. Out of the 61 loci, 21 were associated with some black phenotype compared with zero of five negative control loci, which were chosen as established GWAS loci for bipolar disorder, which, presumably, should share limited mechanistic etiology with plaque. They used the software package VEGAS to run gene-based analyses. They also assessed SNiPA relationships with gene expression and methylation in multiple tissues derived from two independence Swedish biobanks, which included atherosclerotic arterial wall, internal mammary artery, liver, subcutaneous fat, skeletal muscle, visceral fat, and fasting whole blood.                                 One CAD locus on chromosome 7q22 that survived correction for multiple testing was associated with intraplaque fat, and was also an EQTL for expression of several genes across multiple tissues. In addition, it was also a methylation QTL.                                 The authors focused on this locus and looked at correlation of expression within the LDL receptor and noted associations with HDL and LDL cholesterol in the global lipids genetics contortion data, which suggests that this locus may have a role in the metabolism. At this locus, the HBP1 gene expressed foam cells may be an interesting candidate as a causal gene in determining plaque-lipid accumulation and cardiovascular risk.                                 So next up, we have a paper that is also about athero and is coauthored by many of the same group as did that previous study. So yeah, this group's productivity is kind of making the rest of us look bad this month. So Martin Siemelink, Sander van de Lann, and Gerard Pasterkamp and their colleagues published, "Smoking is Associated to DNA Methylation in Atherosclerotic Carotid Lesions."                                 Okay. So I think one of the few things we can all definitely agree on is that smoking is bad. But, does smoking exert any of its cardiovascular damage by altering within atherosclerotic plaques? That's the question this group set out to answer.                                 They carried out a two-stage epigenome-wide association study, or EWAS, with discovery and replication of differentially methylated loci with tobacco smoking within carotid arteriosclerotic plaques of a total of 664 patients undergoing carotid endarterectomy and enrolled in the arthero-expressed biobanks study. In discovery, they found 10 CpG loci within six genes that associated with smoking. Four of the CpG loci replicated. These four loci mapping within six genes showed reduced methylation in current smokers compared with former or never smokers.                                 However, there was no difference in specific plaque characteristics based on methylation at any of the four loci. There was also no significant difference in plaque gene expression at these loci based on smoking status. However, a SNiPA at a nearby locus located in the 3' UTR of the PLEKHGB4 Gene was associated with methylation at AHRR, and was a [inaudible 00:09:58] QTL for PLEKHGB4 of expression but not a AHRR expression. The authors speculate that PLEKHGB4 may co-regulate AHRR expression. The authors also examined blood methylation in a subset of the same subjects, and they were able to replicate previously identified CPG sites associated with smoking.                                 This is a really complex area, and it's hard to identify mechanisms and causality from these multiple layers of data, but the authors demonstrate the importance of using disease relevant tissues to start to understand how environmental factors interact with genetics and other underlying physiology to modify methylation and function within the vasculature.                                 Our final full-length research paper this issue from Brian Byrd and colleagues Michigan, is actually the subject of our interview today. So I won't go into too much detail on it right now, but keep listening for an interview with Brian about their paper, "Human Urinary mRNA as a Biomarker of Cardiovascular Disease: A Proof-of-Principle Study of Sodium-Loading in Prehypertension."                                 Our review article this month is about the "Dawn of Epitranscriptomic Medicine" from Konstantinos Stellos from Newcastle University and Aikaterini Gatsiou from Goethe-Universität Frankfurt. In this paper, they're taking us to the next level beyond just RNA, but towards RNA epigenetics. Given the large number of possible modifications that can and are made to RNA during RNA name metabolism, there's huge potential to gain a new biological and mechanistic understanding by studying the RNA epitranscriptome. I think we'll ignore this at our peril. So if you need to catch up on this new field, this comprehensive review will get you right up to speed.                                 Moving on, our research letters are short format papers that allow authors to present focused results. These are also a great avenue to submit findings from replication studies that might not necessitate a full-length paper. So if you have some data from a replication study that you've been procrastinating writing up, a short research letter is a great format to consider.                                 This month, Bertrand Favre, Luca Borradori and coauthors from Bern University Hospital published a letter entitled, "Desmoplakin Gene Variants and Risk for Arrhythmogenic Cardiomyopathy: Usefulness of a Functional Biochemical Assay." The desmoplakin is essential for the cell-cell adhesion complex's desmosomes. Mutations in this gene have been associated with a wide range of phenotypes, including some in skin and hair, but also in heart, which can manifest arrhythmogenic or dilated cardiomyopathy. This protein anchors intermediate filaments, so mutations that alter binding to intermediate filaments may pathogenicity.                                 The author selected seven reported amino acid altering mutations in desmoplakin, and they screened for effects on binding using a novel fluorescence binding assay. They found that three of the seven mutations had a clear impact on binding. This assay is a novel way to assess functional impact of desmoplakin variants, and may be useful to inform the severity of future phenotypes in individuals carrying a desmoplakin mutation.                                 Finally, if you want to stay up-to-date on the genetics of aortic disease and Marfan syndrome, you can find a letter from Christian Groth and colleagues and an author response from Norifumi Takeda and colleagues regarding their previously published paper on impact of pathogenic FBN1 variant types on the progression of aortic disease in patients with Marfan syndrome.                                 I am joined today by Dr. Brian Byrd from the University of Michigan, who is the senior author on a Manuscript published in this month's issue, entitled, "Human Urinary mRNA as a Biomarker of Cardiovascular Disease: A Proof-of-Principle Study of Sodium-Loading in Prehypertension."                                 So welcome Brian. Thanks so much for coming on the podcast. Brian Byrd:          Thank you for having me. Jane Ferguson: So before we get started, could you give a brief introduction of yourself to the listeners and maybe tell us a little bit about how you got into the field? Brian Byrd:          Absolutely. So I am a cardiologist and a physician scientist. I'm an assistant professor at the University of Michigan, where I have a laboratory engaged in clinical investigation. My background is that I did my Internal Medicine Residency at Vanderbilt University. And after I finished residency, I entered Nancy Brown's lab. She's the Chair of Medicine at Vanderbilt, as I know you're aware. And she had a laboratory focused, and still does have a laboratory focused, on the investigation of high blood pressure, with a lot of focus on understanding high blood pressure as it occurs in humans. And I got a Master of Science degree in clinical investigation while I was in her lab, and we did some work on a number of topics related to the renin-angiotensin-aldosterone system, which has been a long-standing interest of mine ever since then.                                 So, at the same time, I was learning how to take care of patients with very complex blood pressure problems, who required three, or four, or five, or six blood pressure medications, in some cases, to control. And it's with that background that I became very interested in the science that underlies treatment-resistant high blood pressure in people and what we might be able to do about that. Jane Ferguson: Wow. Nice. Yeah and I think that background of sort of the combination of clinical and experimental is really nice and really important. I think your paper actually exemplifies that really nicely, so using humans but also some basic science techniques and combining them to really have a very patient focused instead of mechanistic interrogation.                                 So as I mentioned, you just published this really nice manuscript using urine as a source of mRNA biomarkers, which has relevance to hypertension and potentially also to other diseases. But before we get sort of too much into the weeds on the specific details, for any of our listeners who didn't get a chance to read your paper yet, maybe you could briefly summarize what you did? Brian Byrd:          Okay, so the general overview of what we were interested in was that the patients who have treatment resistant high blood pressure tend to have a lot of activation of a receptor in the kidney called the mineralocorticoid receptor. And this receptor helps control salt in bladder in the body. Obviously the amount of salt in the blood stays very, very homeostatic, but we if eat more salt one day then the next and there needs to be a system to help regulate the homeostasis. And so, you waste more or less salt in the urine depending upon how much sodium you're taking in.                                 And one of the functions of the mineralocorticoid receptor is to control this salt and bladder regulation or to fine tune it anyway. And the reason we know that that's an important receptor in patients with treatment-resistant high blood pressure is because of a series of studies done by David Calhoun and Brian Williams and others, showing that mineralocorticoid receptor blockers, or antagonists, are very effective in the treatment of tough to control high blood pressure.                                 And so, we had some insight that there would be something interesting to study there, and one of the things that we knew was that the mineralocorticoid receptor is a ligand activated transcription factor. So when it gets activated by it's ligan which canonically is a steroid hormone from the adrenal gland aldosterone, the receptor, which is in the cytoplasm, ordinarily dimerizes and translocates to the nucleus, where it controls the regulation of a number of genes. We also were aware that cells secrete RNA, and others had the idea that it might be inside vesicles because there's a lot of ribonuclease and biofluids. And you would think it might be difficult to pass the RNA if it were sort of naked as it were.                                 And it turns out that that's right. If you, for example, introduced synthetic RNAs into biofluids, the RNAs will be gone very quickly in a matter of seconds. So, we had this idea that we might be able to look at RNA that was being secreted by cells probably in vesicles, and assay the activity of the receptor potentially. We weren't sure if that was going to be possible or not.                                 One of the things we did was we used part of the available data to look at the transcriptome of vesicles in the urine that had been isolated from 3300 milliliters of urine by ultracentrifugation [inaudible 00:18:57]. Jane Ferguson: So it's a big centrifusion. Brian Byrd:          Exactly. Jane Ferguson: Like you [inaudible 00:19:00] Brian Byrd:          It must have been some project. So that was the work of Kevin Miranda and colleagues, and we were able to compare that transcriptome to the transcriptome of human kidney cortex samples from the GTEx project, which a large consortium focused on human transcriptomics.                                 And that was sort of the first part of what we presented in this paper, and the second thing that we did was we looked within a crossover study in a collaboration with Scott Hummel, one of my close collaborators here at the University of Michigan. We looked at individuals who had been put on a low salt diet activating renin-angiotensin-aldosterone system and causing more activation of the mineralocorticoid receptor. And then, those same individuals underwent saline infusion, so salt loading, and we knew that that would suppress the renin-angiotensin-aldosterone system. And we measured the [inaudible 00:20:02] measures of the renin-angiotensin-aldosterone system, but we also took the urine samples that had been recently banked from that experiment and we centrifuged them to try to palette the cells. We took the supernatant and we extracted RNA after trying to enrich for extracellular vesicles.                                 And with that approach, we measure targets that we thought would be regulated my the mineralocorticoid receptor, as well as some things that we did not think would be regulated by mineralocorticoid receptor. So that's the general overview of what we undertook. Jane Ferguson: Great. Right. So it's very cool. I guess we can break it down into sort of the two different parts, because I think it was a really nice examples of using public data to sort of start addressing your question and then actually doing a human experiment. But so for the GTEx data and the urinary data, you looked at few different tissues, right? And was kidney the one that you were thinking upfront would sort of most likely to correlate, or were you also looking at bladder and other sort of tissues that could potentially be of relevance to urine? But what sort of the ... I guess sort of tell me more about those different tissues that you looked at and what you found and what surprised you or not. Brian Byrd:          Great question. So, the kidney was on our minds from the outset. We knew that Mark Knepper at the National Institute of Health had published in the [inaudible 00:21:25] National Academy of Sciences back in 2004 that there are urinary extracellular vesicles. And he had found proteins that are very characteristic of the aldosterone sensitive distal nephron, that part of the kidney that we're interested in, embedded in the vesicles.                                 So we became quite interested in the idea that it seemed that there was likely a population of vesicles in the urine that is of kidney origin. And that's not to say that there weren't also plenty of vesicles from other origins as well, and there could very well be RNA that is not vesicle enclosed, but is rather ribonucleic protein bound or even bound to other carriers potentially. That could be there as well, and it's possible that the origin of those things could be any number of tissues. I don't really think that we know yet where the possible tissue origins are.                                 But we were curious to know ... I guess the direct answer to your question is we thought from the outset that we probably would find some sort of signal related to the kidney. But we wanted to also consider the possibility that our findings were not very specific to the kidney. And so we thought that the brain would be an interesting negative control. If we say very high correlation with the brain, it would suggest that maybe what we're looking for is a signal that's not really coming from the kidney.                                 And we also wanted to look at the bladder just to try to understand whether or not the signals that we're detecting could be coming from the bladder. It's certainly true that some aspects of the system that we're interested in are present in the bladder, so I wondered whether that might even serve as a signal amplifier for what we were looking for since there's, presumably, quite a bit of bladder tissue right around the urine. It might be contributing vesicles.                                 So that's sort of the rationale for why we looked at those things. Jane Ferguson: And you found mostly enrichment for kidneys. So sort of I guess what you were hoping to find came true? That actually there was sort of evidence that even though there may be contribution from other tissues, that really kidney seem to be the predominant contributor to the expression of the genes in the urine. Brian Byrd:          I think there's a lot of truth to that. One of the things I would say is we found high correlation looking across all genes. But it occurred to us ... As soon as we thought that, we realized, wait a second, that could be driven by ubiquitously expressed genes. Housekeeping genes.                                 So we really wanted to stratify our analysis by things we thought would be expressed in the kidney as well as things that we thought would be ubiquitously expressed to make sure that we could see signals that correlate ... That the transcriptome of the kidney, per se, had a good correlation with those same in terms of the abundance of the gene counts or recounts. They said it was similar to what was in the vesicles.                                 And so, we looked in the literature, and we found that a group had already established a number, 55 genes actually, that were highly kidney enriched as well as over 8000 genes that were ubiquitously expressed. And so we started the analysis from this perspective of the stratification. We thought that was a very important aspect of the analysis. And it's definitely true that if you look at our findings with respect to the kidney enriched genes, as you might expect, they correlate quite well with what is in the urinary extracellular vesicles compared to the kidney cortex.                                 You look at the brain as you might expect the expression of those kidney enriched genes is not well correlated with what's happening in the urine. And then, with respect to the bladder, it's sort of somewhere in between. Jane Ferguson: Okay. Interesting. So I know that some people look at small non-coding RNAs in urine, but you were mostly focused on mRNAs. Is that right? Brian Byrd:          That's right. I thought of this as sort of frontier, something that I knew from some early publications was probably measurable. But I didn't know what it would signify, if anything, with respect to physiology. And I knew that there were quite a few papers about micro RNAs and I wanted to do something a little bit innovated, partly.                                 But the main reason that I was interested in the RNAs was because I could relatively easily tie those to the existing literature from animal models. Preclinical animal models and cell culture studies showing what happens when the mineralocorticoid receptor's activated. That was really the driving reason that I was interested in the RNA. Because if you think about what is the approximate event that might be a readout for activation of a new growth hormone receptor like the mineralocorticoid receptor, it's really the transcriptional events that happen when the receptors translocates to the nucleus and serves its ligan activated transcription factor role. Jane Ferguson: Right. So, [inaudible 00:26:43] sort of the first part of analysis, you saw these really nice correlations between expression and kidney and in urine. And then, a lot of the times when you tried to publish that kind of thing, people are like, "Okay, so what? So you didn't do any intervention. We don't really know what that means."                                 But I like that you took it to the next step and you did sort of a human intervention experimental model. So tell me more about that model and how that worked. Brian Byrd:          Right. Well, I'll just mention also that the work that was done in terms of RNA [inaudible 00:27:14] was done in collaboration with Mark Bertini in Italy as well as Dr. [inaudible 00:27:19]. They were fundamental to getting that work done.                                 With respect to the collaboration with Scott Hummel, one of my colleagues here at the University of Michigan, what we did in that setting was to look at whether or not we could identify within these urinary mRNA signals that are in the supernatant in the urine, whether we could identify changes in physiology. That was the question that was of greatest interest scientifically.                                 And for a very practical or blind perspective, the question was could we detect the activation of the receptor that might determine whether or not people should get a certain medication. Of course, we're not saying that that's an established fact yet, but this is sort of concept, that there's something here to explore further.                                 And so, what we found was that a number of genes that are regulated by the mineralocorticoid receptor, including genes encoding the subunits of the amiloride-sensitive epithelial sodium channel that regulates the salt that I was talking about earlier. We found that those genes changed with sodium loading in terms of their abundance in the expected direction.                                 We also found that several of the assays that we made changed ... I'm sorry. That they correlated with the serum aldosterone concentration. So the concentration of the ligan for the receptor whose readout we were looking for. And we also noticed an inverse correlation with urinary sodium excretion, which is what we would expect if we really identified a readout of the mineralocorticoid receptor's activity.                                 So this study supported the idea that we have identified a way to measure this nuclear hormone receptors activity in living humans. Jane Ferguson: Right. Which is really nice. So there's probably a huge amount of extra things you could do with this, some sort of different ways you could look at it. So how did you pick the time point? So, I suppose when you think about it, I mean the genes, they're transcribed and then that takes a little bit of time, and then it takes a little bit of time for that to sort of make its way into the urine and to be excreted.                                 So how did you decide on sort of what time points to use, and do you think you would see the same things or different things [inaudible 00:29:39] if you did repeated sampling or if you looked at different time points? Brian Byrd:          That's a fantastic question. So this was a study that had already been completed, and I had mentioned to Scott what we were working on. And he said, "You know, we have these samples from this study and it might be possible for us to collaborate."                                 So, we didn't get to pick the timeframes. Jane Ferguson: Right. Brian Byrd:          So, that's a great point. And what I would say is that, as you can imagine, we're very focused on exactly the questions you're asking now. What about sort of signal refinement? What about the chrono-biology of these signals, and how do we understand when we see what in the urine?                                 So, I'm actively pursuing those questions. Jane Ferguson: Right. So, I know as well, there was quite a lot of sort of technical challenges I think to doing this work. Sort of getting to be even able to amplify and get a signal from these RNAs that are really present, sort of pretty low abundance in urine compared to tissues or biofluids that we're used to working with.                                 So tell me maybe a little bit about that process and sort of how much optimization was required to get these essays to work? Brian Byrd:          Great question. So, I had known [inaudible 00:30:58] since 2014 when I took a course on isolation of extracellular vesicles in Heidelberg, Germany. And I had talked to him at a meeting in Washington DC, and I had mentioned what we were trying to do. And he said, "You know, if you were trying to do that, you might want to consider preamplification." You know, using something like 15 cycles of preamplification. And he was willing to share that protocol that he had with me, because they were interested in similar issues. So, I was able to use that protocol to evaluate these gene targets in the urine. And so that was immensely helpful.                                 And the other thing that we did was we used locked nucleic acid probes to try to increase the sensitivity and specificity of our assays. Finally, we just tried to use good logic in the design of the assays. So we were concerned that the RNA might be fragmented, so where it was possible to do so within the design constraints that I'll mention in a second, we made multiple assays per gene target just in case this was fragmented. Which makes the analysis a little more complicated, but I think it was probably the right thing to do, given the state of knowledge that we had then.                                 And one of the other things we did was we made sure that the primers either ... Within a primer, there was an intron or between the primers there was an intron, so that if we actually did try to amplify DNA, abundant amounts of DNA, with those primers just to make sure that our theorizing about the inability to amplify things was actually factual. And that turned out that we couldn't amplify anything at 40 cycles with those.                                 So, we spent a lot of time thinking about how not to get fooled, but also to have adequate signal detection. And have included in the supplement quite a bit of information about the technical merits of the assays and showing how close the technical replicates were. They tended to be very, very similar to one another. We didn't see a signal in every urine sample for every participant at both time points, and I think that was interesting to me about that there tended to be a very binary result, so that you'd either see three technical replicants for the QPCR assays, our QPCR assays that were extremely similar to each other, or you would see no CT value detected.                                 [inaudible 00:33:47] That these were valid assessments of very low copy numbers. Jane Ferguson: Right. And that's probably related to up front of what happens to urine right after it's collected and stored, or during that RNA extraction. But it seems like once you've got RNA, then downstream assays were sort of ... They held through, but I guess ... I mean, and you obviously didn't have necessarily a huge amount of control over how these urine samples were collected. So it's kind of nice that you were able to see something even though these were collected possibly in a way that was not optimized for preserving RNAs.                                 But do you think those ... Are there ways that you could make this even sort of more streamlined and better as far from the get go of how you collect the urine, whether you could be extracting stuff right away? Is that anything you sort of looked into of how this could be improved? Brian Byrd:          That's really been the focus of the labs work since we completed that project, is sort of understanding how would we do this in a prospective study in the best possible way so that the results are highly repeatable, that we get a CT value in everybody so that we're really ... I mean, as you can imagine, that actually has something to do with the input volume of urine that you use. So if you have too little input volume, then you won't be able to detect the targets that you might be interested in every person.                                 However, if you have more, then you can do more with that. But then you have to think about how you're going to deal with the larger volumes of urine. There are lots of questions that we've been interested in related to extract the RNA and the stability of the RNA. And so we have done some experiments of that type, and we continue to work in that area. And I do think that those questions you're asking are the right questions with respect to next steps. Jane Ferguson: All right. So you looked at sort of specific targets, which I think made a lot of sense. Sort of this proof of principle. But do you think this would work on a transcriptome wide level? I mean, could you look at all the genes, or do you think that's just sort beyond the possibility right now given sort of the RNA fragmentation and how you have to sort of amplify it before being able to detect anything? Brian Byrd:          I think it's possible. So the group that had preceded our work with 3300 mils of urine, isolating the vesicles from there, eight have showed that that's something that can be done. The question that's of interest to me is does it actually require such large volumes of urine? And I think the answer to that question is going to be no from what we're overseeing so far.                                 And so, we're thinking along exactly the lines that you are. And certainly some of the feedback we've gotten as we've discussed this project with people is, "Hey, could you look at everything rather than picking targets at [inaudible 00:36:41]."                                 I think there's advantages and disadvantages. I think we chose based on prior knowledge in a way that was rational. But at the same time, it may turn out that there are many things about activation of the mineralocorticoid receptor in humans, especially in the living in-tact human, that don't exactly mirror what's found in rabbits, rats, mice or cells, which are really the systems that have been evaluated the most thoroughly in the past.                                 So I'm very interested in exactly what you're proposing. Jane Ferguson: Yeah. I mean, I think it's exciting because it's obviously relevant for hypertension, but potentially a lot of other conditions, to be able to look at that sort of dynamic change. So I think it's really exciting. It's very cool. Brian Byrd:          And I appreciate your asking about this study. We were excited to do this work and very, very excited to see where we can in the future with this. And I agree with the point you were making, that here we've gone from a rather specific application driven question and we've, I think, made some insights that are probably useful outside the application that we had in mind. And it may turn out that the application where this is the most important is not even the one that we considered in the first place at all.                                 And so I'm pleased by that. I'm pleased by the fact that I think in a sense we're working in what Donald Stokes described as pasture's quadrant, which is a sense that the work is driven both by curiosity and by an intent to use the results. Jane Ferguson: Right. Brian Byrd:          And so that's really what gets me out of bed in the morning, is working that exact space. So that's what we were glad to have done and continue to do. Jane Ferguson: Yeah. No, I think it's grea.t and I feel like a lot of people will read this paper and be like, "Hey, I have urine stored in the freezer. What can I do with this now?" Brian Byrd:          Contact me. Let's talk. We'll see what we can do. But we certainly tried to describe the methods in such a way that people could easily follow in our footsteps if they want to apply these methods. Jane Ferguson: Yeah. Now having read through them, I think that ... Really thorough. I really liked the sort of attention to detail. It was definitely one of those ones where I was like, "Oh yeah. I can see exactly how I could do this if I wanted to. So I think that was great. Brian Byrd:          Thank you. Jane Ferguson: So yeah. Congratulations on the paper. Really nice work and thanks so much for talking to me. Brian Byrd:          Thank you. It was a delight. Jane Ferguson: That's it from me for September. If you haven't had enough yet, you can access all the papers online and you can choose to digest the papers in video format. Available on our website or the Circulation YouTube channel. Thank you for listening and subscribing. I look forward to bringing you more next month.  

Jane Ferguson: Hello. Welcome to episode 19 of Getting Personal: Omics of the Heart, the issue from August 2018. I am Jane Ferguson, and this podcast is brought to you by the Circulation: Genomic and Precision Medicine Journal and the American Heart Association Council on Genomic and Precision Medicine. Before I dive into the papers from this month, a reminder that early bird registration for AHA Scientific Sessions runs until September 4th, so go register now if you haven't already to take advantage of reduced rates. The meeting will be held in Chicago from November 10th through 12th, and it's the first year of the new three-day meeting format. It's already promising to be a really great meeting, and I'm hoping to see a lot of you there.     The August issue has a number of really interesting papers. First up, Gardar Sveinbjornsson, Eva Olafsdottir, Kari Stefansson, and colleagues from deCODE genetics-Amgen report that variants in NKX2-5 and FLNC cause dilated cardiomyopathy and sudden cardiac death. This team leveraged available DNA samples from the Icelandic population to carry out a genome-wide association study in 424 cases of dilated cardiomyopathy and over 337,000 controls. They applied whole genome sequencing to all of these samples, allowing them to identify common and rare variants. In total, they tested over 32 million variants.     They found two variants that were significantly associated with DCM at genome-wide significance, a missense variant in NKX2-5 and a frameshift in FLNC, both associated with heart failure and sudden cardiac death. Further, the NKX2-5 variant was associated with atrioventricular block and atrial septal defect. Although these variants are rare and not documented in other populations, they are significant contributors to familial DCM in Iceland. Because of the unique population structure of Iceland and known genealogy, the researchers were able to trace the NKX2-5 variant back to a common ancestor born in 1865. They traced the FLNC variants to a common ancestor born in 1595.     While the specific variants identified in this study may not be present in other populations, they are located in genes with known relevance for cardiac function. NKX2-5 encodes a cardiac transcription factor, which is required for embryonic cardiac development, and other variants in this gene have been associated with cardiac dysfunction in other populations. FLNC encodes filamin-C, a muscle cross-linking protein. Variants in FLNC have previously been ascribed to associate with myofibrillar myopathy, muscular dystrophy, and cardiomyopathy. This study adds to our knowledge of the genetics of dilated cardiomyopathy and supports screening for NKX2-5 and FLNC variants, particularly in the Icelandic population, which would allow for early intervention and monitoring in carriers.     Staying with the topic of dilated cardiomyopathy, Inken Huttner, Louis Wang, Diane Fatkin, and colleagues from the Victor Chang Cardiac Research Institute in Australia report that an A-band titin truncation in zebrafish causes dilated cardiomyopathy and hemodynamic stress intolerance. We actually talked to Dr. Wang about this research last year when he was presenting this as a finalist for the FGTB Young Investigator Award. You can go back in the archives to episode 10 from November 2017 if you'd like to hear more.     Titin mutations are responsible for a large number of cases of dilated cardiomyopathy, but there are also individuals with titin mutations that remain asymptomatic. This group used zebrafish as a model of human titin mutations and generated fish with a truncating variant in the A-band of titin, as has been identified in families with DCM. They found that homozygous mutants had a severe cardiac phenotype with premature death, but that heterozygous carriers survived into adulthood and developed spontaneous DCM. Prior to onset of DCM, the heterozygous fish had reduced baseline ventricular systolic function and reduced contractile response to hemodynamic stress, as well as ventricular diastolic dysfunction.     Overall, the mutant fish displayed impaired ability to mount stress responses, which may have contributed to development of disease. Extrapolating this to humans, this could suggest that hemodynamic stress may be a factor that contributes to timing and severity of disease in individuals with titin variants. Hemodynamic stress can be exerted by exercise, pregnancy, and other diseases contributing to ventricular volume overload. Modifying these hemodynamic stressors in at-risk subjects could potentially help to modulate the severity of DCM phenotypes.     Moving on to the topic of coronary artery disease, Vinicius Tragante, Daiane Hemerich, Folkert Asselbergs, and colleagues from University Medical Center Utrecht in the Netherlands report on druggability of coronary artery disease risk loci. This group was interested in using results from genome-wide association studies for CAD to identify new targets that may be amenable for drug repurposing. They used results from published GWAS for CAD and created a pipeline to integrate these loci with data on drug-gene interactions, chemical interactions, and potential side effects. They also calculated a druggability score based on the gene products to prioritize targets that are accessible and localized to increase the chance of a drug being able to find the target without affecting core systemic processes or housekeeping genes.     Their pipelines allowed them to identify three possible drug-gene pairs, including pentolinium to target CHRNB4, adenosine triphosphate to target ACSS2, and riociguat to target GUCY1A3. They also identified three proteins to be prioritized for drug development, including leiomodin 1, huntingtin-interacting protein 1, and protein phosphatase 2, regulatory subunit b-double prime, alpha). While these predictions were all made in silico and need to be extensively tested in clinical trials, the pipeline did identify many current therapies for CAD and myocardial infarction, including statins, PCSK9 inhibitors, and angiotensin II receptor blockers. These positive controls support that this method can successfully discover effective CAD therapies.     Staying on the topic of drugs, Kishan Parikh, Michael Bristow, and colleagues from Duke University report on dose response of beta-blockers in adrenergic receptor polymorphism genotypes. Two clinical trials have reported pharmacogenomic interactions between beta-blockers and beta-1 adrenergic receptor genotype in the setting of heart failure with reduced ejection fraction. In a retrospective analysis in almost 2,000 subjects from the BEST and HF-ACTION studies, the authors analyzed whether genotype at the Arg389Gly polymorphism in beta-1 adrenergic receptor, or an indel in the alpha-2C adrenergic receptor interacted with drug dose to affect mortality and hospitalization.     They found that ADRB1 genotype affected mortality in response to drug dose with less all-cause mortality in high versus no or low-dose beta-blockers in individuals homozygous for arginine at position 389, but not in individuals carrying a glycine at that position. In individuals on high-dose beta-blockers, genotype did not affect outcomes, but there was a significant difference by genotype in all-cause mortality in individuals on no or low-dose beta-blockers. These data support the guideline recommendations to use high-target doses of beta-blockers in HFrEF.     Switching gears towards precision medicine and genotype-guided approaches, Laney Jones, Michael Murray, and colleagues from Geisinger were interested in the patient's perspective. In their paper, Healthcare Utilization and Patients’ Perspectives After Receiving a Positive Genetic Test for Familial Hypercholesterolemia, they explored the impact of providing genotype test results for familial hypercholesterolemia to subjects participating in the MyCode Community Health Initiative. In MyCode, exome sequencing is conducted in participants, and results are returned for pathogenic and likely pathogenic variants in genes representing actionable conditions based on American College of Medical Genetics secondary findings and recommendations.     It is estimated that 3.5% of MyCode participants will be carriers of such variants, and this number may increase as more variants are discovered. In this pilot study, the authors screened for individuals with mutations in LDLR, APOB, or PCSK9, consistent with FH. They identified 28 individuals, of which 23 were eligible for inclusion in the study. Only five of the 23 subjects had previously been diagnosed with FH. Receipt of genetic test results led to change in medications in 39% of individuals. 96% of the subjects had previous LDL measurements, but only four subjects had ever met LDL goals. After genetic test results, three individuals met their LDL goals.     Seven individuals consented to participate in interviews about their experience. Almost all of these subjects already had a personal or family history of high cholesterol or heart disease, and all subjects felt that they were being adequately treated. Only three of the seven subjects mentioned using diet and exercise to control their high cholesterol, with most individuals being relatively unconcerned because they felt their medication was effective in controlling disease risk.     While the numbers studied here are too small for any statistical testing or inference, the paper describes the results from the interviews, including some excerpts from patients, which really highlight the complexities of returning results and of helping patients understand what their results mean. Given increasing genetic testing and returning of results, studies like this are really important to help us figure out the most effective ways to communicate results and support patients and their care providers.     Also from a patient-centric perspective, we have an article from Susan Christian, Joseph Atallah, and colleagues from the University of Alberta in Canada on when to offer predictive genetic testing to children at risk of an inherited arrhythmia or cardiomyopathy, the family perspective. This article considers the timing of cascade testing to predict inherited arrhythmias and cardiomyopathy in children of affected individuals. European and North American guidelines differ on when or if they recommend genetic testing in children.     In this study, surveys were circulated to foundations and patient groups to solicit familial perspectives on when genetic testing should be offered to children. In total, 213 individuals responded. In the case of long QT syndrome, 92% of respondents thought testing should be offered before the age of five, while 77% of respondents thought genetic testing should be offered before the age of 10 for hypertrophic cardiomyopathy and arrhythmogenic right ventricular cardiomyopathy.     Overall, the potential benefits of genetic testing, including guiding therapies, sport participation, and decreasing worry were ranked more highly than potential risks of discrimination or increasing worry that could occur from genetic testing. Overall, the responses indicated that families would welcome the option of genetic testing for at-risk children from a young age and support initiating early discussions with families to explore costs and benefits of early genetic testing.     Finally in this issue, we have a review from Paul Franks and Nicholas Timpson from Lund University and the University of Bristol entitled Genotype-Based Recall in Complex Cardiometabolic Traits. This review looks at the increasing practice of selecting samples or individuals from larger cohorts or biobanks based on their genotype to carry out additional studies. The article focuses on examples of such genotype-based recall studies in cardiometabolic disease, highlights approaches and new methods, and discusses the ways these types of studies can be used to extend and supplement randomized trials and large population-based studies.     As always, you can find all the articles, accompanying editorials, and video summaries online. Our website recently underwent some redesigns and has moved. You should be redirected if you have the older site bookmarked, but you can also find us directly at ahajournals.org/journal/circgen. Also, thanks to everyone who participated in the Twitter poll last month. You were pretty evenly split on what you want to hear in the podcast, but please continue to leave suggestions and feedback on what we're doing and where we can improve things. That's it for the August issue of Circulation: Genomic and Precision Medicine. Thanks for listening, and tune in next month for more.    

Jane:                                     Hi, everyone. Welcome to Episode 18 of Getting Personal: Omics of the Heart. I'm Jane Ferguson, and this podcast is brought to you by the Circulation: Genomic and Precision Medicine Journal and the American Heart Association Counsel on Genomic and Precision Medicine. It is July 2018, which means that the best possible place to be listening to this episode is at the beach, but failing that I can also recommend listening on planes, during your commute, while exercising or while drinking a nice cup of tea.                                                 So before I get into the papers we published this month, I want to ask for your help. If you're listening to this right now, hi, that means you, we're a year and a half into podcasting and I would love to know what content you like and where we could improve things. We have a poll up on Twitter this week, and I would really appreciate your input. If you're listening to this a little bit later and miss the active voting part of the poll, you can still leave suggestions.                                                 Okay, so what I would like you to do right now is to go to Twitter. You can find us as Circ_Gen and locate the poll. If you don't already follow us on Twitter, go do that now too. We want you to let us know what content we should focus on and what is most useful to you, so go ahead and pick your favorites from the options and also please reply or tweet at us with other thoughts and suggestions.                                                 Options include giving summaries of the recent articles like I'm about to do later this episode, conducting interviews with authors of recently published papers, interviews with people working in cardiovascular genomics, broader topics. For example, to get their insight on career paths and lessons learned along the way.                                                 And something we have not done yet on the podcast but are considering, would be to record podcasts that focus on particular topics in genomics and precision medicine. These could give some background on an emerging field or technology and we could talk to experts who are leading particular innovations in the field. So, if that sounds good to you, let me know! If you're not on Twitter, I don't want to exclude you, so you can email me at jane.f.ferguson@vanderbilt.edu and give me your thoughts that way. I'm looking forward to hearing from you.                                                 Okay, so on to the July 2018 issue of Circ.: Genomic and Precision Medicine. First up is a PhWAS from Abrahim Rao, Eric Ingelsson, and colleagues from Stanford. The discovery of the PCSK9 gene as a regulator of cholesterol levels has led to a new avenue of LDL lowering therapies through PCSK9 inhibition. However, some studies suggest that long term use of PCSK9 inhibitors could have adverse consequences. Because of the long follow-up time required, it will take many more years to address this question through clinical studies. However, genetic approaches offer a fast and convenient alternative to address the issue.                                                 In this paper, entitled: "Large Scale  Phenome-Wide Association Study of PCSK9 Variants Demonstrates Protection Against Ischemic Stroke," the authors use genetic and phenotype data from over 300,000 individuals in the UK BioBank to address whether genetic loss of function variants in PCSK9 are associated with phenotypes including coronary heart disease, stroke, type II diabetes, cataracts, heart failure, atrial fibrillation, epilepsy, and cognitive function.                                                 The missense variant RS11591147 was associated with protection against coronary heart disease and ischemic stroke. This SNP also associated with type II diabetes after adjustment for lipid medication status. Overall, this study recapitulated the associations between PCSK9 and coronary disease, and revealed an association with stroke.                                                 Previous studies suggested use of LDL lowering therapies may increase risk of cataracts, epilepsy, and cognitive dysfunction, but there was no evidence of association in this study. Overall, this study provides some reassurance that the primary effect of PCSK9 is on lipids and lipid related diseases, and that any effects on other phenotypes appear to be modest at best. While a PhWAS can't recapitulate a clinical trial, what this study indicates is that PCSK9 inhibition is an effective strategy for CVD prevention, which may confer protection against ischemic stroke and does not appear to convey increased risk for cognitive side effects.                                                 Next up we have a manuscript form Jason Cowan, Ray Hershberger, and colleagues from Ohio State University College of Medicine. Their paper, "Multigenic Disease and Bilineal Inheritance in Dilated Cardiomyopathy Is Illustrated in Non-segregating LMNA Pedigrees," explored pedigrees of apparent LMNA related cardiomyopathy identifying family members who manifested disease, despite not carrying the purported causal LMNA variant. Of 19 pedigrees studies, six of them had family members with dilated cardiomyopathy who did not carry the family's LMNA mutation. In five of those six pedigrees, the authors identified at least one additional rare variant in a known DCM gene that was a plausible candidate for disease causation.                                                 Presence of additional variants was associated with more severe disease phenotype in those individuals. Overall, what this study tells us is that in DCM, there is evidence for multi-gene causality and bilineal inheritance may be more common than previously suspected. Future larger studies should consider multi-genic causes and will be required to fully understand the genetic architecture of DCM.                                                 Yukiko Nakano, Yasuki Kihara, and colleagues from Hiroshima University published a manuscript detailing how HCN4 gene polymorphisms are associated with tachycardia inducted cardiomyopathy in patients with atrial fibrillation. Tachycardia induced cardiomyopathy is common in subjects with atrial fibrillation, but the pathophysiology is poorly understood. Recent studies have implicated the cardiac hyperpolarization activated cyclic nucleotide gated channel gene, or HCN4, in atrial fibrillation and ventricular function.                                                 In this paper, the authors enrolled almost 3,000 Japanese subjects with atrial fibrillation, both with and without tachycardia-induced cardiomyopathy, as well as non-AF controls. They compared frequency of variants in HCN4 in AF subjects with or without tachycardia-induced cardiomyopathy, and found a SNP, RS7164883, that may be a novel marker of tachycardia-induced cardiomyopathy in atrial fibrillation.                                                 Xinyu Yang, Fuli Yu, and coauthors from Tianjin University were interested in finding causal genes for intracranial aneurysms, and report their results in a manuscript entitled, "Rho Guanine Nucleotide Exchange Factor ARHGEF17 Is a Risk Gene for Intracranial Aneurysms." They sequenced the genomes of 20 Chinese intracranial aneurysm patients to search for potentially deleterious, rare, and low frequency variants. They found a coding variant in the ARHGEF17 gene which was associated with associated with increased risk in the discovery sample, and which they replicated in a sample of Japanese IA and in a larger Chinese sample.                                                 They expanded this to other published studies, including individuals of European-American and French-Canadian origin and found a significantly increased mutation burden in ARHGEF17 in IA patients across all samples. They were interested in further functional characterization of this gene and found that Zebra fish ARHGEF17 was highly expressed in blood vessels in the brain. They used morpholinos to knock down ARHGEF17 in Zebra fish, and found that ARHGEF17 deficient Zebra fish developed endothelial lesions on cerebral blood vessels, and showed evidence of bleeding consistent with defects in the vessel. This study implicates ARHGEF17 as a cerebro-vascular disease gene which may impact disease risk through effects on endothelial function and blood vessel stability.                                                 Sumeet Khetarpal, Paul Babb, Dan Rader, Ben Voight, and colleagues from the University of Pennsylvania used targeted resequencing to look at determinants of extreme HDL cholesterol in their aptly titled manuscript, "Multiplexed Targeted Resequencing Identifies Coding and Regulatory Variation Underlying Phenotypic Extremes of HDL Cholesterol in Humans." Stay tuned because we're gonna hear more about this paper from the first author Dr. Sumeet Khetarpal later this episode.                                                 Rounding out this issue we have a Perspective article from Chris Haggerty, Cynthia James, and coauthors from Geisinger and Johns Hopkins Medical Center entitled, "Managing Secondary Genomic Findings Associated With Arrhythmogenic Right Ventricular Cardiomyopathy: Case Studies and Proposal for Clinical Surveillance." In this paper the authors discuss the challenges for returning findings from clinical sequencing for arrhythmogenic right ventricular cardiomyopathy, presenting case studies exemplifying these challenges. They also propose a management approach for returning clinical genomic findings, and discuss new innovations in the light of precision medicine.                                                 We also published a review article by Pradeep Natarajan, Siddhartha Jaiswal, and Sekar Kathiresan from MGH on "Clonal Hematopoiesis Somatic Mutations in Blood Cells and Atherosclerosis", which discusses recent advances in our knowledge on the role of somatic mutations in cardiovascular disease risk.                                                 Finally, we have an update on some pharmacogenomics research into CYP2C19 Genotype-Guided Antiplatelet Therapy by Craig Lee and colleagues which we published a few months ago. Dr. Lee was also featured on Podcast episode 15 in April of this year.                                                 Jernice Aw and colleagues from Khoo Teck Puat Hospital, Singapore shared from complimentary data from their sample of 247 Asian subjects which found the risk for major adverse cardiovascular events was over 30-fold greater for poor metabolizers, as defined by CYP2C19 genotype on clopidogrel, as compared to those with no loss of function allele.                                                 You can read that letter and the response from Dr. Lee and colleagues online now. And, as usual, all of the original research articles come with an editorial to help give some more background and perspective to each paper. Go to circgenetics.ahajournals.org to find all the papers and to access video summaries and more.                                                 Our interview is with Dr. Sumeet Khetarpal who recently completed his MD-PhD training at the University of Pennsylvania, and is currently a resident in Internal Medicine at Massachusets General Hospital. Sumeet kindly took some time out from his busy residency schedule to talk to me about his recently published paper, and to explain how molecular inversion probe target capture actually works.                                                 So I am here with Dr. Sumeet Khetarpal who is co-first author on a manuscript entitled, "Multiplexed Targeted Resequencing Identifies Coding and Regulatory Variation Underlying Phenotypic Extremes of High-Density Lipoprotein Cholesterol in Humans."                                                 Welcome Sumeet, thanks for taking the time to talk to me. Dr. Khetarpal:                    Thank you so much Dr. Ferguson, it's really a pleasure to talk to you today. Jane:                                     Before we get started, maybe you could give a brief introduction on yourself and then how you started working on this paper. Dr. Khetarpal:                    Sure, so this work actually was a collaboration that came out at the University of Pennsylvania that I was involved with through my PhD thesis lab, my mentor was Dan Rader, and also a lab that is a somewhat newer lab at Penn, Benjamin Voight's lab which is a strong sort of computational genomic lab.                                                 This work actually highlights the fun of collaborating within your institution. We had, for some time, been interested in developing a way to sequence candidate genes. Both known genes and also new genes that have come out of genome-wide association studies that underlie the extremes of HDL cholesterol, namely very high cholesterol versus low HDL cholesterol. We've been looking for a cost-effective and scalable way to do this.                                                 Independently, Ben, who is very interested in capturing the non-coding genome, was interested in developing a method to better understand the non-coding variation, both common and rare variation that may be present at all of these new loci that have come out for complex traits such as HDL.                                                 We, at some Penn event several years ago, were talking about our common interest and Ben had actually identified this work that had come out of J. Shendure's lab at the University of Washington. A paper by the first author, Brian O'Rouke, in Science in 2012 in which they had developed an approach that involved molecular inversion probes, or MIPs, to capture regions of the genome related to target the gene that they were interested in studying for autism-spectrum disorders.                                                 They had applied this largely to coding regions of, I think, almost 50 genes and almost 2,500 patients with the feedback to do deep, targeted sequencing. So our thought was, well, we could try to apply this approach and adapt it to capture non-coding regions, and also see if we can expand the utility of this approach to study the phenotypic extremes of a complex trait such as HDL cholesterol. Jane:                                     Yeah, that's really cool. I love how you saw this method in a totally different application and then realized that there was expertise at Penn that you could bring together to apply this in a different way.                                                 I'd love to hear more about this MIP, the molecular inversion probe. How does it work? How difficult is it to actually do? Is it very different from normal library preparation for sequencing or is it something that's actually relatively easy to apply? Dr. Khetarpal:                    These MIP probes are oligonucleotide probes that capture your region of interest by flanking them and capturing by gap filling. There's a method to capture parts of the genome in a library-free way. They do ultimately involve barcoding the way traditional library-based target capture does and then deep sequencing.                                                 But the most impressive feature about them is just that they're very scalable. I think in the original paper by O'Rouke and colleagues they were able to sequence their set of genes and their set of samples at about a sample preparation cost of $1 per sample, and we were actually able to do about the same for our study.                                                 The main utility of the approach is just the economic scalability, and the ability to customize your panel to capture several regions of the genome that are adjacent to each other. Jane:                                     Right, so how many genes or regions can you multiplex at the same time? Is it just one prep, like you just design all of your oligos, you put them all together in one reaction, or are you doing separate reactions for each region? Dr. Khetarpal:                    We're actually doing all of our oligos together. In our case, I think it ended up being around the order of almost 600 oligos together to capture our ultimately 50kB of genomic territory that we wanted to capture. Really, our study was kind of a pilot experiment where we picked a few genes or regions of high interest to us, both known genes that effect HDL and also those that have been implicated in genome-wide association studies that were of high interest to our labs.                                                 I think that this approach could actually be expanded to capture much more genomic territory in a single capture reaction. We sort of touched the surface probably of what we could do. Jane:                                     Wow, that's cool! And then for sequencing it, I guess it's really just a function of how many samples you wanna multiplex and how much you want to sequence from each region. So I suppose the way you did it, you had about 50kB and then you had over 1,500 participants and you were able to do those on a single HiSeq run, right? Dr. Khetarpal:                    Right. Jane:                                     So I suppose if you'd done more genetic regions, you would've had fewer people and vice versa so you can balance that out depending on if you're having more samples or more genomic regions to sequence. Dr. Khetarpal:                    Exactly, in certain ways the design of our experiment we had a limited sample size that did afford us some luxury in terms of knowing that we would have deep coverage of the region that we were targeting. I think that's always a critical question in sort of targeted or just sequencing in general. The balance between the number of regions that you want to sequence and the number of samples you want to sequence is going to dictate what your sequencing depth with be. Jane:                                     Right, okay so I guess if we go on to what you actually found, how'd you pick this? You picked seven regions which encompasses eight candidate genes for HDL, so how did you select those? Dr. Khetarpal:                    The population that we were studying, the samples we were looking to sequence were largely individuals which fall into two bins if you will. One was extremely high HDL cholesterol which we're defining as the greater than the 95th percentile, but really there was a range within that population that spanned individuals with probably greater than the 99th percentile of HDL.                                                 We were hoping as a proof of principle effort to identify variation in genes that were known causes of high HDL cholesterol in prior studies of Mendelian genes for HDL. So genes such as LIP gene which encodes endothelial lipase or CETP or SCARB1, these 3 genes are, at this point, well-known genes that loss of function mutations are associated with extremely high HDL. We thought that capturing some of those genes would potentially both provide a level of validation for the approach, hypothesizing that individuals with high HDL would be enriched with these genes, but also may allow us to find new variants in these genes or also non-coding variants which has not previously been studied before.                                                 Some of the genes came out from that line of thinking, then some of the other genes happened to be genes that in the Rader laboratory we had a vested interest in understanding the genetic variation that might link the genes to HDL, which may not have necessarily come out before.                                                 For example, the gene GALNT2 is one of the first g-loss implicated novel genes for HDL, novel as in the earliest g-loss study for plasma lipids had identified that gene as associated with HDL but it never had come out before as being so. Our laboratory was very interested in better understanding the genetic relationship between genes such as GALNT2 and several of the others such as CCDC92 and ZNF664 with HDL.                                                 It ended up being a hodge-podge or a sampling of genes that had at some level been implicated with HDL, but really it's just a proof of principle that this method could work for both identifying variation in known genes and also less studied ones. Jane:                                     You validated the MIP genotyping by exome genotyping, and then saw concordance of over 90%, is that lower than you were expecting? Was it about what you were expecting based on these two different methods of genotyping? Dr. Khetarpal:                    Yes, I think we were expecting somewhere on the order of 90 plus percent. It's hard to know why we just hit that, we likely would've benefited from being able to genotype all of the individuals by the exome chip that we had sequenced as well, where we were able to validate in about two-thirds of those individuals.                                                 It's hard to know exactly what the cause of the about 10% discordance rate might be, whether it's just in certain samples the genotyping quality was perhaps on the border of being valid or the sequencing quality. Jane:                                     Right, I'm wondering sort of with the MIP, what's the gold standard? Is the XM chip genotyping still the gold standard and the MIP maybe is more error-prone, or perhaps the other way around? Or is it you can't tell at this point which is the true genotype and which is an error potentially for those discordant ones? Dr. Khetarpal:                    Certainly whenever there's a new sequencing methodology that is proposed I think it's critical to have some sort of validation. We happened to cover regions that would span the genome enough that we had XM chip genotyping in a large subset, that that might be the best approach. But if you had a limited number of regions or variance that you were interested in one could imagine also doing Sanger sequencing as the tried and tested validation approach. Of course it becomes not so scalable at a certain point.                                                 Certainly we would say that the MIPs, while the method has been developed and expanded by the Shendure lab, our hope is that through our studies maybe it will be applied further. It's still very much a new approach and so validation is key. Jane:                                     Very important. What do you think was the most exciting finding that came out of this, after you analyzed the data, what were you most excited about seeing? Dr. Khetarpal:                    The critical finding for us, which I think implies the utility of the approach, was just the validation of four of the loci that we had studied. Validation in our cohort of known genome-wide significant associations for HDL that had been published previously in almost 200,000 individuals in terms of sample size, in our experiment involving just about 1,500 people we were able to find consistent associations of those same variants that segregated with low versus high HDL. Directionally consistent with the large genome-wide association studies.                                                 I think the value of this finding is really just to emphasize the utility of the case control design in these phenotypic extremes, in addition to the overarching goal of our study, which was in a way that perhaps provides the most validation of the approach in terms of concordance with prior known studies. Jane:                                     So if somebody was listening to this and was trying to decide should they use MIP for a study they have in mind, should they use another technique? Based on your experience, what would you recommend? Dr. Khetarpal:                    I think in our current stage it's a very exciting time because we're just seeing whole genome sequencing really take off and being used at scale to ask critical questions about non-coding variation as it relates to both disease and complex traits. I don't think we're quite there yet with being able to apply that approach in a cost effective manner. The ability to annotate and analyze that data is still at it's infancy. The utility of the MIPs is that it provides a very cheap alternative.                                                 I can say from my experiences actually doing the capture and preparation from sample to sequencer stage that it's a very easy to use methodology that is very fast and cheap. That if one is really interested in a handful, or more than a handful, of candidate genes and their non-coding regions as it relates to a trait or disease of interest, it may not be the era for going full on with whole genome sequencing, especially at the current cost. That's where I think the MIPs really come in to be very useful. Jane:                                     It sounds great, is there anything else that you'd like to mention? Dr. Khetarpal:                    Just to say that we recognize it's a relatively small study as our pioneer approach with this method but that the Rader lab and Voight labs are actively pursuing larger applications of this to study, not only HDL, but other complex traits, such as diabetes, in much larger populations. I can't overemphasize how easy of a method it is to apply, but also that I think a bigger take home of this study for me as a very recent graduate student working in a very collaborative institution the ability of two laboratories to come together with different sets of expertise to try to tackle a problem that I think goes beyond the individual science. For any human geneticist how to find the variation you're interested in and not break the bank is kind of at the core of what we do, and so I think it was very fun to be part of this collaboration and our hope is that the outcome of it is a method that can be useful for many people, both in our field and beyond. Jane:                                     I think it's great and I'm hoping this will inspire a lot of other people to try this method and see if it can work for them. So, congratulations on the study, it's really nice work. Dr. Khetarpal:    Thank you so much!                                                                                                                                       Jane:                                     That's all I have for you for July, thanks for listening. Send me your thoughts on the podcast via Twitter or email, or leave us a review in Itunes. I look forward to talking to you next month.  

Jane Ferguson:                 Hello, welcome to Getting Personal: Omics of the Heart. It is June 2018, and this is podcast episode 17. I'm Jane Ferguson, an assistant professor of medicine at Vanderbilt University Medical Center, and a proponent of precision medicine, genomics, and finding ways to prevent and treat heart disease. Jane Ferguson:                 This podcast is brought to you by Circulation: Genomic and Precision Medicine, and the AHA Council on Genomic and Precision Medicine. Jane Ferguson:                 For our interview this month, early career member, Jennie Lin talked to Beth McNally about science and careers in genomic medicine. We'll have more on that later but first I want to tell you about the cool papers we published in the journal this month. Jane Ferguson:                 First up, Orlando Gutierrez, Marguerite Irvin, Jeffrey Kopp, Cheryl Winkler, and colleagues from the University of Alabama at Birmingham, and the NIH, published an article entitled APOL1 Nephropathy Risk Variants and Incident Cardiovascular Disease Events in Community-Dwelling Black Adults. This study was conducted in over 10 thousand participants of the Reasons for Geographic and Racial Differences in Stroke, or, REGARDS Study. They examined associations between APOL1 variants and incident coronary heart disease, ischemic stroke, or composite CVD outcome. Because there are coding variants in the APOL1 Gene that are only found in individuals of African ancestry, these are hypothesized to contribute to the disparities in cardiovascular and renal disease in African Americans. Jane Ferguson:                 The authors found that carrying the risk variants was associated with increased risk of ischemic stroke, but only in individuals who did not have diabetes, or chronic kidney disease. They hypothesize that because diabetes and kidney disease already increase CVD risk, the variant does not have an additional effect on risk in individuals with existing comorbidities. But, it contributes to small vessel occlusion and stroke in individuals without diabetes. Jane Ferguson:                 They also found that the magnitude and strength of the association became stronger in a model adjusted for African ancestry, suggesting an independent effect of the APOL1 risk variants. Jane Ferguson:                 While future work is needed to study this more, this is an important step in understanding the complex relationship between APOL1 and disease. Jane Ferguson:                 Next up, Daniela Zanetti, Erik Ingelsson, and colleagues from Stanford, published a paper on Birthweight, Type 2 Diabetes, and Cardiovascular Disease: Addressing the Barker Hypothesis with Mendelian Randomization. The Barker Hypothesis considers that low birthweight as a result of intrauterine growth restriction, causes a higher future risk of hypertension, type 2 diabetes, and cardiovascular disease. However, observational studies have been unable to establish causality or mechanisms. Jane Ferguson:                 In this paper, the authors used Mendelian Randomization as a tool to address causality. They used data from the UK Biobank, and included over 237,000 participants who knew their weight at birth. They constructed genetic predictors of birthweight from published genome wide association studies, and then looked for genetic associations with multiple outcomes, including CAD, stroke, hypertension, obesity, dyslipidemia, dysregulated glucose and insulin metabolism, and diabetes. Jane Ferguson:                 The Mendelian randomization analysis indicated that higher birthweight is protective against CAD type 2 diabetes, LDL cholesterol, and high 2 hour glucose from oral tolerance test. But, higher birthweight was associated with higher adult BMI. This suggests that the association between low birthweight and higher disease risk is independent of effects on BMI later in life. While the study was limited to a well nourished population of European ancestry, and would need to be confirmed in other samples, and through non-genetic studies, it suggests that improving prenatal nutrition may be protective against future cardiometabolic disease risk. Jane Ferguson:                 Laura Muino-Mosquera, Julie De Backer, and co-authors from Ghent University Hospital, delved into the complexities of interpreting genetic variants, as published in their manuscript, Tailoring the ACMG and AMP Guidelines for the Interpretation of Sequenced Variants in the FBN1 Gene for Marfan Syndrome: Proposal for a Disease- and Gene-Specific Guideline. Jane Ferguson:                 With a large number of variants being uncovered through widespread sequencing efforts, a crucial challenge arises in their interpretation. The American College of Medical Genetics and Genomics, and the Association for Molecular Pathology put forward variant interpretation guidelines in 2015, but these were not tailored to individual genes. Because some genes have unique characteristics, the guidelines may not always allow for uniform interpretation. Jane Ferguson:                 In their manuscript, the authors focused on variants in fibrillin-1 that cause Marfan Syndrome, and reclassified 713 variants using the guidelines, comparing those classifications to previous in-depth methods which had indicated these variants' causal or uncertain significance. They find 86.4% agreement between the two methods. Jane Ferguson:                 Applying the ACMG, AMP guidelines without considering additional evidence may thus miss causal mutations. And it suggests that adopting gene specific guidelines may be helpful to improve clinical decision making and accurate variant interpretation. Jane Ferguson:                 Delving deeper into FBN1 and Marfan Syndrome, Norifumi Takeda, Ryo Inuzuka, Sonoko Maemura, Issei Komuro, and colleagues from the University of Tokyo examined the Impact of Pathogenic FBN1 Variant Types on the Progression of Aortic Disease in Patients With Marfan Syndrome. They evaluated 248 patients with pathogenic, or likely pathogenic, FBN1 variants, and examined the effect of variant subtype on severe aortopathy, including aortic root replacement, type A dissections, and related death. They found that the cumulative aortic event risk was higher in individuals with haploinsufficient type variants, compared with dominant negative variants. Jane Ferguson:                 Within individuals with dominant negative variants, those that affected Cysteine residues, or caused in-frame deletions, were associated with higher risk compared with other dominant negative mutations, and were comparable to the risk of the haploinsufficient variants. These results highlight the heterogeneity and risk of the FBN1 variants, and suggest that individuals with haploinsufficient variants, and those carrying dominant negative variants affecting Cysteine residues or in-frame Deletions, may need more careful monitoring for development of aortic root aneurysms. Jane Ferguson:                 Lydia Hellwig, William Klein, and colleagues from the NIH, investigated the Ability of Patients to Distinguish Among Cardiac Genomic Variant Subclassifications. In this study, they analyzed whether different subclassifications of variants of uncertain significance were associated with different degrees of concern amongst recipients of genetic test results. 289 subjects were recruited from the NIH ClinSeq Study, and were presented with three categories of variants, including variants of uncertain significance, possibly pathogenic, and likely pathogenic variants. Participants were better able to distinguish between the categories when presented with all three. Whereas, a result of possibly pathogenic given on its own, produced as much worry as a result of likely pathogenic. The authors conclude that multiple categories are helpful for subjects to distinguish pathogenicity subclassification, and that subjects receiving only a single uncertain result, may benefit from interventions to address their worry and to calibrate their risk perceptions.  Jane Ferguson:                 Erik Ingelsson and Mark McCarthy from Stanford, published a really nice review article entitled Human Genetics of Obesity and Type 2 Diabetes: Past, Present, and Future. Over the past decade, we've had a lot of excitement, optimism, and also disappointment in what genome-wide association studies can deliver. Doctors Ingelsson and McCarthy do a great job laying out the history and the successes in the field of genetic interrogation of obesity and diabetes, as well as acknowledging where reality may not live up to the hype, what challenges remain, and what the future may hold. They also have a figure that uses an analogy of a ski resort to emphasize the importance of taking a longitudinal perspective. And I would argue that any paper that manages to connect apres-ski with genomics is worth reading, for that alone. Jane Ferguson:                 Robert Roberts wrote a perspective on the 1986 A.J. Buer program, pivotal to current management and research of heart disease. Highlighting how the decision by the AHA in 1986 to establish centers to train cardiologists and scientists in molecular biology, has led to huge advances in knowledge and treatment of heart disease. Jane Ferguson:                 Finally, rounding out this issue, Kiran Musunuru and colleagues, representing the AHA Council on Genomic and Precision Medicine, Council on Cardiovascular Disease in the Young, Council on Cardiovascular and Stroke Nursing, Council on Cardiovascular Radiology and Intervention, Council on Peripheral Vascular Disease, Council on Quality of Care and Outcomes Research, and the Stroke Council, published a scientific statement on Interdisciplinary Models for Research and Clinical Endeavors in Genomic Medicine. Jane Ferguson:                 This paper lays out the field of cardiovascular research in the post genomic era, highlights current practices in research and treatment, and outlines vision for interdisciplinary, translational research and clinical practice, that could improve how we understand disease, and how we use those understandings to help patients. Jane Ferguson:                 Our guest interviewer today is Dr. Jennie Lin, an Assistant Professor at Northwestern Universities Feinberg School of Medicine, and the incoming Vice Chair of the Early Career Committee of the AHA Council on Genomic and Precision Medicine. As an aside, Jennie is a great person to follow on Twitter for insights into genomics and kidney disease, and as a bonus, she also posts the occasional dog photo. So she's well worth following just for that. You can find her on Twitter @jenniejlin. As you'll hear, Jennie talked to Dr. Beth McNally about her view on genomic medicine, and Beth also shared some really great practical tips for early career investigators building their independent labs. So make sure you listen all the way to the end. Take it away Jennie. Dr. Lin:                                  Thank you for tuning in to this edition of Getting Personal: Omics of the Heart, a podcast by the Genomic and Precision Medicine Council of the American Heart Association, and by Circulation: Genomic and Precision Medicine. Today I am joined by Dr. Elizabeth McNally, the Elizabeth J Ward Professor of Genetic Medicine, and director of the Center for Genetic Medicine at Northwestern University. Beth, thank you for taking time to chat with all of us. Dr. McNally:                       Happy to be here. Dr. Lin:                                  As a successful physician scientist, you have been interviewed in the past about your life, your scientific interests, and advice for budding investigators. I don't want to rehash everything you have already stated beautifully in an interview with Circ Res, for example. But instead wanted to focus more on your views of genetic medicine and genome science today. Dr. Lin:                                  So you mentioned in that prior Circ Res interview that you started your laboratory science training and career during college, when you participated in a project focused on genetic variation among children with muscular diseases. What have you found to be most interesting about the process of identifying functional genetic variants back then, and also that on-going work now. Dr. McNally:                       Well, I think over the years I've been doing this is the tools have gotten so much better, to be able to actually define the variants much more comprehensibly than we ever could in the past. And then also to be able to study them, and very much to be able to study them in context. And so I look at the revolutions in science that will cause people to look back on this era as the era of genetics. It began obviously with PCR, we couldn't have gotten anywhere without that. Dr. Lin:                                  Right. Dr. McNally:                       And then you leap forward to things like next generation sequencing, and IPS cells, and now CRISPR/Cas gene editing. And to realize that the last three happened within a decade of each other, is going to be so meaningful when you think about the next few decades, and what will happen. So being able to take an IPS cell and actually study a mutation or a variant in context of that patient, the rest of their genome, is really important to be able to do. Dr. Lin:                                  Okay, Great. And so, where do you envision ... with taking say for example, this next gen. technology, CRISPR/Cas9, studying variants in an IPS cell, for example. How do you envision this really revolutionizing the study of human genetics for patients? And how far do you think we've come in fulfilling that vision, and what do you think should be our focus going forward? Dr. McNally:                       I think broadly thinking about human genetics we're really very much still at the beginning, which I know is hard to say and hard to hear. But, we've spent a lot of the last 15 years very focused on that fraction of the genome that has high frequency, or common variation, through a lot of the GWA studies. With those common variants, we had a lot of associations, but relatively small effects of a lot of those, causing a lot of people to focus on the missing heritability and where we might find that hiding. And of course, now that we have deep sequencing, and we have deep sequencing where we've really sampled so much more of the genome, and from so many more people, I think we're just at the beginning of really appreciating that rare variation. And beginning again to really appreciate that 80-85% of the variation that's in each of our genomes is really characterized as rare. Dr. McNally:                       And so we really each are quite unique, and that when we understand a variant we do have to understand it in the context of all that other variation. So computationally that's very challenging to do. Obviously requires larger and larger data sets. But even in doing that, you are not going to find exact replicates of the combinations that you see in any one individual. While I know everybody would love that we're going to have the computational answer to all of this, it's still going to come down to a physician and a patient and making what you think is that best decision for the patient, based hopefully on some genetic data that helps inform those decisions. Dr. Lin:                                  Right, right. So it kind of gets into this whole concept of precision medicine, which has gained a lot of popularity and buzz in recent years, and Obama has really brought it to the forefront in the public arena. You mention rare variants in ... finding rare variants in each patient, for example. And moving a little bit away from some of the common variants that we find in GWAs. What does it mean for a patient to have a rare variant and come see you in your cardiomyopathy clinic, is it going to be precision for that patient, or suing rare variants among many different individual patients to try to find function for a gene? Dr. McNally:                       It's a great question. So I think the first way we approach it is, it depends who's asking the question. So if it's somebody who comes to me who has cardiomyopathy, or has a family history of cardiomyopathy and sudden death, that's a very different question to ask what's going on with their rare variants, for example in cardiomyopathy genes. Now if you translate that over to, I have a big population of people, I don't particularly know what their phenotype is, and I see rare variants for cardiomyopathy, those are two fundamentally different questions. So we very much know a lot about how to interpret rare variants for cardiomyopathy in the context of a patient or a family who has disease, and I do emphasize the latter part of that, the family, working with families and seeing how variants segregate within families. We interpret that very differently, and I think it's appropriate to interpret that very differently in that context. And that's completely different than again, going against what is the regular population, notice I'm not calling it normal population- Dr. Lin:                                  Right. Dr. McNally:                       ... but the general population that's out there. The first step in doing that is the list of the ACMG, American College of Medical Genetics, actionable genes. So this is an interesting question in and of itself. It's 59 genes, of course that list is too small, and it should be bigger than that, and ultimately that will happen. But to take a population based approach to those actionable genes, and looking across the population, finding someone who's got variants in, lets say our favorite genes MYH7, MYBPC3. Knowing what that risk means on a population level is very different than knowing what that means in the context of a patient who comes to you, who has that variant, runs in their family, and has clear disease. Those again, really two different questions, and we have to come up with what's the best practices on that, how to answer either of those questions. Dr. McNally:                       I think the first step working with patients and families who have known disease and have clear variants that segregate with disease, I think its very powerful. I think we've probably got close to a good decade of doing that already. It's incredibly useful for those patients and families. It helps us reduce their risk. It helps us treat them early, it helps us manage their arrhythmias. There's no question that that information is incredibly valuable, but we're still learning how to process that across the population, and how to answer that question for people who are coming who don't already have disease. Dr. Lin:                                  Right, right. That makes sense. And I guess that kind of plays into a follow up question about whether or not we need to test, or think about every variant of unknown significance in lab, and ... the- Dr. McNally:                       It's a great ... You know again, you always have to very carefully consider the context in which the question's being asked. So again, if you're talking about a relatively normal population, well, walking, healthy person, and you're seeing variants of uncertain significance, that's a very different question than somebody who's coming in to you with cardiomyopathy and has a highly suspicious variant of uncertain significance that falls right within the head domain of MYH7. We know a lot about that, and we can do a lot of interpretation in that case. Dr. McNally:                       However, I would say that to put too much value on what we do in the research lab ... Just putting a regulatory hat on for a second and thinking about it, there's nothing from a regulatory standpoint that really validates what we do in the research lab, to say that we can really fairly adjudicate a VUS or not. We can't do that, that's over-valuing what we do in research lab. Dr. McNally:                       So I think, how do we consider variants among certain significance? I think it's really important to recognize that it's exactly that, it's a variant of uncertain significance. And so when you're a clinician taking that to a patient, you have to approach it from the standpoint of saying, this is a variant of uncertain significance. Which means we don't know whether it's pathogenic, but we also don't know that its benign. Because I think right now what we've seen, a lot of clinicians, and even researchers, fall into the path of this believing that variant of uncertain significance is the equivalent of benign. That's not true. It is simply ... That is a rare variant, and we don't know whether it's pathogenic or non-pathogenic. And hopefully overtime we will learn more to better assess that, and better provide the interpretation of what that means in the context of that patient. Dr. McNally:                       It's a good conversation to have. It's important to recognize they're not necessarily pathogenic, but they're also not necessarily benign. Dr. Lin:                                  Mm-hmm (affirmative). So do you see a role, for example, when you see this variant of uncertain significance, is there a role to go back into lab, for example, and try to knock that mutation to IPSC's and test to see if its pathogenic? Or is that going a step too far? Dr. McNally:                       In some cases, that is the right thing to do. Because genetics is so powerful, genetics doesn't only give you the association of a gene with an outcome, and GWAs was fabulous at doing that ... giving us a lot of variants, and often nearby genes, sometimes far away genes, but linking genes to phenotypes, and that's very powerful. But specific variants can actually tell you a lot about mechanism, about how a gene and protein actually function, and how it functions when it's broken. And so, particularly where you can gain a lot from the research front in understanding mechanism, then I think it's really powerful to take those things to the laboratory and to use that to learn about mechanism. Dr. McNally:                       Sometimes you can do it to help adjudicate whether something's pathogenic or not, but again, I think we want to be cautious in doing that. Because what we do in the res ... I always like to say, "What we do in the research lab isn't exactly CLIA certified." Dr. Lin:                                  Right. Dr. McNally:                       There isn't anything magical about what we do, but we definitely ... It is so powerful what's available out there in terms of the genetic variants, and teaching us about how genes and proteins interact. And so I think it is such a rich resource of information right now. The things I bring back to the laboratory, and get my students and trainees excited about working on, is usually where I think we can gain something new about mechanism. Dr. Lin:                                  Right, right, right. Since you are a role model physician scientist, and you think about questions in lab that will ultimately benefit your patients, and you are a genetic cardiologist. What are your thoughts on doing genome editing as a possible therapy for your patients? It's a little bit of a loaded question [crosstalk 00:21:51], it's a little bit controversial. Dr. McNally:                       So I think, no doubt CRISPR/Cas9 gene editing is transforming what we do in the research setting. It's a fantastic tool. Is it a perfect tool? No. Anybody who has been using it a lot in the lab knows that it is much better than anything we've had before, but still quite limited in fidelity and efficiency. And so imagining that we are going to do that in patients is still pretty daunting to me. We do enough gene editing in cells to know that you have to select through an awful lot of cells before you get the one that has the exact variant you are trying to make. So that's not something we can tolerate in the human setting. But we're not there yet, we know that. Dr. McNally:                       Many of the disorders I see clinically are things that are autosomal dominant due to very precise single base-pair changes. And so envisioning how we're going to correct only one copy of an allele and do in a very precise manner, we don't have those tools available yet. Now on the other hand, if you look at a disease like one of the diseases I spend a lot of time on, Duchenne muscular dystrophy, where the majority of mutations are deletions. It's X-linked, it's male, so there's only one copy of the gene, and we know a whole lot about the structure and function of the gene. We know that if we take out this other part we can skip around that mutation and make an internally truncated protein. That's actually a very good use of gene editing, because it only requires making deletions. They don't need to be very precise, and there's only one copy of it that you have to do the gene editing on. Dr. McNally:                       So I see that being something in the near term that will happen, simply because the genetics positions it well to be something where that could be successful. The hard part is still how are we going to get the guides, and how are we going to get the Cas9 in safely into all the cells that need to be treated? And ultimately that lands us back at looking at what our delivery vehicles are. Which at this point in time is still viral delivery, and still has a lot of issues around can we make enough of it? Are people immune to it? So all those questions that come with viral delivery. So still lots of hurdles, but you can see some paths where it makes sense to go forward. Dr. Lin:                                  Very interesting. Okay great. Well thank you for providing your thought on human genetics and genome science. We're going to switch gears for the last portion of this podcast, and talk about your thoughts on career development issues for young investigators. At a recent AHA Scientific Sessions meeting, you participated on a panel that was assembled to provide advice to early career scientists. When you were starting out, what were some of the biggest challenges you faced when you were transitioning to independence and building your own lab, and what's your advice to those facing the same challenges today? Dr. McNally:                       Well, even though I did it quite a few years ago, many of the things are still the same. Transitioning to independence, I think is easier if you pick up and move and start in a new place. I think it's much easier to establish your independence when you're not in the same place as your mentor. That said, we have many more people who now stay in the same place as where their mentors were and we have many more approaches towards doing that. So I think people are much more open to both possibilities as being ways of doing that. But at some level it still comes down to starting your own lab, and you hopefully have been given some start-up resources and you have to think about how to wisely spend them, and how to really get things going. I don't think this is changed either. Dr. McNally:                       I usually tell people, don't just start in one area, if you can, start in two areas because things don't work, and sometimes things do work. In reality when you look at people who are successful, they're often working in more than one area. And so the sooner you start getting comfortable working in more than one area, that's a good thing. Now ideally, they should be areas that have some relationship to each other, and then feed each other in terms of information so that they grow off each other. But what does that practically mean? I always say, "Well if you can hire two people and start them on two different paths, that's a really good way to get going." And practical things like look at all different kinds of private foundations and things like that for getting some good pilot start up money to help develop new projects in the lab. And always be looking at how can these projects help me develop a bigger data package, that's going to put me in a good competitive position for example bigger grants and federal funding, and things along those lines. Dr. McNally:                       Very much a stepwise process. People want to shoot for the moon and get the biggest things first, but sometimes just focusing on the smaller steps which are definitely achievable and building your path towards those bigger steps is the smarter way of doing it. Dr. Lin:                                  That's great advice. You also mentioned recently that young investigators should try to have as many mentors as possible. What advice would you have for, in particular, early career genomics investigators, for finding these mentors passed the postdoc phase? Some of us get introduced at the postdoc phase to maybe some other collaborating labs, but those are really collaborations of our mentors per se. Dr. McNally:                       Well I think especially in the field of genetics and genomics, collaboration is key, and I will say one of the things that has changed over since I started doing this is there is a lot more understanding of the need to collaborate. Not so many years ago, it wasn't really an independent investigator went and started a lab, and it would be your trainees and the papers would have only those people on it. Dr. McNally:                       I think these days, the best science is where you've tackled a problem from multiple different directions, one or two of those being genetics, genomics directions. And then sometimes there's other ways that you've approached that scientific problem. And by necessity, that usually involves collaborating with other people. And your role is sometimes to be the coordinator of all those collaborators, and that's where again you might be in a senior author position then doing that. But your role sometimes is to be the good collaborator. And so when I look at people being successful right now, seven, eight years in to running their own lab, I like to see that they've been the organizer of some of those, that they've collaborated with people who are even senior to them, and that they've established those good collaborations, but that they've taken the leading role in doing that. But also that they've had middle author contributions, that they've been a good collaborator as well. Dr. McNally:                       And so, part of that is not being afraid to collaborate, and to recognize the value of doing that. And what's so great about doing that is you can collaborate with people at your same institution where you are, but you can also collaborate with people all over the world, and I think that's what we do. You go to where you need somebody who is using a technique or an approach that really helps answer the question you want to have answered. And so that's reaching out to people and really establishing again that network and good collaborators which you can do by a whole bunch of ways. You can do it by meeting somebody at a meeting, scientific meeting. You can do it through emails, phone calls, Skype, all sorts of different ways that you can reach out and collaborate with people. Dr. McNally:                       It is easier than ever to share data and share ideas, but that negotiation of how to establish the terms of the collaboration and how to make it be successful is a critically important part of being a scientist. And what we now know when we look at the promotion process, is people who do that effectively, that's a really important mark of being a successful scientist, and marks them as somebody who should be promoted through the process. So great. Dr. Lin:                                  Yeah. No I agree. Certainly with the direction science is moving, it's definitely very difficult to work in a siloed manner. Dr. McNally:                       Yeah. Well you won't get very far. You'll be able to have some really good first ideas, and show some proof of principle approaches. But to really, really address an important scientific problem, we know that you have to see those signals using multiple different methods. And once you have five different ways showing you that that's the right answer, then you're much More confident that you've gotten to the right answer. Dr. Lin:                                  Right. Alright, so I think we're going to wrap up. Do you have any other final thoughts for any other young investigators or genomics researchers listening to this podcast? Dr. McNally:                       It's a great time to be doing genetics and genomics, and particularly human genetics, where we now finally have all this information on humans, and we'll have even more of it in the future. So I think humans are coming close to becoming a real experimental system. Dr. Lin:                                  Excellent. Alright well thank you so much for your time. It was a pleasure having you on this podcast. Dr. McNally:                       Great. Thank you for doing this. Jane Ferguson:                 As a reminder, all of our original research articles come with an accompanying editorial, and these are really nice to help give some more background and perspective to each paper. To read all of these papers, and the accompanying commentaries, log on to circgenetics.ahajournals.org. Or, you can access video summaries of all our original articles from the circgen website, or directly from our YouTube channel, Circulation Journal. And lastly, follow us on Twitter @circ_gen, or on Facebook, to get new content directly in your feed. Jane Ferguson:                 Okay, that is it from us for June. Thank you for listening, and come back for more next month.  

Jane Ferguson:                 Hi, everyone. Welcome to Getting Personal: Omics of the Heart. This is podcast episode 16 from May 2018. I'm Jane Ferguson from Vanderbilt University Medical Center and this podcast is brought to you by Circulation Genomic and Precision Medicine and the AHA Council on Genomic and Precision Medicine. Jane Ferguson:                 This month we talked to Dr. Caitrin McDonough from the University of Florida. We briefly mentioned her paper in last month's episode Genetic Variants Influencing Plasma Renin Activity in Hypertensive Patients From the PEAR Study, but we wanted to go into it in more depth this month. Caitrin shared with us that this manuscript actually resulted from student course work and was a collaborative effort between students and instructors. The manuscript highlights has successful as approach can be both in increasing student engagement and as an effective way to do high quality research. You can hear her talk more about her innovative approach to student learning and the study findings later in this episode. Jane Ferguson:                 Of course, we have a great lineup of papers in Circulation Genomic and Precision Medicine this month. First up, a paper entitled, "SCN5A Variant Functional Perturbation and Clinical Presentation Variants of a Certain Significance" by Brett Kroncke, Andrew Glazer, and Dan M. Roden and colleagues from Vanderbilt University Medical Center. They were interested in investigating the functional significance of variants in the cardiac sodium channel in particular to see if they could explain why some variant carriers present with cardiac arrhythmias while others remain asymptomatic. Through a comprehensive literature search, they identified 1712 SCN5A variants and characterized the carriers by disease presentation. Variants associated with disease were more likely to fall in transmembrane domains consistent with the importance of these domains for channel function. Jane Ferguson:                 Using American College of Medical Genetics Criteria for variant classification, they found that variants classified as more pathogenic were also more penetrant. Penetrance was also associated with electrophysiological parameters. This approach highlights how modeling the penetrance of different variants can help define disease risk for individuals who carry potentially pathogenic variants. Jane Ferguson:                 Next we have a paper from Vincenzo Macri, Jennifer Brody, Patrick Ellinor, Nona Sotoodehnia and colleagues from the University of Washington and Massachusets General Hospital. This is also related to sodium channels and the paper is entitled, "Common Coding Variants in SCN10A Are Associated With the Nav1.8 Late Current and Cardiac Conduction". They were interested in SCN10A and sequenced this gene in over 3600 individuals from the CHARGE consortium to identify variants associated with cardiac conduction. They were able to replicate associations between variants and PR and the QRS intervals in a sample of almost 21,000 individuals from the CHARGE Exome sample. They identified several missense variants have clustered into distinct haplotypes and they showed that these haplotypes were associated with late sodium current. Jane Ferguson:                 Continuing the cardiac conduction theme, Honghuang Lin,  Aaron Isaacs and colleagues published a manuscript entitled, "Common and Rare Coding Genetic Variation Underlying the Electrocardiographic PR Interval". They conducted a meta-analyses of PR interval in over 93000 individuals which included over 9000 individuals of African ancestry. They identified 31 loci, 11 of which have not been reported before. We see SCN5A come up again as a gene of interest in this study but their analyses also implicated a novel locus, MYH6. Jane Ferguson:                 Next up moving from the heart to the vasculature, Janne Pott, Markus Scholz and colleagues from the University of Leipzig published a manuscript entitled, "Genetic Regulation of PCSK9 Plasma Levels and Its Impact on Atherosclerotic Vascular Disease Phenotypes". They were interested in whether circulating PCSK9 can be used as a diagnostic or predictive biomarker. To address this, they conducted a GWAS of plasma PCSK9 in over 3000 individuals from the LIFE-Heart study. They found that several independent variants within the PCSK9 gene were associated with plasma PCSK9 as well as some suggestive variants in another gene locus FBXL18. They used Mendelian randomization to probe causality and the data suggest that PCSK9 variants have a causal role in the presence and severity of atherosclerosis. Jane Ferguson:                 Moving on to another biomarker, Lisanne Blauw, Ko Willems van Dijk and colleagues from the  Einthoven Laboratory for Experimental Vascular Medicine report on CETP in their manuscript Cholesteryl Ester Transfer Protein Concentration A Genome-Wide Association Study Followed by Mendelian Randomization on Coronary Artery Disease. They aimed to assess potential causal effects of circulating CDP on cardiovascular disease through GWAS and Mendelian randomization. Jane Ferguson:                 In a study of over 4000 individuals from the Netherlands Epidemiology of Obesity Study, they identified three variants in CTP that associated with plasma levels of CETP and explained over 16% in the total variation in CDP levels. Genetically predicted in CETP was associated with reduced HDL and LDL cholesterol suggesting that CETP may be causally associated with coronary disease. Jane Ferguson:                 Rounding out the table of contents we also have a clinical case perspective from Nosheen Reza, Anjali The Importance of Timely Genetic Evaluation in family members in cases of cardiac disfunction and cardiomyopathy. We have a report from Adrianna Vlachos, Jeffrey Lipton and colleagues on the Diamond Blackfan Anemia Registry and we have a clinical case from Yukihiro Saito, Hiroshi Ito and colleagues on TRP and poor mutations in patients with ventricular non-compaction and cardiac conduction disease. Jane Ferguson:                 To read all of these papers and the accompanying commentaries, log on to circgenetics.aha.journals.org and if you're a visual learner or you need a work related excuse to spend time on YouTube, you can also access video summaries of all our articles from the CircGen website or directly from our YouTube channel Circulation Journal. Lastly, follow us on Twitter at circ_gen or on Facebook to get new content directly in your feed. Jane Ferguson:                 I'm joined today by Caitrin McDonough from the University of Florida and Caitrin is an Assistant Professor in the Department of Pharmacotherapy and Translational research in the College of Pharmacy and she's the first author on a recently published manuscript entitled, "Genetic Variants Influencing Plasma Renin Activity in Hypertensive Patients From the PEAR Study". This was published in the April 2018 issue of Circulation Genomic and Precision Medicine. Welcome, Caitrin. Caitrin M.:                           Thank you. Jane Ferguson:                 For listeners who haven't had a chance to read the paper yet, I wonder could you give us a brief overview of what prompted you to do this study? Caitrin M.:                           Sure so this looks at plasma renin activity and just initially a GWAS but it was done in a hypertensive population from the pharmogenomic evaluation of antihypertensive responses study. Particularly, since our group here at the University of Florida is more interested in pharmacogenomics we wanted to address plasma renin since it can influence blood pressure response to antihypertensive medication particularly if you use it as something to predict but also to correlate it with that as there have been also prior data from our group that shows if you have different levels of plasma renin that would predict if you would respond better to certain types of antihypertensive medications such as a beta-blocker or a diuretic. Caitrin M.:                           We used both a GWAS approach as well as a prioritization through blood pressure response to focus in on signals and then furthered by using prioritization using data from RNA seq and looking at eQTLs and then finally looking at more of just a traditional net replication of the original plasma renin activity signal. Caitrin M.:                           Overall, one of the interesting things and why we were initially doing this study was really in connection with a graduate course that myself and another faculty member here who's also an author on the paper, Yan Gong [inaudible 00:09:12]. We often have the students analyze data from the PEAR study as we have a lot of data from that study and it helped us analyze additional papers but we didn't necessarily know if this was going to be an interesting phenotype but through that course work which turned out that it really did have some interesting signals so we wanted to follow up more on. Jane Ferguson:                 Yeah, I love that approach so I think that's a really smart way to do it. To actually get your students to analyze your data and get them really involved in the process. How much then did the students ... how much were then they able to get involved when it started transpiring that their results would actually be something that could be put together for a manuscript? Caitrin M.:                           Overall, they are fairly involved. During the course work, what we usually do is give them just directly types data since a lot of them have not done this type of genetic analysis before and we split it up where each student gets about four to five chromosomes of data and then different phenotypes in the different race groups as we have both whites and African Americans. They get a certain race group, certain number of chromosomes and so they're able to conduct the analysis just using the Uplink software which is fairly user-friendly and straightforward. Then they get experience making Manhattan plots and using LocusZoom. Caitrin M.:                           After they have the basic techniques, then we teach them how to start following up top signals and determine what is a good signal. They're looking at the LD or SNP function or possibly gene function or looking at their genotype, phenotype relationships and making sure that it's not just one person who's driving the whole signal. Then selecting what top reasons and top SNPs may need a follow up. That part they all do there in the class and learn more of the basics. Caitrin M.:                           After the class, the students who want to continue to participate we get together and redistribute data where they would then move on to working on the imputed data sets and we teach them how to do that. Then we give them ... operate it somewhat similar to a consortia level meta-analysis type thing. I write up an analysis plan, each student does some part of the analysis. They have to bring it all back to me. I sort through it. We meet and go through it. Then we set our next steps to follow up. Then different students get different SNPs to investigate the function of or different subanalyses to do. Caitrin M.:                           One of our graduate students who is on this particular project, her dissertation project was very focused on our RNA seq data so that was how we were able to bring in the eQTL analysis using the RNA seq data as she had done a lot of the groundwork with that already. In one of our discussions that was one of the ways that we were able to incorporate the prioritization since she was intimately familiar with that data set. Jane Ferguson:                 Yeah and I think that's great. I can imagine that, that's a much more compelling way for students to learn about how to analyze data when they see the natural follow through. Do you find that some of the students maybe get more excited about research or are more likely to pursue future research opportunities by having had this hands on experience with the publication process and completing a project really did to this very end? Caitrin M.:                           They do, yeah. I see some of the students that end up sticking with it more are the students who I work more closely with and see more closely some of the students who are from other departments still stay involved but sometimes don't stay quite as involved. But, all of them really do continue to follow up and ask if they can still help or if there's anything they need to do until we get it to publication which is really nice. Jane Ferguson:                 Yeah, right. I think that's fantastic and I'm sure every study has its challenges. I'm interested what were the challenges you encountered in doing this study and which one of them may be unique to the way you have a lot of different people analyzing different aspects of the data versus the regular challenges that would come up in a study like this. Caitrin M.:                           Yeah so some of it I think is just keeping everyone on track and keeping it organized, making sure I think some of our challenges with this study was just me making I think on a lot of other studies, while I had certainly hands on the data it was more of an oversight rule for some pieces of it and just making sure everything looked the way that I thought it did, double checking. Some of it I think the teaching aspect of it just making sure everything was also done correctly and then keeping everything organized made the study a little bit more challenging. Caitrin M.:                           I think part of it too was with the PEAR study, it is a very rich data set. Determining what we wanted for our prioritization scheme and how to work through the different types of data sets that we had and put it all together as initially we just assign each student a different piece and we had a vague plan but it was a little bit more tricky as to work through how it was all coming together then when everyone came back together since a lot of people were doing as opposed to just one person doing it. Jane Ferguson:                 Right, so yeah and I think you're touching on the part that all of us have when we're writing papers that you sometimes end up with a lot of data at the beginning, you're trying to sift through it and then sometimes at a certain point you see something and you're like, "Okay, yes. This is interesting." Then you start following it up. Jane Ferguson:                 I wonder at what point did that happen? I suppose you probably ... You ran the GWAS for plasma renin activity and then find a number of suggested SNPs that were significant you associated but then ... Describe your strategy and you did so the second screening stuff to look at the pharmacological aspect defining [crosstalk 00:15:12]? Caitrin M.:                           Yeah, our initial plan going in was the first two steps, to do the GWAS for plasma renin activity and then to do the prioritization through blood pressure responses. I was very familiar with what our lab was familiar with but then after we got there, I think we were then troubled with what we did next and where to go. When we decided to bring in the RNA seq data, I think that was when it really started coming together as our top signal, the SNN-TXNDC11 gene region really stuck out then and it showed up. That seemed like a much stronger signal and it gave us a little bit more focus and also brought it much more of a functional aspect where we would maybe start to believe that signal more. That I think was really when we did that more of a turning point for the study and helped us focus more on where then to go with the results. Jane Ferguson:                 As far as the data you had I think over 700 people for your GWAS. Then you had a pretty large number of ... Was it the same subjects or different subjects where you also had the RNA seq data to do the QTL analysis? Caitrin M.:                           The same subject so not everyone has RNA seq. We have RNA seq data on 50 individuals and they were selected from whites and at the extremes of the blood pressure response so that it has a slightly interesting selection process. It's the main data analysis there was a best responder, worse responder to thiazide diuretics. Caitrin M.:                           When we do the eQTL analysis, we aren't always sure what we're going to get since we're missing the middle of blood pressure response. But, when we're just looking strictly at eQTL analysis sometimes we get lucky and sometimes it looks weird. Jane Ferguson:                 In your case as well you had the added issue of subjects were randomized to drug treatment so it was some where responders were ... I guess some people got the drug that worked for them and some people did not get the drug that worked for them. Caitrin M.:                           Yeah. Jane Ferguson:                 Did you I guess were incorporating both groups so good responders to either and some of that was because of their gene. They got the right drug for their genotype. Caitrin M.:                           Exactly, yeah. Jane Ferguson:                 It's good and then you were able to replicate this. After you were able to prioritize your gene region based on the GWAS and the drug response and the eQTL data, you actually ended up being able to go to a second sample to replicate the association right? Caitrin M.:                           Yes, it was in a lot of the same investigators, we have a second study PEAR-2, which has a very similar design to the PEAR study but used different drugs but also collected baseline plasma renin activity. We were able to use that phenotype again. We did have slight differences in GWAS to imputation panels at that point in time for when we were conducting this study so we ended up using a proxy to replicate but we did see the same signal in the second population which was very nice to see. Jane Ferguson:                 What is known about this gene region or either of these two genes? Caitrin M.:                           Overall, that was I think one of our harder points when we started trying to make the connections back to our phenotype. This was one of the areas where we did also have help from our students and the students as that was part of their initial training where they really looked to see what function was of various different genes and how to follow them up. That was one area where they came in, was to help look up some of the function of these ... there have been some connections with the various genes, the other phenotypes and with SNN and to atherosclerosis and other inflammatory cytokines such as TNF-alpha. Then there have been data also from [inaudible 00:19:37] that really show that there is an eQTL in this region that which supports what we saw in our own data. However, there really wasn't any direct connections with renin and the renin angiotensin aldosterone system and blood pressure regulation that we could find in the literature. Caitrin M.:                           We're not exactly sure how it connects but based off of our functional data and levels of evidence and then we saw some of that in publicly available data, we're still very interested in the region. Jane Ferguson:                 I think the data is compelling enough that it looks like you've identified the new region that probably is the mechanistically related that will require a whole bunch of basic mechanistic research to figure out what exactly the genes in this region are doing and how this ultimately connect back to blood pressure and response to drug therapy. Caitrin M.:                           Exactly. Jane Ferguson:                 I could see this ... I mean obviously there's a whole lot of potential functional work there and then probably also the clinical work, I wonder what you think about how this would affect any pharmacogenetic therapeutic ... You know at present I think you can look at plasma renin activity and use that as a predictor of drug response to help guide therapies. Would you think that a genotype guided therapy may end up being more effective than the plasma renin activity measurement in this case? Caitrin M.:                           In this case since this is so connected with a phenotype that you could use with plasma renin, I think if you're able to draw a plasma renin you may just want to do that. I think our overall goal would be if someone had preexisting genetic data and you weren't wanting to do an additional test or if you're contemplating response to a lot of different drugs that perhaps you could use a genetic data. One of the issues that was brought up on review and that are a lot of group considers quite a lot is that we have a lot of signals and that our group has certainly published a lot in this area and there's a lot of signals that we have to a lot of different drugs and how do you incorporate all of them together, is there overlap between them or where do they all fall? Caitrin M.:                           That is certainly something that we're still working on as more I think ultimate goal would be more to delve more of a SNP score or gene score and some type of risk score that would help you determine what drug you would best respond to. We've done that a little bit in some of our prior publications but we haven't yet taken all of our data together and help to build something that would if you had a lot of data on an individual and various different alleles at various different genes, how that would respond. Caitrin M.:                           Overall, when we look at blood pressure response as a pharmacogenetic signal, certainly we see larger affect sizes than you would in disease genetics but we're not seeing affect sizes like you would with more of an adverse drug event. We're in between there and we're often times it's not necessarily just going to be one SNP or one gene that would tell your whole story but a combination of quite a few of them. Jane Ferguson:                 I wonder are there more similar stories like this from the same data set? You know you've been through this process from start to finish and building in the functional work and do you think that next year's class will be able to do this again with the same data set? That maybe pick one of the next priority candidate down the list and maybe find another interesting story like this? Caitrin M.:                           Yeah, so we actually just finished our class this year and they looked at potassium. We just got done grading final papers and submitting grades so we will over the summer be working with them a little bit more. I think some of our new graduate students too are starting to work on trying to make more connections between a lot of our different phenotypes and as you start to layer those together what it exactly means for a patient or implementation perspective. Jane Ferguson:                 Yeah, interesting. We'll have to look for that story whenever you guys get done with it. Otherwise, are you planning on following up this specific SNP region in any other way or any other studies? Where's next for you guys overall? Caitrin M.:                           I think one of the things we would like to do is look at this more in PEAR-2. We really just brought the PEAR-2 data set in here as replication of the top region in that last stage but we have that data set and we can certainly look at that data set. Caitrin M.:                           The other thing that I would like to do is as we started this project in conjunction with the class that was a couple of years ago at this point in time, we used [TAP/MAP 3 00:24:35] imputed data since that was what we had in the lab and what we were using at that point in time. At this point in time, we have now imputed both PEAR and PEAR-2 2000 genomes phase three data. It'd be interesting to see if we are able to see any additional signals or if these regions become stronger or exactly what would happen using a more imputation panel that has more coverage and where we would have the same panel between both PEAR and PEAR-2. Jane Ferguson:                 Right because you may or may not have identified the causal SNPs in the previous access but- Caitrin M.:                           Yeah. Jane Ferguson:                 -yeah so it'd be nice to see if you can actually get that out. That potentially could end being a drugable target maybe suitable for something more specific but who knows. Is there anything else that we haven't covered yet that you'd like to mention? Caitrin M.:                           Overall, I think that just this type of model of utilizing more of a real world analysis and data in a class project really certainly engages our students a lot and I think they all enjoy actually being able to work with data that came out of this study and have a lot more hands-on experience and really project-based analysis experience. We've been very happy with this model and have used it multiple times. We have an HDL paper, the renin paper, our glucose response paper and now we're working on the potassium project. It's been a good model for us here with our pharmacogenomics class. Jane Ferguson:                 Yeah, I mean I think it's a really smart and intuitive way to think about education. It's mutually beneficial it sounds like, so it's helping you guys get your data analyzed. It's really helping the students learn so I think it's a win-win situation. I think it's a model that a lot of other people would really be interested in adopting. Caitrin M.:                           Yeah. Jane Ferguson:                 Okay well thanks so much for talking to me and talking about your model and your research. It's been great. Caitrin M.:                           Yes, thank you very much for having me. Jane Ferguson:                 That's it from us for May. Thank you for listening and come back for more next month.  

Jane Ferguson:                 Hello, welcome to Getting Personal: Omics of the Heart. This is podcast Episode 15 from April 2018. I'm Jane Ferguson, an Assistant Professor of Medicine at Vanderbilt University Medical Center, and this podcast is brought to you by Circulation Genomic and Precision Medicine and the AHA Council on Genomic and Precision Medicine.                                                 As usual, we have a great lineup of papers in Circ Genomic and Precision Medicine this month. The first is actually the subject of our interview this month. Sony Tuteja talked to Craig Lee from the University of North Carolina about his manuscript entitled, "Clinical Outcomes and Sustainability of Using CYP2C19 Genotype Guided Antiplatelet Therapy After Percutaneous Coronary Intervention." This manuscript investigated the use of pharmacogenomics to improve treatment after PCI, and you can hear a lot more about it directly from the first author later in the podcast.                                                 Our next manuscript also used pharmacogenomics approaches to look for snips associated with plasma renin activity and to assess the effect of top snips with blood pressure response to atenolol and hydrochlorothiazide. The first and last authors are Caitrin McDonough and Julie Johnson from the University of Florida. And their manuscript is entitled, "Genetic Variants Influencing Plasma Renin Activity in Hypertensive Patients from the Pharmacogenomic Evaluation of Antihypertensive Response," or PEAR study. They find that snips in the SNNTXNDC11 gene region associate with higher baseline plasma renin activity in their sample of over 700 subjects and with a smaller systolic blood pressure reduction to hydrochlorothiazide. Variation in the region may act through modulation of TXNDC11 gene expression. They also identified several other candidate genes of interest. These new candidates may allow for precision medicine approach to selection of hypertensive treatment and further study the mechanisms may reveal novel biology on blood pressure response to pharmacological treatment.                                                 Next up is a manuscript by Deirdre Tobias and colleagues entitled, "Circulating Branch Chain Amino Acids and Incident Cardiovascular Disease in a Prospective Cohort of U.S. Women." I actually had the chance to talk to Deirdre about her research last month. So check out the March podcast, Episode 14, to hear more from Dr. Tobias about this study.                                                 A study of hypertrophic cardiomyopathy from Hannah [inaudible 00:02:36] and Michelle Michels and colleagues from the Erasmus Medical Center in the Netherlands assessed the effects of genetic screening in family members of patients with a known hypertrophic cardiomyopathy mutation. In their manuscript entitled, "Outcomes of Contemporary Family Screening and Hypertrophic Cardiomyopathy," they described their study which assessed cascade screening in 777 relatives of 209 probans between 1985 and 2016. Genetic and clinical screening resulted in a diagnosis of HCM in 30% of family members at the time of testing. An additional 16% of family members developed HCM over seven years of follow up. Of the 43% of family members who were genotype positive, 37% were ultimately diagnosed with HCM. There was no difference in survival between genotype positive and genotype negative family members or with relatives who did not undergo genetic testing.                                                 There are genetic considerations that are unique to the ancestral composition of the Netherlands with a high proportion of individuals with a founder mutation, so the proportion of probans with identified mutations is higher than in other reported studies. This paper demonstrates the potential benefit of genetic screening in family members, which can identify individuals who should undergo intensive screening, and at the same time reduce concerns for family members who are genotype negative. However, the classification of the pathogenicity of variants and understanding variable penetrance remains a challenge.                                                 A manuscript entitled, "Exome Sequencing in Children with Pulmonary Arterial Hypertension Demonstrates Differences Compared to Adults." From Na Zhu, Claudia Gonzaga-Jauregui, Carrie Welch, Wendy Chung, and colleagues from Columbia University, ask the question whether there were differences in the genetic mutations responsible for early onset pulmonary arterial hypertension, or PAH, in a pediatric sample compared with adult onset disease. While some mutations, particularly in BMPR2 appear to be similar in the pediatric and adult samples there were significantly more mutations in TBX4 in the children compared with adults.                                                 Further, children were more likely to have de novo mutations identified through exam sequencing that were predicted missense variants. Given the additional complications associated with pediatric onset of PAH, understanding the genetic differences in this population is an important step towards identifying novel genes and mechanisms which could guide future therapeutic development.                                                 Our next manuscript authored by Iisan Kadhen, Carolyn Macdonald, Mark Lindsay, and colleagues from Harvard Medical School is entitled, "Prospective Cardiovascular Genetics Evaluation in Spontaneous Coronary Artery Dissection," or SCAD. They genotyped individuals with SCAD to find out the genetic contribution to the disease. Of the patients for whom genetic testing was performed, six of them were 8.2%. Identifiable mutations in genes known to be involved in vascular disease, including COL3A1, LMX1B, PKD1, and SMAD3. These individuals were significantly younger at the time of their first SCAD event compared to patients with no identifiable mutation. Given the relatively higher rate of mutations identified in this sample, there may be a rationale to conduct genetic testing in all individuals presenting with SCAD, particularly in younger individuals.                                                 Shiu Lun Au Yeung, Maria-Carolina Borges, and Debbie Lawlor, from the University of Hong Kong and the University of Bristol, set out to find out if reduced lung function is causal in coronary artery disease. As reported in their manuscript, entitled "The Association of Genetic Instrumental Variables for Lung Function on Coronary Artery Disease Risk, A 2-Sample Mendelian Randomization Study," they used a Mendelian Randomization approach to assess causal relationships between two measures of lung function. Forced expiratory volume in one second, and forced vital capacity on CAD. Genetic predictors of increased forced expiratory volume were associated with lower risk of CAD. While there was a similar association with forced vital capacity, this was attenuated in sensitivity analyses. Overall, the data suggests that higher forced expiratory volume may independently protect against CAD. However, the mechanisms remain unclear.                                                 Finally, the April issue also contains a white paper from Kiran Musunuru, Xiao-zhong Luo, and colleagues entitled, "Functional Assays to Screen and Dissect Genomic Hits, Doubling Down on the National Investment in Genomic Research." This paper lays out strategies to followup on findings from high-throughput genomic analyses, including the use of novel technologies, assays, and model systems that can help to effectively translate big data findings and capitalize on previous investment in genomic discovery.                                                 To see the latest issue of Circulation Genomic and Precision Medicine, and to access all the papers we talked about and to browse previous issues, go to "circgenetics.ahajournals.org." Sony Tuteja:                       Hello, my name is Sony Tuteja, I'm an assistant Professor of Medicine at the University of Pennsylvania in Philadelphia, I'm also an early career member of the American Heart Association Council on Genomic and Precision Medicine. Today I'm joined by Dr. Craig Lee, an associate Professor of Pharmacy at the University of North Carolina School of Pharmacy. Dr. Lee is a first author of an article published in April 2018 issue of Circulation Genomic and Precision Medicine entitled, "Clinical Outcomes and Sustainability of Using CYP2C19 Genotype Guided Anti-Platelet Therapy After Percutaneous Coronary Intervention." Welcome Dr. Lee, and thank you for joining me today. Craig Lee:                            Thanks for having me. Sony Tuteja:                       First let me just say congratulations on spearheading such impactful work on the implementation of CYP2C19 pharmacogenetic testing. Craig Lee:                            Thanks, this has been a very complicated project, but a lot of fun. Sony Tuteja:                       Great. So I think some of our listeners may have not had time to read your paper yet so I was wondering if you could provide a brief overview of the paper and what the study was about. Craig Lee:                            Sure. Although it's been widely described that loss of function polymorphisms in the drug metabolizing enzyme, CYP2C19, which is responsible for the bio-activation of the antiplatelet drug clopidogrel, impairs its effectiveness, there remains considerable debate and uncertainties surrounding whether CYP2C19 genetic testing should be used clinically for guiding antiplatelet therapy in percutaneous coronary intervention, or PCI patients. As the evidence base is expanded, an increasing number of institutions are seeking to implement CYP2C19 genetic testing despite limited data on the use and impact of using this genetic testing to guide antiplatelet therapy selection following PCI in real world clinical settings.                                                 UNC was an early adopter for CYP2C19 genotype-guided antiplatelet therapy in high-risk PCI patients. Our algorithm recommends that patients carrying one or two loss of function alleles in CYP2C19 be prescribed an alternative antiplatelet therapy such as prasugrel or ticagrelor. Our algorithm was implemented back in the summer of 2012, under our then-director of the Catheterization Laboratory, and now Chief of Cardiology, Dr. Rick Stouffer. We conducted the study to better understand the feasibility, sustainability, and clinical impact of using CYP2C19 genetic testing to optimize antiplatelet therapy selection in PCI patients in real-world clinical practice.                                                 Basically what we did was following the implementation of our algorithm in the summer of 2012, we've been retrospectively collecting data from all patients that come through our Cath lab that undergo a PCI. We collect information on their clinical characteristics, whether or not they underwent CYP2C19 genetic testing, what antiplatelet therapy they were prescribed when they were in the hospital at discharge and over the course of followup, and more recently we've been assessing clinical outcomes, both ischemic outcomes and bleeding outcomes. The data presented in our paper described the algorithm's use at our institution over the first two years following its implementation from 2012 to 2014 with one year of followup data. Since we do about 600 PCI procedures per year on our Cath lab, the study population is just under 1200 patients.                                                 Our main findings were that CYP2C19 genotypes were frequently ordered, efficiently returned, and routinely used to guide antiplatelet therapy selection after PCI over this two year period. However, we also observed that the frequency of genotype testing and frequency of using alternative therapy such as prasugrel or ticagrelor in the patients that carried CYP2C19 loss of function alleles fluctuated over time. We also observed that use of clopidogrel in patients that were tested, but carried either one or two copies of a CYP2C19 loss of function allele was associated with a significantly higher risk of experiencing a major ischemic cardiovascular event compared to use of alternative therapy. These risks were particularly evident in the highest risk patients, and largely driven by patients who carry only one copy of the loss of function allele, the so-called intermediate metabolizers.                                                 Our primary takeaway from this analysis is that implementing a genotype-guided antiplatelet therapy algorithm is feasible, sustainable, and associated with better clinical outcomes in a real-world clinical settling, but challenging to maintain at a consistently high level over time. Sony Tuteja:                       Great. I know it's always challenging to implement new work flow and new testing into the clinical setting. Can you describe how the algorithm was incorporated in the cardiologist workflow to minimize disruption? Craig Lee:                            Absolutely. This algorithm was spearheaded by our interventional cardiologists with the support of our clinical pharmacy specialists and pathology laboratory. They key element to our success is that we have the capacity to do the genotype testing in our molecular pathology lab on site. Dr. Karen Weck is the director of that laboratory and is a coauthor on our paper. Since the prescribing decision for antiplatelet occurs in a highly specialized clinical setting, we have all the pieces in place to do this in-house at UNC, which seems to make things very efficient.                                                 There really wasn't very much disruption in the workflow given that the testing is done on-site and the test seems to be treated like another laboratory test that's done, which is really the ultimate goal of pharmacogenomics. We don't currently actually have clinical decision support built into our electronic health record, so the reason we could actually get this off the ground was because of the substantial collaboration between our physicians, pharmacists, and pathology lab.                                                 But one of the things we learned through this experience, which is described in the paper, is that there are fluctuations in the use of the genetic testing to guide prescribing over time that we believe could be remedied by developing more automated clinical decision support, to help make things a little bit more efficient for the clinicians. But at the start of it, it was really just a will to do it, which was really exciting to observe. Sony Tuteja:                       Absolutely. That's exciting that everybody was on board with this project. What do you think were the most challenging aspects of the implementation? Craig Lee:                            That's a great question, and one that often comes up. I think that the education on the front end is really, really important. It needs to recur as the implementation spans over a period of time. For example, there's turnover in the interventional cardiology fellows every summer as well as occasional turnover of attending physicians and clinical pharmacy specialists. As individuals come and go into the clinical environment, it is important that they understand how the algorithm works, and how it can be applied in practice. And this is accomplished by recurring education and communication.                                                 The other thing that's been a challenge is turnaround time. Even though our molecular pathology lab typically turns tests around within one day of a PCI procedure, if the test result isn't available or the antiplatelet therapy isn't changed in response to the genetic test before the patient is discharged from the hospital, we found that it can be challenging to followup on the result before the next encounter. Typically, if a change in medication needs to occur after discharge and prior to the first followup clinic visit, the communication piece has proven to be very important. It's not an insurmountable barrier, but one we observe that created one additional challenge. Other institutions around the country that are doing this have expressed similar things. Sony Tuteja:                       You showed in your study that during the middle of the implementation there was a decline in testing. What do you think were the major reasons that led to decrease in testing? Craig Lee:                            Yeah, that's a great question. We're not sure. We didn't collect information prospectively, and more specifically, we did not survey the physicians in terms of why they ordered the test. But we believe, just based on anecdotal experience and talking about this with everyone, there was this big surge of momentum, with the initial implementation, and as the practice evolved there was just sort-of a settling of individuals in terms of, I think, the practice patterns.                                                 Overall, the test was ordered and over 70% of PCI patients, an alternative therapy was prescribed and approximately 70% a loss of function allele carriers. These numbers exceeded 80% early on which was higher than we expected. They dropped down to about 60%, which is still a pretty high utilization rate when you compare to other institutions that have implemented. After some educational efforts, the testing rates and use of alternative therapy and loss of function allele carriers began to increase again over the last six months. Sony Tuteja:                       Yeah I was just gonna ask, since the study is completed, have you taken any further steps to maintain the frequency of the testing at the high level that you initially started with? Craig Lee:                            Yeah, so again recurring education has been really important particularly with interventional cardiology fellows, since they're the ones that really execute this in terms of ordering the tests and working with the clinical pharmacy specialists. And as I mentioned, we're in the process of developing clinical decision support to help make this a little bit easier on the prescribers. When a test result is available, we believe this will make it a little easier for the result to be more readily available for the clinical decision. Sony Tuteja:                       Yeah I think the CDS tools will be key to have more compliance with the results in adherence to the test results. I'm just curious, who pays for the genotype tests at your center and are you billing for these tests? Craig Lee:                            Yes. We're billing for these tests as part of routine clinical care. Sony Tuteja:                       Great, and you've had good success with reimbursement? Craig Lee:                            As far as we can tell, yes. Sony Tuteja:                       That's great to hear. I think that will really incentivize other centers to pursue similar lines of testing. So what do you think are the broader implications for implementing genetically guided care for other drugs? Craig Lee:                            Yeah, I think that it's interdisciplinary collaboration. Communication is really important among physicians, clinical pathologists, and clinical pharmacists. We found that this has been essential to success of the program here at UNC with this one gene drug pair. And again, this is fueled by a spirit of collaboration and will for our clinicians to work together to optimize patient care. And really, I think clinical pharmacists are uniquely positioned to help make this happen. Clinical pharmacists are uniquely positioned to interpret pharmacogenomic test results, provide medication recommendations, as well as counsel patients on how to interpret the tests and why the prescribing decision is being made. Our clinical pharmacists at UNC are fantastic and have really embraced this. They've shown that pharmacogenomics can be an important part of medication therapy management.                                                 Although implementation of pharmacogenomics testing is clearly a challenge, it is now part of the routine in our Cath lab and in our cardiology services. And again, that's been really exciting to observe. I also think this experience provides a foundation in an example for other pharmacogenomic implementations to occur at our institution. Sony Tuteja:                       That's great, it's so nice to hear about the team working together to get this accomplished. What has been the patient response to the testing? How have they responded to receiving genetic test results? Craig Lee:                            We think it has been overall positive. And again, it's now part of the workup in terms of providing the best possible care for the patient given the evidence that we have. And so again, since it's part of the clinical work flow, there's not a separate research consent that's done. The testing is part of the consent to the procedure. Sony Tuteja:                       Well great, that's all the questions I have for you today. Do you have any final thoughts you wanna share with our listeners? Craig Lee:                            No, other than just a thank you again for having me in for talking about our paper. And I guess, I would just urge those that are out there that are either planning to do this or doing this, to collect data. It's really important to evaluate the practice, evaluate the frequency of testing, the frequency of prescribing decisions being altered by the testing, and trying to understand what the barriers are. And if possible, evaluate clinical outcomes.                                                 You know, we started this study under the umbrella of continuous quality improvement and it really has taught us a lot. I think it has helped optimize how the algorithm is used, and as other centers around the country have been doing this, it provided a basis to collaborate and really evaluate the impact on clinical outcomes, which is really the question that is on everybody's minds. And as the evidence base expands, I think there will be a lot more comfort with doing these things, but we should always strive to generate the evidence we need to assure that we're making the right decisions in practice. Sony Tuteja:                       Absolutely, I think that outcomes piece will be critical to getting this in the mainstream. Well I'd like to thank you for your time today, it was a pleasure speaking to you and once again, congratulations. Craig Lee:                            Thanks. Jane Ferguson:                 That's all for this month. As a reminder you can follow us on Twitter, @Circ_Gen or connect with us on Facebook. Thanks for listening, and I look forward to bringing you more on genomics and precision medicine of the heart next month.  

Jane Ferguson:                 Hello, welcome to Getting Personal: Omics of the Heart, episode 14 from March, 2018. I'm Jane Ferguson, and this podcast is brought to you by Circulation Genomic and Precision Medicine, and the AHA Council on Genomic and Precision Medicine. This month I talk to Deirdre Tobias about her research on branch gene amino acids and incident cardiovascular disease in women, and I got the chance to talk to Kiran Musunuru, the new editor in chief of Circulation Genomic and Precision Medicine, about his take of the publishing process and new directions the journal has been taking.                                                 I'm joined today by Dr. Deirdre Tobias who is an instructor in medicine at the Brigham and Women's Hospital in Boston. Dr. Tobias is the first author of a paper entitled Circulating Branch Chain Amino Acids and CBD Risk in Women that is published this month in Circulation Genomic and Precision Medicine. Deirdre is also presenting this work at the lifestyle sessions in New Orleans this month, where she is receiving the Scott Grundy fellowship award for excellence in metabolism research. Deirdre, thank you so much for joining us, and congratulations on your manuscript and your upcoming award. Deirdre Tobias:                 You're very welcome, and thank you. Jane Ferguson:                 Could you tell us a little bit more about your background, and what led you to this specific research focus? Deirdre Tobias:                 Sure. I am an epidemiologist, and I investigate risk factors for type two diabetes and other obesity related chronic diseases. Most recently I've been interested in looking at the mechanisms linking obesity with many of it's associated risk factors through metabolomics. Metabolomics is a field that's relatively new, and it identifies, or seeks to identify small circulating molecules throughout the blood, or urine, or other specimen samples, and then relate those levels to risk of disease.                                                 Type two diabetes has had large successes with finding markers that are novel, and these are often consistently identified across studies, which is very reassuring, and not always the case for many risk factors of chronic diseases. Branch chain amino acids in particular have been consistently associated with diabetes risk across study populations, and tissue type samples, and methodologies for measuring metabolites. This is reassuring that these metabolite markers might really be picking up on an important signal in diabetes risk. Jane Ferguson:                 For any of our listeners who haven't had a chance to read your paper yet, I wondered. Can you give us sort of a brief overview of what you did, and a summary of your findings? Deirdre Tobias:                 Sure. Metabolomic studies, as I mentioned, have been successful in the field of type two diabetes, but less so for cardiovascular disease endpoints. With branch chain amino acids being a risk factor for type two diabetes we then thought to establish, or to examine whether they were also associated with incidents of cardiovascular disease risk. In our study we had branch chain amino acids measured on over 27,000 women from their baseline blood samples, and these women were participants in the women's health study in the US.                                                 This cohort already has over 18 years of mean follow up with a number of CBD and diabetes cases already accrued, so we used these baseline measures of branch chain amino acids measured from plasma samples to then relate these to incident CBD risk. We had over 22,000 MI, stroke, and revascularization events in these women. What we then did was analyze the relationship between branch chain amino acids levels with incident in CBD. Jane Ferguson:                 That's a really amazing data set. I mean, that's a huge number of subject, so that's really, really powerful. Deirdre Tobias:                 Yeah. For metabolomics most of the prior evidence has come from smaller case control studies, so this is really an unprecedented number of women participants in general that have had metabolomics with this much follow up. It was definitely a rich resource to be able to address this question and with a substantial amount of statistical power. Jane Ferguson:                 Right. I know you ran a large number of different models, and you looked at a lot of different covariances, so what were the primary findings? Deirdre Tobias:                 The main results indicated that even adjusting for the traditional CBD risk factors, including behavioral, and lifestyle, smoking status, family history, race and ethnicity, even body mass index which is a strong predictor of branch chain amino acids levels, we observed a striking association indicating that higher levels of branch chain amino acids were associated with a greater risk of MI stroke or revascularization during follow up. Jane Ferguson:                 Yeah, and I thought it was really interesting that you saw this in the whole sample, and then you also saw this sort of same association, in sort of like attenuated strengths in individuals without type two diabetes. Then, when I was looking I suppose you had adjusted for a number of different biomarkers, and then when you adjusted for the biomarkers as pre diabetic risks, so hemoglobin A1C or insulin resistance, the association went away. I wonder, even in your subjects who didn't have diabetes yet, were the association between the branch chain amino acids and CBD events was significant, but do you think that this was primarily driven by the pre-diabetes level, where these women are probably on track to developing diabetes, and just haven't hit that threshold for diagnosis yet? Deirdre Tobias:                 I suspect that could be the case. We do know that diabetes is a risk factor for CBD, and as I mentioned with the strong, consistent evidence indicating branch chain amino acids as a risk factor for type two diabetes we wanted to disentangle any association that we would see with branch chain and CBD to be able to show if that was independent of intermediate type two diabetes or not. When we did stratify by diabetes, clearly the majority of that risk seemed to be driven through type two diabetes.                                                 The women who had diabetes prior to their CBD event, clearly we saw the relationship much stronger for them. The residual is still there among those without type two diabetes, could be some clinical diabetes or pre-diabetes. It's hard to really say for sure, but we do have additional models where we adjusted for biomarkers that were measured in the same baseline blood samples as the branch chain amino acids.                                                 When we adjust for these markers that are more related to insulin resistance and glycemic control we see that the relationship between branch chain amino acids and CBD becomes attenuated. We can interpret this as being that the branch chain amino acids may be mediating the relationship with CBD through these markers of insulin resistance and glucose metabolism. If we similarly adjust for cholesterol, LDL or HDL, the relationship between branch chain amino acids and CBD didn't budge.                                                 That would indicate that perhaps branch chain amino acids and CBD risk is being mediated largely through this diabetic pathway rather than other more traditional CB pathways such dyslipidemia or inflammation. Jane Ferguson:                 Right, so it's an entirely independent pathway, I guess, that probably adds to the risk rather than being complimentary with traditional risk factors like LDL cholesterol. Deirdre Tobias:                 Yeah. The common soil hypothesis which has been around for many years suggests that there's a common set of risk factors, or cardio metabolic dysfunction, that leads to both diabetes and CBD. This suggests that branch chain amino acids and impaired branch chain amino acid metabolism could be one pattern or marker of this common predisposition to both type two diabetes and CBD. Jane Ferguson:                 Right. It's really interesting stuff, and I think this field, it's so challenging following up these markers when you're trying to find out, is a biomarker, when it's associated with a disease is it actually causal, is it a bystander? Are branch chain amino acids increased by insulin resistance, but don't actually themselves contribute to disease, or are they also individually contributing to disease. I know there's been some papers trying to address this through Mandelian randomization approach.                                                 It's indicated that the genetic predictors of branch chain amino acids aren't necessarily causal for diabetes, but that's sort of a consequence for insulin resistance. I wonder how that fits into your findings with, then, the additional associations with cardiovascular disease where we think of this pathway where we get this increased diabetic risk, and then perhaps as a consequence of the increased diabetic risk you have increased branch chain amino acids. Are these, then, themselves increasing oxidative stress or some other mechanism that's then leading to pro-atherogenic, pro-cardiovascular risk, or is it still just, well, it's a bystander, and they're there?                                                 They're a biomarker, but they're maybe not great targets for therapeutic interventions. I don't know what you think of this. Deirdre Tobias:                 Yeah. No, that's a completely fair interpretation of the data, and the literature overall. I think with observational epi, disentangling not only the causality but the temporality, so which biomarker is really, if it is causal, occurring first on this pathophysiology from say obesity or lifestyle to ultimately CBD? I think that it's difficult to really determine from one single study what that directionality is. Looking to the other literature we do see that there is the Mandelian Randomization. I think there have been more than one now, but one did indicate that genetic predictors of circulating branch chain amino acid levels were associated with type two diabetes risk, which supports there being a causal relationship between branch chain and diabetes.                                                 I haven't seen that also done for CBD, but that could be a next step. I think another next step for branch chain amino acids and metabolomics in general is to establish whether or not these are modifiable. If they're just along for the ride, or they're even just a strong biomarker, what does that mean clinically? I think it's still a very open question for many of these metabolomic studies, and even when we have these metabolites that really rise to the top as strong biomarkers, what do they mean?                                                 It could even be that we know they're causal, but that they're not entirely modifiable. Maybe they are just very strongly genetically determined. I think the next step might be to then identify what modifies levels of branch chain amino acids, whether it's lifestyle, or pharmacologic therapies, I think is still unknown, but if we can identify what can modify branch chain amino acids and then answer that next step of, "Well, does the modifying branch chain amino acids then confer a lower risk in disease," that would be an ultimate question to answer.                                                 I think these large scale epidemiological studies are very important as that first step in telling us whether we're even going in the right direction, and then subsequent studies will only strengthen these results, especially when it comes to the causality point. Jane Ferguson:                 Right. Right. I know in your current study, you're limited somewhat by the data, but I wonder. Were you able to look at whether dietary factors, or physical activity and exercise levels were even associated with branch chain amino acids. Obviously protein intake, or carbohydrate, sort of a prudent versus a western diet, were you able to look at that, or is that something that you may be able to look at in the future? Deirdre Tobias:                 Right. In the future we are hoping to disentangle what the determinants are for branch chain amino acids in this cohort, so looking at the role of physical activity, or dietary patterns, or specific foods and nutrients might play. But for this analysis here, we adjusted for all of those factors, and when we do compare, the first model where we only adjust for age, with the second model where we include adjusting for all the more traditional CBD factors as well as the behavioral, lifestyle, dietary patterns or et cetera, we see that there is quite an attenuation.                                                 This does suggest that there is some room for modifiability, because these other potential confounders are impacting the association that we see. If there was no effect in our results when we adjusted for all of these behavioral factors then they might not be that strong determinant of branch chain amino acids, but we do see some attenuation. It is possible that many of these factors, or maybe collectively they impact branch chain amino acid levels. But that's another next step, and I think that's something that we can address very well with the data that we have in our current data set, the women's health study. Jane Ferguson:                 Right. I mean, given the large number of subjects, even with messy dietary or exercise data, I think you'll have a lot of power to at least get you in the right direction to then define interventional trials that could specifically address that issue. Deirdre Tobias:                 Right. Another thing to keep in mind about branch chain amino acids is that they're essential amino acids, meaning that they're derived from diet and not synthesized within our bodies, but surprisingly the correlation between dietary intake and circulating levels is quite low. This, to me, tells the message that metabolism of branch chain amino acids, more so than their dietary intake, is what's driving these elevated levels, and what leads to this impaired ability to break down branch chain amino acids leaving these higher circulating levels, I think that's the risk factor that might predispose to these other cardio metabolic conditions down the road.                                                 Really I think the next steps would be to determine why certain people have more impaired metabolism of branch chain than others. Body mass index is highly correlated with branch chain amino acid levels, that's, you know, an obvious next direction would then be to look at lifestyle factors and anthropometrics to see where those lead us. Jane Ferguson:                 Right. Right, and I wonder, is there some genetic [inaudible 00:16:05] where you could maybe find the people who don't fit the profile, like say somebody who has really dysregulated glucose and insulin homeostasis but actually has very low branch chain amino acids, or the converse, where they have very high branch chain amino acids but actually their insulin levels seem fine, and see. Is there something special about those groups of people, if they even exist? I'm sure there's probably some disconnect sometimes. It's not obviously perfectly predicted. Deirdre Tobias:                 Right. Sometimes looking at those discordant phenotypes can be really informative, and I think that could be interesting to look at as well. I think the genetic predictors, the few snips that have been associated with circulating branch chain amino acid levels from some of the Mandelian Randomization studies indicate that heritability might not be that strong, but certainly there are genetic factors that influence levels. But a lot of it is likely to be more of the modifiable, or environmental risk factors. Jane Ferguson:                 Right. Right. Deirdre Tobias:                 Which I think is an important message for prevention, or motivation towards prevention, because if we can modify this important risk factor, then we can ultimately reduce risk. It's certainly a lead worth investigating. Jane Ferguson:                 Yes, absolutely. Yeah, what's next for you. Are you going to keep working on some branch chain amino acids? Are there other metabolites that you're interested in as well? Deirdre Tobias:                 The overall metabolome is something I'm still very interested in. Branch chain amino acids have risen pretty quickly to be among my favorite metabolites, if I'm allowed to have a favorite metabolite, but I think that although an overall metabolomic pattern identifying what the overall pattern or profile of metabolites looks like for individuals who go on to develop certain diseases, and using those patterns as predictors, or again looking to see what we can modify about them I think is also really interesting. But branch chain amino acids clearly have an important role either as a predictor, a bystander along for the ride, or possibly even causal, yet to be determined.                                                 Yeah, next steps would be to, like I said, identify, what are the determinants of higher levels, and whether or not these can then be modified to reduce risk. Jane Ferguson:                 Important work. Is there anything else you want to add that I haven't asked you about yet? Deirdre Tobias:                 The other findings in the paper were that it seemed fairly consistent across the three branch chain amino acids that we investigated, so isoleucine, leucine, and valine, which share a similar catabolic pathway. Again, maybe the overall breakdown of this metabolite type rather than any one metabolite might be what's relevant to [inaudible 00:19:01]. Jane Ferguson:                 Right. Yeah, so a defect, if there is one, is a stream of all of the branch chain amino acid processing. Deirdre Tobias:                 Mm-hmm (affirmative). Yeah. Jane Ferguson:                 Yeah, really interesting. Well, thank you again for joining this, and for talking about your work. Congratulations again on it. It's a really interesting paper, and your presentation and award at EPI Lifestyle, it's great stuff. Deirdre Tobias:                 Great. Well, thank you very much. I appreciated talking to you. Jane Ferguson:                 Thanks. As many of you may know, Dr. Kiran Musunuru is an associate professor of medicine at The University of Pennsylvania, Perelman School of Medicine. More pertinent for this conversation, he assumed the role of editor in chief of Circulation Genomic and Precision Medicine this year. Welcome Kiran, and congratulations on your appointment as editor in chief. That's a fantastic achievement. Kiran Musunuru:              Thank you. Jane Ferguson:                 With great power comes great responsibility. I know you've been making a lot of changes to the types of content available on the journal website, and finding new ways to ensure the journal is publishing best science and keeping up with changing technologies and capabilities. I'd love to hear about some of the initiatives that you've been putting in place and what your vision is for the journal going forward. Kiran Musunuru:              Sure. I think it's not just about publishing the best science, although I think that's obviously the core mission, but I think also publicizing the good science that we're publishing. With that in mind, the name change of the journal to Circulation Genomic and Precision Medicine really indicates our desire to go beyond what can seem like the very limited scope of cardiovascular genetics, and really take advantage of the rich work, and the energy that's now being infused into whatever you want to call it, Omics, or precision medicine, or so forth. It really is much more than just cardiovascular genetics.                                                 We're casting a wider net. We want to publish a more diverse group of articles. In a sense, we want to be a player in the field. We have a nice opportunity where there is this thing called precision medicine, but everyone is hard pressed to define exactly what that means. I think what that signifies is that it's just so new, and still in the process of early evolution, that the journal I think will have a part to play in helping to define the field over the next few years. I think that's very exciting. It's a very exciting opportunity for any journal, or any journal editor, to be able to contribute in that way.                                                 But as I said, it's not just about publishing the best science. It's also about really trying to make maximum possible impact with that science. With that in mind we're working very hard to introduce elements in the publication process to really expand the reach of the work that is published. The way I like to think of it is, traditional journals a paper is published on the printed page. It gets mailed out to it's subscribers. Some people read it. Some people don't, but within a few weeks after the issue is published basically that's it.                                                 No one really, certainly is not going to casually pick up an issue and look at it, and only a very limited number of people are going to come across the paper on PubMed or whatnot, and actually go track the paper down. I think it should not be the end of it. I think that should just be the beginning. I think the day that it's published online, or in the formal journal, is really just the beginning of the opportunity to make that work known, and really try to make an impact on the field.                                                 Some of the things that we are doing in that spirit include this podcast series, that chain. You've taken the leadership, and have been doing a wonderful job with in terms of promoting some of the science that we're publishing, as well as just general issues in the field that I think that will be of broad interest to the same people who are interested in the content of the journal, and interested in the science that's encompassed by the Genomic and Precision Medicine Council, The American Heart Association.                                                 One element beyond that is what we call our calling, I think of as video summaries. Some journals have been experimenting with this in a sort of exploratory way inviting authors to make their own videos and submit them so that they can be placed on the journal website. Of course that demands a lot of authors 'cause there's a lot of inconsistency in the tone and the quality of those videos because they are coming from very diverse people rather than being centrally produced with the journal.                                                 Some of the very high profile journals have a substantial enough budget that they can actually internally produce, with high production values, very nice videos, but again, they tend to focus on just one or two key articles in each issue. Our ambition is to have a video summary for every original research article that's published in the journal bar none. We think that plays a very useful purpose.                                                 We think that getting someone to actually download a PDF, and read through it carefully, in this day and age is a harder sell than it has been in the past, and if we want to take full advantage of social media, and the fact that everything is out there on the internet, and can be accessed through devices, through smart phones, et cetera ... We felt the best way to introduce a paper and the content of that paper to the casual viewer, or listener, or reader, if you will, maybe not someone who is super specialized in the topic that's covered by a given paper, but still nevertheless has something the learn from the paper, can get something useful out of it.                                                 We feel that generating a video summary that goes through the high points of the paper, that can actually within the video show the key figures of the paper, and then in, not exactly layman's terms, or lay people's terms, but rather, in a non-specialist sense, so still speaking to the science, but without a lot of jargon, without a lot of specialized vocabulary that's really specific to the specialists in the area that's covered by the paper, but really more broadly speaking to scientists, and even more than that, clinical practitioners, really explain in a very straightforward and simple way exactly what the key message of the paper is and why it is important for genomic and precision medicine.                                                 We have started doing this internally. Starting with the January issue we have had a video accompany every single original research article as well as a scientific statement that we published in the January issue. They are linkable, or linked to, from the website, but they are physically, well, not that anything digital is really physical, but let me put it this way, they're hosted by a YouTube channel under the umbrella of the Circulation Journal. They can be found there because they are on a YouTube channel they are essentially a permanent part of YouTube, so anyone searching YouTube can find them.                                                 Then something we've started doing with the transition internal leadership and the new name of the journal is using Twitter to make people aware of these articles that we are publishing, but one very nice thing we can do since we have video summaries available on YouTube is we can actually link within a Tweet directly to that video, and people can actually watch the video directly within Twitter on their devices, on their smartphones, on their computers, wherever they are, however they're accessing it.                                                 We've been finding that people are watching these videos, regardless of what channel they use to get to the videos. They are watching. I think that's a nice thing, because it is expanding the reach of the journal to an audience that might not necessarily or naturally go to the journal website to see what the latest articles that have been published are. It's a different way of reaching the intended audience. Jane Ferguson:                 Yeah, I think it's fantastic. I mean, it's much easier to watch a video for a few minutes when you're, I don't know, waiting for the bus or something, rather than take out your PDF and try to read through these dense method sections. It's really fantastic, I think, even for people who are, if it's our field, or if it's something outside of our field, or people who don't typically read scientific articles but are interested in science [inaudible 00:28:24] and learning more. I think it's really nice to offer that sort of alternative format for people to discover a new science.                                                 I wonder, what do you think is the value of commentating an interactivity in scientific publications? I know in some places they allow commenting, whether anonymous, or named. For a lot of people they get a lot of their science on Twitter, and they have Twitter conversations. Do you think that's an important role that needs to grow more in our dissemination, our discussion of science? Do you think there's an ideal forum for this kind of discussion, or is it still something that's evolving, and we'll find our place, whether it's the comments section of the YouTube videos, or on Twitter, or elsewhere? Kiran Musunuru:              Sure. No, that's a great question. It strikes me that a lot of groups, a lot of organizations, have been hitting upon the same question, and attempting to answer it by generating a forum that's, if not exactly proprietary, is very much under their thumbs, with the intent of fostering conversations about papers published in journals, about content from publishers and so forth. Because everyone is trying to do their own thing it ends up being quite fragmented.                                                 There's not a lot of uptake for any one particular publisher's website, or any one particular mechanism. What has sort of naturally, spontaneously occurred, is a lot of those conversations have just happened on Twitter, which is not intended specifically for scientific discourse, but has cast such a wide net, and has so prevalent a footprint in our society that it serves that purpose just as well as it serves 100 other purposes.                                                 I think it's becoming clear that, increasingly, conversations, discussions, debates, controversies even, are unfolding on Twitter. That's not to say that everyone's on Twitter, or even a large segment of our intended audience is on Twitter, but I think a growing proportion of our audience is on Twitter, and Twitter I think skews younger. But we're a young discipline, and I feel like a lot of the practitioners, a lot of the investigators in our field are of a younger generation who is more ready to embrace Twitter, and is already out on Twitter, on Facebook, on other social media outlets.                                                 I think this is going to be absolutely critical going forward. Twitter, it's not happenstance of course, but it turns out that it lends itself nicely to communication about scientific articles because you can link directly to a paper through Twitter right there in the Tweet, so people don't have to look very far. You can send out your Tweet describing the paper in very brief terms, but then within that Tweet you actually see a box with a description of the paper, with a link to the paper. You can link on it. It takes you directly to the journal's website.                                                 You don't have to actually work to find the paper. The same is true of other elements that we've already discussed. If you want to listen to this month's podcast for Circulation Genomic and Precision Medicine, spearheaded by Jane Ferguson, then the Tweet that goes out, and then is retweeted among our network, allows you to click right there and go to the podcast. Again, you don't have to track it down. You don't have to find it. It's just kind of put there, out there, and people will immediately be notified about it because it will be on their feeds, and then they'll see it.                                                 Then, as you say, when you're waiting for the elevator, or you have a little bit of down time, you can go ahead and just click on it and immediately start consuming it, if you will. The same, of course, is true of our videos. Our videos are five to ten minutes, ten minutes maximum. They're intended to be bite sized pieces that you can kind of catch on the run as you described. It's not a matter of you having to download a PDF and read through very dense language and through method sections and try to figure out what's going on.                                                 Then people can access these on Twitter, through other channels. Then a lot of the conversation can happen on Twitter, and on the other channels. I think we're seeing more and more of this happening, among at least that portion of the scientific community that is Twitter savvy. Jane Ferguson:                 Yes. And I will use this opportunity to plug everybody to follow us on Twitter. You can follow us at @circ_gen, and you can tweet at us and tell us what you think of the journal, of the council, of the podcast, of science in general. We would love to hear your thoughts on that. As we're talking about this, how we're able to access so much information so quickly at our fingertips, the publication process has in some ways still lagged behind, where you submit your manuscript, and it goes out for review, and sometimes you don't hear back for a very long time. I'm wondering how you at the journal are dealing with that aspect of the publication process, and the turnaround time? Kiran Musunuru:              Sure. I mean, I think it's becoming increasingly less acceptable for what has happened in the past, and it has been highly prevalent in the past to continue to occur. What I mean by that is, a very long, drawn out, peer reviewed process and time to publication of the final project. I mean, it's so unacceptable these days when there's so many avenues through which you can communicate your science.                                                 We're seeing things like pre-print servers that are really getting messages out. Then to have to go through a formal review process, and take many months, even years, to actually get your work actually formally published in a journal with the imprimatur of high quality peer reviewed process is just too long. It's impeding communication among scientists. That's a disservice to the scientific process, especially in a day and age where there is such a rapid evolution of technologies. There's such rapid advances in a field, to think that the journals are having to play catch up with what's actually going on on the ground is really quite frustrating.                                                 It's part and parcel of the publication process as it is, and it's not going to change overnight, but in my own role as editor of a journal I'm doing what I can to remedy it. I've committed, and the journal's editorial staff has committed to really trying to streamline the review process. Now, we're not going to work miracles, but I'm happy to say that, whereas in prior years it would have taken on the order of three, four, five weeks for a paper to be reviewed, we've really worked hard to streamline that to make it much more quick, nimble, and we hope less cumbersome to authors.                                                 For example, I will say in the month of January our average time from submission to first decision was under one week. Part of that is, we've accelerated the review process, engaged high quality reviewers who we know are responsive, and who will turn around papers relatively quickly. We've engineered things so that there's not much delay to any step in the process. Even those papers that are sent out for review we've accelerated the process and decreased the time on that basis alone, but something else we've introduced is a very rapid triage process.                                                 Rather than have a paper sit around, sit around, and then be assigned to somebody to look at, and then have to wait for a weekly conference call among the editors to decide the disposition of that paper, you can see how things can get drawn out. Even before the decision is made to send something for review you might have a paper sitting around for a week or two. That happens, I know from my own personal experience at quite a few journals. We're committed to making the decision, ideally within 24 hours, but certainly within 48 hours.                                                 Our editorial staff is extremely responsive. When a paper is submitted, there's a whole lot of eyeballs among the editorial staff that look at that manuscript from the get go, quite a few opinions, not just one, or two, or three opinions, but more like six or seven opinions within 24 to 48 hours. That's useful in two ways: One, it allows us to make a very informed but rapid decision as to the disposition of a paper.                                                 I think the optimal scenario is where we can forecast whether a paper is not going to fare well in the review process, and is ultimately going to get rejected, without actually having to go through the full length peer review process. If we can predict that outcome occurring, but be able to do that within the first 24 to 48 hours, then we can just make that decision and spare the authors a long delay for what ultimately is likely to be a negative outcome.                                                 Conversely, if we are fairly confident that a paper will ultimately get accepted, we'll need some revisions. Every paper can be improved, and that's what the peer review process at it's best is all about is improving the quality of a paper. But if we can forecast very early, this paper is very likely to be accepted. This is what we should send out for review. Then that allows us to really save everybody time.                                                 It saves author's time, 'cause they're not going through a long, drawn out process before ultimately having a negative outcome, but it also spares reviewers' time. We can focus reviewers' efforts on those papers that are extremely likely to ultimately be accepted, and that's the most constructive use of reviewers' time, I think.                                                 There's a secondary purpose, however, and that is when we do reject a paper without review because we've garnered so many opinions about the paper in a very short time, we're actually in a position where we can offer useful feedback to the authors as to why the paper was felt not to be of high enough priority for the journal. Most journals, and I'm speaking from my own experience as a scientist, as an investigator, as an author, you send the paper, and then it takes them how many days, or weeks to make a decision. Then often they will decide that it's just not for them, and they will reject without review.                                                 That's okay, but it can be very frustrating when the decision is made, and then you get the letter back saying, essentially in effect, "We don't like this paper," without any explanation why. What we have been doing, which I think has been well received by authors, is offering fairly detailed feedback. If you want to think of it as a triage review you can think of it that way, but corporate feedback from not just one or two peer reviewers, but actually six or seven editors who have looked through the paper.                                                 We've actually gotten feedback from a few of the authors, that even though we are not publishing the paper, it's been useful in helping them to revise the paper and then go on to the next journal for submission. Jane Ferguson:                 Yeah. I think that's fantastic on all fronts. I think making the process faster is better for everybody, and also making it constructive, so that yes, you're not just sending out form rejection letters, but you're actually helping scientists and authors to identify what could be improved in their papers and what sort of things they might not have thought about. I think that's really fantastic. Is there anything else new with the journal, or anything else that we can look to in the next few months coming out? Kiran Musunuru:              Well, I think we've hit the high points. I think the idea that we want to be friendly to authors, that we want to streamline the review process, that we want to work hard to publicize their work and maximize it's impact in the community. I think by broadening the scope of the journal we're hoping to get substantially more submissions, and more diverse papers. We're very interested in publishing in the area that broadly falls within genomic and precision medicine. We're working hard to generate high value, non-original research articles.                                                 What I mean by that is scientific statements from The American Heart Association, working closely with the Council on Genomic and Precision Medicine to generate scientific statements that are of value to the community. Of course, we would continue to want to publish state of the art reviews, but also partner with other organizations, and publish white papers. For example, we have a couple of white papers on which we're working with The National Heart, Lung, and Blood Institute. That should be coming out in calendar year 2018. Jane Ferguson:                 Okay. Kiran Musunuru:              Beyond things you would normally associate with publishers, we're also working hard to increase our presence at meetings. Part of that is what you might expect editors of journals to do, which is being there in person, and attending talks, and attending poster sessions, and pressing the flesh, and inviting promising presentations to be submissions to the journal. Of course, that's part and parcel of what any good, proactive journal is going to do.                                                 But I think going beyond that, and doing more, and actually being involved in contributing educational activities at conferences. As you well know, the Council on Genomic and Precision Medicine in partnership with the journal has been organizing boot camps and workshops at various American Heart Association meetings that have been extremely well received. And we've now started to organize similar sorts of boot camps and workshops at non American Heart Association meetings.                                                 We'll be doing quite a lot of that, at least three or four national or international meetings in calendar year 2018. I think this is all part of a big strategy to not just be a place where papers are published, read for a few weeks, and then never really thought much of again, but really to make it more of a living piece of work that can inform the community, that has a reach beyond the printed page, whether it's through videos, or podcasts, or forming the basis for educational activities, workshops, boot camps, maybe eventually journal clubs, things of that sort. That's what we would really like to do with the journal going forward. Jane Ferguson:                 That's fantastic. I think it's really great. You're doing some great new initiatives. Congratulations again on your appointment as editor, and on all of the wonderful work you're doing. Kiran Musunuru:              Thank you, Jane. Jane Ferguson:                 That's all for this month. As a reminder, you can follow us on Twitter @circ_gen, and you can also now connect with us on Facebook. Find us under Circulation: Genomic and Precision Medicine, and hit like or follow to get the latest in your news feed. Thanks for listening.  

Transcript of the February Podcast, “Getting Personal: Omics of the Heart”, Episode 13   Hosted by Jane Ferguson   Assistant Professor at Vanderbilt University Medical Center & Associate Editor of the Circulation: Precision and Genomic Medicine journal of the American Heart Association Jane Ferguson:             Hello. This is episode 13 of Getting Personal: Omics of the Heart. It's February 2018. I'm Jane Ferguson, an assistant professor at Vanderbilt University Medical Center, an associate editor at Circulation: Precision and Genomic Medicine, and an occasional podcast host. This month, I talked to Kiran Musunuru and Svati Shah about how they spearheaded name changes for Circulation Cardiovascular Genetics and for the AHA Council on Functional Genomics and Translational Biology, and Andrew Landstrom talked to Kaytlyn Gerbin and Brock Roberts from the Allen Institute about some extremely cool work they are doing with CRISPR and IPS cells to create fluorescently tagged maps of live cells, which allowed them to image and better understand the structure and function of individual cells.                                     I'm delighted to have two guests on the podcast today, Dr. Svati Shah is the current chair of the AHA Council on Genomic and Precision Medicine formerly called the Council on Functional Genomics and Translational Biology. She is an associate professor of medicine at Duke University Medical Center. Dr. Kiran Musunuru is editor in chief of Circulation Genomic and Precision Medicine formerly named Circulation Cardiovascular Genetics, and he's an associate professor of medicine at the University of Pennsylvania Perleman School of Medicine.                                     Dr. Shah and Dr. Musunuru were kind enough to take time out of their busy schedules to join me for a joint discussion on the recent enhancement of name changes for our council and our journal. With tight schedules and last-minute flight cancellations we didn't have ideal settings for recording, so apologies in advance for a little more background than usual.                                     My instruction highlighted a number of name changes and astute listeners will have noticed that the new names for both the journal and the council are very nicely aligned, so I know this was not a coincidence, and I'd love to hear from both of you, what prompted the decision to change the respective names of the council and the journal, and how did you come together to streamline these name changes? Svati Shah:                   Well, maybe I'll take a first start, you know, we, we're really lucky in our council, we have a very, you know, certainly one of the smaller councils [inaudible 00:02:26] we have a very collegial spirit that wants to get things done. So, these conversations actually started probably three years ago, umm, when Jen Howell was chair of the FGTB council. And we realized that not only was our constituency broadening in expertise and breadth and depth, but also, umm, the desire to kind of move beyond the really wonderful work the council is doing around technology platform, genomics, genetics and you know important advances in many of our council members have made in the translational biology field and really thinking about the fact that we have this amazing expertise that can come together across a wealth of disciplines to really translate what's being done in the omic space, and apply it in this new world of precision medicine.                                     And so, umm, that is what stirred really thinking about a name change so that not only would it reflect this expanding constituency in expertise and hopefully draw even more people, across the, you know, wide expertise. But also to harmonize more with people who are in other councils, including clinical cardiology, and people that, really, in the end we are actually quite allied with scientifically, but perhaps those councils didn't recognize really what our council was about because of our previous name. So in that context, you know, it's been wonderful. Kiran has been a wonderful partner in all of this, he's been a real leader in the council and over the past two years we have had many conversations across council leadership and the entire council, and thinking about what this name change would be. And actually, it was almost a consensus amongst council leadership to choose Genomic and Precision Medicine as the name, really to reflect our core beliefs and our core science in genetics and genomics, but also to reflect the expanding expertise of all the different omics platforms, our expertise in clinical genetics with more genetic counselors joining our council, and our expanding expertise in computational biology. And this really allied nicely also with the American Heart Association building a very important institute, the Precision Medicine Cardiovascular Institute. So, I'll let Kiran go from here but again, Kiran has really been a great partner in this and he can kind of expand on that story and how that led to the journal name change. Kiran Musunuru:         Sure, so, with respect to the journal, I think these changes have been growing for a while. I think a lot of the same considerations came into play, the feeling that the journal with the name Circulation Cardiovascular Genetics was perhaps too narrowly defined given how the field, how the science was evolving. And the other consideration is that the Functional Genomics and Translational Biology Council has had a journal, a companion journal if you will, all of this time with a fairly distinct name, Circulation Cardiovascular Genetics, and so it wasn't necessarily obvious to those who are not on the inside so to speak that there was supposed to be a very tight connection between council and journal, that the journal really was the journal of the council and so in the process about deliberating about a council name change, it became natural to think that, "Wow, wouldn't it be nice if the journal could execute a similar name change", and separately, even though this predates my tenure as editor of the journal there had been conversations going on separately or independently that perhaps the journal would benefit from signaling that it was not just about cardiovascular genetics in the very narrow sense, but was really about a much larger area of science. And so there had already been contemplation for quite a while about a name change and so when I assumed the editorship I didn't really have to do much to convince anyone that this would be a useful thing.                                     The scientific publishing committee of the American Heart Association and all the various people involved publishing the journal were already sort of primed for a name change and then it just ended up being a nice convergence of opportunities, Svati with her work in the council and really showing the leadership to lead the transition from Functional Genomics and Transitional Biology to Genomic and Precision Medicine.                                     That really laid the groundwork, and because it was such a deliberative process, such an inclusive process, involving dozens of people on the leadership committee of the council as well as general membership of the council, it was really a no-brainer. The hard part had already been done, the thinking had already been done and I was straightforward to say that we should change the name of the journal to match, Circulation Genomic and Precision Medicine. Jane Ferguson:             Have there been any logistical difficulties in getting this name change through, or has it all happened very organically? Svati Shah:                   The American Heart Association has been a real partner in the name change, sometimes things require many layers of approval and in fact, it has been a relatively seamless process. We came up with a consensus around the name change and later applied formally for that change in the council name, and that was pretty quickly approved by the Scientific Advisory Committee, within a few months really. Our name change became official and we are in the exciting time now of advertising and kind of marketing the name change and appeal to a broader constituency and really reach out to group that perhaps wouldn't have realized that this council is a great home for them again thinking of genetic counselors and computational biologists. So, it really, you know, has been a surprisingly seamless and fun process. Kiran Musunuru:         As I mentioned before it was already kind of in the air that a change was imminent and so when I posed the name change to the Circulation Genomic and Precision Medicine it ended up being a very smooth transition. It was timed so that the volume change, that is changing from the volume associated with the calendar year 2017 to the volume associated with the calendar year 2018, January first ended up being a very logical transition time and so that's when the change occurred.                                     And happily, the council name change ended up occurring almost in lockstep; whereas, you know within a few weeks of the journal announcing its name change the council was able to announce its name change as well. I think that has had a reinforcing effect across the American Heart Association and its membership. It really signals that the council and the journal are tightly tied together, are partners in moving in lockstep. Jane Ferguson:             Svati, this question's probably more for you, so what does the name change mean specifically for existing FGTB council members, and what if anything will change, and then what might it mean for potential new members who are trying to decide what council to join? Svati Shah:                   That's a great question, Jane, you know I am a pragmatic person and I think our council also reflects that pragmatism. We get a lot of things done and we, I think, spiritually all agree that we shouldn't just change the name just for the sake of changing the name. And so we really, actually the name change followed [inaudible 00:10:18] were involved in, these discussions are a year and a half of really thinking about what direction we wanted the council to go and then what the sort of short and long term goals of our council are and then how does the name change effect the long term goals.                                     So, we have a lot of great initiatives in the short and long term, which again will capitalize on our broadening expertise in these different clinical genetics and precision medicine and really, translating genomic and omic findings into, into important patient care. And so, we have several things coming down the pipe that are sort of proof of principle examples of what the name change reflects.                                      So, one example is that we are now working on developing a certificate in medical genomics with the idea that we really need more genetics education. Our council has been very much embedded in genetics and genomics education, Kiran being a key example of that. And now we are expanding that into thinking about how genomics is applied to clinical medicine but making it at the level that is digestible and understandable and is easily applied by a general cardiologist and even primary care doctors will be able to use that resource. And the idea is this will be your self-sustaining certificate that's given through the American Heart Association, so we have a group that's been working on that certificate and hopefully that will be coming out soon.                                     Another key component of what we're doing is trying to reach out more and partnering with other associations including the American College of Medical Genetics and the National Society of Genetic Counselors, again really thinking about how we transition our important scientific discovery work into translation implementation science around patient care.                                     To give you some examples of what that means in terms of what the name change is reflecting, I think with the right use, for the second part of your question, which I think is a really important part of your question is, we want to attract more people in the computational biology field, in the precision medicine space, in the clinical genetic space and again reaching out to genetic counselors through some of these societies, because we, just the wave of precision medicine is here, is going to expand even more and the expertise within our council that was already there but that now we can expand. I think it will be leveraged to really make important contributions to making sure that those efforts in precision medicine are done well, or done responsibly and are done with the patient in mind because in the end the American Heart Association is at the forefront of patient advocacy group.                                     This is a really exciting time, I think that, you know, however you want to define precision medicine the bottom line is precision medicine is here and we can't have, it's not going to be a single faction of individuals or a single expertise that is really, is going to be able to leverage fundamental scientific discoveries whether its genetics, genomics, metabolomics, proteomics, and really translate them responsibly into patient care, so it's going to involve an interdisciplinary and multi-disciplinary effort.                                     I feel really proud that I'm part of the AHA and that we sort of have this perfect storm between Kiran's leadership in the journal, our council changing, you know, its goals and its name aligning with the Institute for Precision Cardiovascular Medicine within the AHA. And I think that, you know, it's not all rainbows and sunshine. We have a lot of work that is cut out for us in the next few years to figure out ways that we can tangibly and concretely, and again responsibly, work together across each of these three components of this perfect storm to make sure that it’s not just a glitzy name change and that there is actually substance and behind all of it, so, you know, it will be, there will be challenges, there will be obstacles, but I think that the amazing people within each of those three components, I feel very confident that we are going to be able to do it well. Jane Ferguson:             Yeah, I agree, as a member of the council, if anybody can do it I think this group of people can do it, so it's very exciting to see, so thank you both for joining, and congratulations again on the new names. It's really exciting to see these, you know, new directions for the council and the journal working together. And I really look forward to seeing all the great initiative that will be coming out in the next few years. Svati Shah:                   Thank you, Jane. Kiran Musunuru:         Thank you, Jane. Andrew Landstrom:     My name is Andrew Landstrom, and I'm an assistant professor in the department of pediatrics section of cardiology at Baylor College of Medicine. I'm a member of the early career committee of the American Heart Association Council on Genomic and Precision Medicine, previously the Council on Functional Genomics and Translational Biology, and I'm joined today by Brock Roberts and Kaytlyn Gerbin, who are scientists on the stem cell and gene editing team at the Allen Institute.  Here to discuss a little bit more about CRISPR editing and what they have done for live cell imaging using fluorescent proteins.                                     So, Brock and Kaytlyn, I'm hoping you can discuss a little bit about what the Allen Institute is and your overall research mission and goals.  Kaytlyn Gerbin:           Yeah, great, so this is Kaytlyn and thanks Andrew for having us on, and we're really excited to share a little bit of the information about what the institute is doing, because we're building a bunch of tools that we think would be really useful for the research community. So, we're excited to get the word out there. And so, the Allen Institute is a non-profit research institute, and we're based in Seattle, Washington, and, essentially what we're trying to do is better understand the cell.                                     We want to understand the various states the cell can take based on structural organization of how different organelles work together. And so, we're doing this, essentially by live cell imaging and also combining that with predictive modeling so that we can build tools to be able to understand structure-function relationships and how cells behave in a healthy state or in a diseased state. So, you can kind of think of this as, we like to say sometimes like a Google Earth for the cell, so if you kind of think about it in that context, a lot of times, you know you could look at the cell at a really high level just like you could look at the Earth at a very high level. Then you could zoom in further and you could look at an individual pathway maybe that you're interested, or perhaps, as an analogy, like a different highway within a part of a city.                                     But you don't really understand how all that works together and how the city functions together until you start to put in things with spatial organization, or maybe temporal dynamics, or how different parts of the structures, or different structures and organelles work together to form the unit that is the cell.                                     And so, essentially, we're trying to generate a bunch of data so that we can build predictive models to help us understand that better. And, we're doing this with human induced pluripotent stem cells, and the first cell state or cell type that we're studying is cardiomyocytes after differentiation.                                     And so, yeah, as we're kind of generating this data we are a non-profit institute, and all of our lines and our plasmids, protocols, data, pretty much everything that we make is becoming publicly available as it passes QC. And so, yeah, we're excited about that, I don't know if, Brock, you have anything else to add. Brock Roberts:             Yeah, just I think an important concept that we're often working with is scale. And, biology exists at certain scales, and that's certainly true for cells and the Google Earth analogy holds.                                     You know, at some level if we want to understand the cell at the scale of its entirety, but we have to kind of cut that down and understand cells at the level of its parts.  And, they're working together as we know, and can infer, but we try to find a way to look at the part one by one and then put it all together in a model that's predictive. And the predictive part is going to be really important. Much like Google Earth can allow us to, you know, look at a traffic pattern in the city or something like that once the data is filled in. We hope to fill in enough data by looking at the cells constitutive parts to make the predictive model. Andrew Landstrom:     And not only looking at, sort of, constitutive parts, you're doing this in a physiologic live cell, so really it's Google Earth, but it's Google Earth in real time as cars are driving down the freeway and people are walking down the street. Brock Roberts:             Right- Kaytlyn Gerbin:           Yeah, exactly. Brock Roberts:             Yeah, that, that's where the dynamics of the cell can really come to life if you've prioritized looking at live cells, which are obviously incredibly dynamic. Andrew Landstrom:     And so, you know to be able to accomplish this, you all have come up with some pretty novel methods. Would you talk a little bit more about your CISPR editing approach, and how you've applied this to different lines and to get, sort of, different markers into cells? Brock Roberts:             Right, sure, the, we should say that we owe a lot to the development of CRISPR-Cas9 editing, which preceded us by a few years, but we've tried to kind of scale it up in some important ways.  And, really the important thing to appreciate about this process is it's a way to make a very precise, precisely guided DNA break in the genome of a cell. And we do this in human induced pluripotent stem cells, and so we can quite precisely choose a position or location in the human genome and trigger DNA damage, trigger breaks in the DNA molecules that make up the chromosomes.                                      And we can do this with, kind of a highly specifically guided RNA molecule that we complex with this Cas9 nucleus molecule, and these are, very famous molecules now, over the last few years they've become very well known.                                     And the upshot of this is we can, sort of trick the cell into repairing that DNA break using the processes that are always at play in living cells to resolve breaks in DNA, but we can sort of trick that process to add something additional at a specific site. And the additional sequence that we use is a tag sequence that corresponds to a fluorescent protein after the DNA is expressed and translated. And so, what we can effectively do is tag proteins that are produced in a highly endogenous, natural fashion within cells.                                     And the proteins that we can tag in this way, using this method, correspond to some of the most canonically recognized structures and organelles within the cell. And so, at this level we try to choose proteins, tag them in this manner, and take advantage of the fact that they will localize predictably to some of the dozens or hundreds of structures that make up cells. Kaytlyn Gerbin:           Yeah, and a key thing I think to add that Brock kind of mentioned was that this isn't any over expression, we're doing all this endogenously. So it’s really like, pretty, I think that's a big advancement over what has typically been done in the past with a lot of fluorescent tagging of proteins within the cell. Brock Roberts:             Right, but what's important to appreciate is that we're using the cells endogenous copies of each protein, expressed from the genome. We've done it in about 30 different genes so far. And we have a high success rate in accomplishing this process, all the way through to completion, which is to say that we know that we can introduce a tag onto at least one copy of each gene that is, that encodes a protein that can be tagged this way, and then we can monitor the cells over several months and ensure that this doesn't have a negative consequence on their growth or on their ability to differentiate or something like that. Our quality control process. We have a high success rate so far. Andrew Landstrom:     And that's really, in my eyes, one of the key, key sort of, innovative factors of your work, in that these are endogenous proteins that are able to be expressed and then to be imaged in real time without really disrupting the underlying cellular physiology. Kaytlyn Gerbin:           Yeah, and we do care a lot about what you just said, that it doesn't have any negative effect on cell behavior because we are using these as a surrogate for understanding cell behavior in, hopefully, a normal context. And we do an extensive amount of quality control work and all of that QC data is available on our website, and then you can actually access all of our cells through Coriell and all of the QC data for all those cell lines is made available, and we've also done a pretty extensive job outlining the QC that goes into this process so that, hopefully, people will take a look at that when they look at our cells and understand what we've done, but we also hope that this will kind of help set a standard for things that other people should be looking at when they're doing editing on their own. Brock Roberts:             And we really hope that people take these cells and do experiments that we don't have the bandwidth to do, and test them in ever expanding ways and let us know and report on it. Let us know how the cells perform and their unique assets. Andrew Landstrom:     Yeah, and I think all that sort of transparency with the quality control really makes it user accessible and just sort of invites that degree of collaboration, that's great. Kaytlyn Gerbin:           Yeah. Brock Roberts:             Yeah, we hope so. Yeah, we think so, too. Andrew Landstrom:     So how many cell lines do you have available? Kaytlyn Gerbin:           Yeah, so, currently, and again you can access all these lines on the website, but we have 16 lines that are released that have gone through the full QC process. Those are available now, and we have another six that are listed as in progress, which means that they will be released very soon.                                     Just to give you a few examples, so again, we're tagging proteins to label organelles in the cells. So, a lot of times, you know there's a lot of different kinds of proteins you could use to tag an organelle, so we've chosen a subset of those. So, we've tagged, for example, Tom20 to label mitochondria, Lamin-B1 for the nuclear envelope, alpha tubulin to look at micro tubules, and we also have started doing a lot more endosomal trafficking pathways, so like the endosome, lysosome, peroxisome, for example, and then a few other epithelial markers such as tight junctions, desmosomes, and actin.                                      And so, there's a kind of a bunch of structures. Those are just some examples of what we've been starting with tagging, but one of the reasons why we chose to use induced pluripotent stem cells for this whole model is because they do have the ability to differentiate into many cell types. And, I mentioned earlier that we chose to start with cardiomyocytes as a key cell type to look at, and so all of our cell line, as part of the QC process go through a cardiomyocyte differentiation protocol. And that kind of helps us ensure that the cells are pluripotent and that they can become a defined cell type and that the structure that we've labeled still is present in that differentiated cell. But it also means that we can start looking at some really interesting things in terms of how these structures change during differentiation and change from the stem cell state to the cardiomyocyte state. And so, one thing that we really started doing towards the end of last year, and we have lines coming out, hopefully soon on some cardiac specific tags. And so, to give you a few examples of things that we're working on, we have cardiac troponin I 1, and this I think will be available, I think it's passed QC and will be available pretty soon. And then we also have, we're working on sarcomeric alpha-actinin, titin, some gap junctions so that connexin 43, and then also starting to do a few signaling pathways and one that is of particular interest for the cardiomyocyte field would be beta-catenin for Wnt signaling.                                     So, we are kind of expanding on that list as well. So, we're really excited to start looking at these cardiac structures in the cells. Brock Roberts:             One way to summarize kind of, our strategy and one thing unites all of the different gene and protein targets that we have produced and focused on so far is to really think about the product gene or the protein as a reporter for an organelle or a structure in the cell. So, there are of course an extraordinary number of genes and proteins using this method, and there are many different justifications that would fly for why you would target a particular molecule, a particular gene, a protein of interest, but, what we really try to focus on are proteins that serve as a reporter for a structure.  Andrew Landstrom:     So, have you tagged any ion channels? Brock Roberts:             We have several targeting experiments that are, that take advantage of tagging the transporter molecule. One that is available is a transporter in the mitochondria, a transporter to the outer membrane, Tom20. And we're also making connexin 43 available for gap junctions. These proteins that function as trans-membrane transporter molecules accommodate the approach quite well.                                     Another that is a bit further behind, but we hope to make available before too long would be marker of the sarcoplasmic reticulum and cardiomyocytes. This is a serca protein. Andrew Landstrom:     So, with all these cell lines at your disposal, you've spoken to, sort of, the dynamic changes that occur both in differentiation of cardiac myocytes and cellular development and cell physiology, what are some other thoughts that you have that these lines might be able to show us? What are some fields that might be immediately informed by these models? Kaytlyn Gerbin:           I mean, I guess just kind of on a big pictures I think that having the ability to study live cells and look at different structures in the cell will help us better understand structure-function relationships. So I think that in cardiomyocytes that, you know, makes a lot of sense, but I think even just in the stem cell field, being able to understand how localization of a particular organelle corresponds to a different state that the cell might take.                                     And so we kind of are thinking about a lot of these different stages and states that the cell can pass through and how do we characterize that based on just kind of at a healthy or just kind of quiescent state, and then comparing that to different protivations, so looking at disease or maybe change in time, change in mutations, drug response, response to stress and how are the structures changing and how does that kind of dynamic integration effect how the cell behaves as a whole?                                     And I think that that's one thing that we're really trying to do at the institute that is out of the scope that a lot of federally funded academic labs can do. A lot of times people are focusing on specific pathways or a specific molecule or a specific protein and don't necessarily have the bandwidth to look at the cell on a systems level. And so, kind of as Brock mentioned, with doing these different proteins as tagging the organelles we're hoping that being able to integrate that and generate enough data where that starts to become predictive I think can be really, really powerful. So... Brock Roberts:             Yeah, and there's another thing to add that's is kind of a larger thought that we are very preoccupied with and interested in, which is to take kind of a post genomic view of biology and cell biology in particular. Genomics has been so explosively successful in allowing us to document and document the state of cells at the level of which genes, which of the many, many, hundreds and thousands of genes are active in a particular cellular state, in a particular cell type or particular state that that cell's in.                                     We can easily get lists of genes that we know are functioning and turned on. What we want to do is take that to the next level and start defining a cellular state as a combination, a particular combination of dynamic behavior of those molecules which we can actually see. So we want to be able to see the parts work together. Not just have a list of the parts, and define states in that way. Kaytlyn Gerbin:           Yeah, and I think you kind of asked about what kinds of communities might find these tools useful and I think lot of the disease, we're thinking about how this might apply to disease modeling or drug screens or even developmental biology and kind of studying things like that, so I think a lot of our, we have some collaborations, and we've also been really trying to expand what kinds of groups and communities are using the cell lines.                                     There's been a lot of, kind of positive feedback on people taking, you know, a highly defined cell type that has a lot of QC done, and then having the right tools to be able to start to look at things like that. So, I think we kind of mentioned some of the tools we have, but I just to kind of restate that, all of the cell lines that we listed, along with many more, are available at Coriell.                                      And then, in addition to that you can get the plasmids that we've used, which have gone through also an extensive QC, so if people are working on patient derived, on their own patient derived IPS lines, you know, you could get the plasmids for whatever reporter and then put those in to your own cells. And we do have protocols available that describe our whole process in a lot of detail for how to do that and kind of different QC steps along the way. Brock Roberts:             Yeah, we describe each targeting experiment in enough detail for it to be, we hope, recapitulated in any human cell line or cell type without too much strain on behalf of the person that's doing that work. So we hope to kind of inspire people to try this, even if they might not be familiar with it. Kaytlyn Gerbin:           Yeah, and another thing is that our data is also available, so all of our imaging data that we're doing, and then, you can actually find that we have a website called the Allen Cell Explorer. And from there you can go through and look at all of the imaging, no, pretty much all of the images that have gone through our pipeline are now on the website. So you can go through and actually look at individual cells that were imaged during what, you know, as live cells, and then look at different structural tags that are in there.                                     Another thing that you can see on that is the predictive modeling, and so what we're able to start doing is predict the structure of, let's say, mitochondria based on the nuclear shape and another organelle that's in there. So, we're able to start doing a lot of that. So that, I think, will be really useful to people.                                     We going to add about the label free...? Brock Roberts:             Yeah, and some of the more interesting results that have emerged recently are, are the ability to infer through machine learning approaches and neural network approaches the status and state and sub-cellular localization of certain organelles in the cell and structures in the cell that are actually unlabeled. Those can be inferred from the sort of sophisticated analysis of bright field, you know, images that are not displaying any particularly obvious properties, any tags or anything like that.                                     But because the work has been done in the background to train these models and deep learning approaches with individual cell lines that do have these very specific reporters of distinct structures and organelles, because that data set exists, our modeling team and imaging team is able to appear actually quite deeply into the state of cells that are actually not labeled. Andrew Landstrom:     Wow. Brock Roberts:             So, it's pretty, pretty interesting. Kaytlyn Gerbin:           Yeah. Yeah, we don't have that up on our website yet, but that's in the works to get that actual predictive model. So essentially what that would mean then is that you could take a bright field image in your own lab and then put it into this model, and then get information about maybe where the nucleus is or where the mitochondria are or where the actin is predicted to be.                                     And all that is actually trained off of thousands and thousands of images that have come through the imaging and then the modeling pipeline. So, I think that that tool itself, once that is out and fully QCed I think could be, have a big impact right away. At least we're hoping that it will. Brock Roberts:             Hoping. And those computational algorithms are among the publicly available tools that we have that can be found through our website, and our publications that are coming out. Andrew Landstrom:     That's absolutely fascinating. Are you able to provide a specific example of how you've used, sort of, artificial intelligent, deep learning predictive modeling to infer a physiologic sale or response that was not directly observed? Brock Roberts:             Well, I think the response is, we're really hoping to go in that direction. To use this to, I guess, if you will, take shortcuts toward a response in the form of a state change after we alter the environment in some way, or perhaps alter the genome to mimic a disease, mutation, or something like that. Right now, we are building the relationships. So, we can, we know, and I guess one example we can give is progress through the cell cycle. Kaytlyn Gerbin:           Yep. Brock Roberts:             That would be one kind of clear example that we, we haven't done a lot yet to manipulate the cellular environment or trigger cells to go through different states, but obviously cells that are in culture and proliferating alter their state by progressing through cell cycle. So that's one example that we can detect. We can clearly look at how the morphology of cells and different cell cycle states that emerge that are their chromosomes are compacted or dispersed as they undergo synthesis or undergo division, progress through metaphase and so forth.                                     We can look at those cues and connect the state of the cell with respect to the cell cycle, to the state of some of the organelles, with the state of the mitochondria, for example. And we're hoping that same approach will hold up when we trigger, in some cases, more subtle changes to the physiology of cells. Andrew Landstrom:     That's particularly fascinating. I think the, you know, the ability to leverage that in the setting of, like you were mentioning, patient derived IPSCs from heritable diseases. You know, these sort of monogenic disease models that impart a biophysical defect in the cell could then be modeled and not only directly observed, but perhaps indirect cellular physiology might be inferred in a way that we really haven't been able to do so previously. Brock Roberts:             Absolutely. Yeah, there would be, in some cases there are monogenic disease mutations and pathologies that we know ought to have an effect, and we're really excited to see if that holds up, and how that holds up and what their phenotype is when looked at in this sophisticated way. And then there are other, more mysterious mutations that would be really excited to see a phenotype in. Kaytlyn Gerbin:           Our goal at the institute is to build the tools and provide the resources to the community to be asking these kinds of really detailed, very interesting questions. I mean, I think there's definitely interest in doing some of that work here, but our main focus is to design the tools and the methods and make that all available to the public as soon as it passes our QC. So, that's the, those are the kind of thing that I think the community will have a big impact on, testing these kinds of things in their own systems given you know, new tools and ways to do it, so. Andrew Landstrom:     Right. Brock Roberts:             Our whole, our whole ethos is to cooperate and to facilitate. And rather than compete with other investigators, we want to make things possible and that are shared and open. For example, our list of genes that we went with to target, that was on open collaboration. We asked as many specialists in the academic community as we could to develop a consensus of what would be the most useful markers for different organelles. And we chose those proteins and genes. So, we're really trying to be collaborators, as best as we can. Andrew Landstrom:     Are there specific examples of collaborations that you've felt were particularly productive or yielded some new exciting insight? Kaytlyn Gerbin:           Mm-hmm (affirmative), yeah. I could give you a few examples. So, Doctor Ben Freedman, who is at the University of Washington, he is working on kidney research. And so, he has a few of our cell lines, he actually, it's convenient because we are located right across the street from each other, so we'll see them fairly often, but, yeah. So, he works on kidney research with the different cell lines and he really wanted to get the cells into a 3D context. So, he is working on a lot of different tissue engineering to study developmented disease.                                     And so, he's also starting to make his own, their own mutations in the cell line, and so that's been, at least so far, that's been one collaboration that's I think has really been very powerful. And it's cool because we don't have the bandwidth right now to be looking at kidney organoids, but I think it's showed, kind of, the power of these kinds of cells and tools that, you know, when you have that you can do the live cell imaging with different structure within the same kind of organoid and you can get a lot of information, and so ... Andrew Landstrom:     Yeah. Kaytlyn Gerbin:           That, that's been fun to see develop. Another one that I know, Chris Chen at at Boston University is using our lines and is making cardiomyocytes with them as well and looking at the effects of patterning. And patterning is something that we also planning to do here, but that collaboration has been great to kind of get things going.                                     And we've also been working closely with a group at the University of Washington, Georg Seelig's lab, who's developed a new way of doing single cell RNA sequencing. And so, that's been fun, we've been looking at that with stem cells and then cardiomyocytes to, kind of help, help us figure out what the different states that the cells are in. And then that is going to help and form, kind of, what future tags we might do or when, when to do imaging or kind of what protivations we want to put the cells through. Andrew Landstrom:     That sounds like you're spanning the gamut really of downstream experimentation on these lines. Brock Roberts:             Yeah, and we've also had a lot of people buy the cell lines. Kaytlyn Gerbin:           Yeah. Brock Roberts:             Acquire them through the Coriell, we hope that each case of their productive application toward different research questions could be defined as a mini collaboration. Maybe we'll hear from some of these people. And in some cases we have, and there may be more things that spring out of that. Kaytlyn Gerbin:           Yeah, I think like, because the lines are available through Coriell it's, it's a little early to start seeing publications from the stuff, because we're a pretty new institute, but we do keep track of where the lines are going and, I mean it's exciting to see, I mean, pretty much all throughout the world there's people ordering the lines and starting to do research in a lot of different kinds of systems. Brock Roberts:             Right. Kaytlyn Gerbin:           So, we don't always necessarily directly collaborate with the people that are using the lines, but a lot of times we do hear from them or we'll run into people at conferences or something who have been using our lines. So that's really fun to see that its, our, you know, the work that we're doing here is actually producing things that people in the community are finding informative and useful. So, that's always fun. Brock Roberts:             It's still so early in this project. I mean we're just at the beginning of a lot of collaborative potential. So, we really hope to see this take off. Andrew Landstrom:     Yeah, and if people listening want to collaborate or want to learn more, how can they learn more and how can they get ahold of you all? Brock Roberts:             Oh, hold on, I think, first of all, we really want to funnel people to our website. We think it's a really great resource and at that allencell.org you can contact us through that link. We look forward to hearing from you. Kaytlyn Gerbin:           Yeah, so you can start with the website there. And as we mentioned before you can find all of our cell lines, plasmoids, protocols, etc. on this site. And we've also started to do a few more instructional videos, and so those are coming up on the website, too. So, some things, you know, especially as more groups are starting to use the lines, we do have the detailed protocols, but I think groups that maybe haven't done stem cell culture before or haven't worked with these kinds of IPS lines before, we're trying to provide as much content for people to make it easy for them to do the research. So we're starting to do more, sort of instructional videos. Brock Roberts:             Yeah, and we seek this out. We want to hear from people. It's not a bother. We're trying to get as much, we're trying to get the, we're trying to branch out and communicate as extensively as we can. Kaytlyn Gerbin:           Actually, one thing I just thought of that I want to add in her is that we have started to work with a few stem cell cores. And so, right now, I mean- Brock Roberts:             These are core facilities at universities. Kaytlyn Gerbin:           Yeah, stem cell core facilities at, yeah, exactly. So, part of trying to distribute the lines is that if we can, you know, individual investigators could get our lines from Coriell and get the licensing and everything to do that in their own lab, but it's, I think, going to be really great if we get some connections with the stem cell cores because then once we can provide the lines to them, they can distribute them to investigators that are part of the core.                                     And so, so far, we already have agreements in the works with University of Washington, UC Berkeley, and then the Salk Institute, but this is something that we're really hoping to expand this year. So I think, in particular, you know definitely contact us if there's questions about the lines or anything, but if you are part of a stem cell core at a university and you think that people at the university would be interested in using our lines we're working really hard to make, you know get, kind of, packages, protocol packages and everything available so that we can get these lines set up in the stem cell cores. Brock Roberts:             Right. Andrew Landstrom:     Well Brock and Kaytlyn, thank you so much for joining me. What an incredible resource that you all have created, and I especially appreciate how open and transparent you are with your lines and your quality control and how you just really, you know, try and strengthen collaborations and to start new ones. Brock Roberts:             Thank you very much for the conversation. Kaytlyn Gerbin:           Yeah, this has been fun, thank you. Jane Ferguson:             I hope you enjoyed listening to this episode of Getting Personal: Omics of the Heart. Let us know how we're doing by leaving a comment or tweeting at us at @circ_gen. We love to hear from you.   

Transcript for January 2018 Podcast Circulation: Genomic and Precision Medicine     Jane Ferguson:                 Hi, everyone. Happy New Year. You are listening to "Getting Personable: Omics of the Heart". I'm Jane Ferguson and this is episode twelve from January 2018.                                                  This month I have some exciting announcements to make. The journal formerly known as "Circulation: Cardiovascular Genetics" has a new name. As of this month, the podcast is brought to you by "Circulation: Genomic and Precision Medicine". We're still publishing papers focused on cardiovascular genetics but as genomics and other omics have expanded our scope has grown to so much more than just genetics.                                                 The new name, "Genomic and Precision Medicine" signifies the journals focus not only on genetics, but also genomics and all the other omic technologies and the feel of precision medicine. Along with the new name we have a new editing team. Dr. Kiran Musunuru, an associate professor of cardiovascular medicine and genetics at the Perelman School of Medicine at the University of Pennsylvania has officially taken over as editor-in-chief. He has already been implementing new initiatives to allow the journal to serve authors and readers even better.                                                 Along with create original research articles you can find accompanying editorials, videos and interviews with authors, including the interview we're featuring in this month's podcast.                                                 Finally, while "Circulation: Cardiovascular Genetics" was published every two months, "Circulation: Genomic and Precision Medicine" will now be published monthly. So, you can look forward to a new issue every month and even less time waiting for the newest research to be published. Check out the latest issue and all of the new material at circgenetics.ahajournals.org and follow us on Twitter at Circ_Gen.                                                 Now, along with the name change for the journal, we have another name change in the pipeline. Our AHA Council, Functional Genomics and Translational Biology, is also being renamed to "The Council on Genomic and Precision Medicine". As with the journal name change this better reflects the evolution in our scope and focus. This name change will be formalized in the coming months. So, if you are one of the many people who could never remember what the acronym FGTB stood for or what order all those letters came in, your struggles will soon be over.                                                 We have a number of interesting papers published this month, including an article by George Hindy and colleagues on how smoking modifies the relationship between a genetic risk score and coronary heart disease; a mendelian randomization study from Jie Zhao and  Mary Schooling on coagulation factors and ischemic heart disease; an exome wide association study of QT interfolds from Nathan Bihlmeyer and colleagues; a study on genetic testing of cardiac ion-channelopathies and still births from Patricia Munroe and colleagues; and a genetic study of cardiac disfunction in Duchenne Muscular Dystrophy from Tetsushi Yamamoto and colleagues.                                                 You can also catch up on the genetic cardi-oncology literature with a review by Marijke Linschoten and colleagues on chemotherapy related cardiac disfunction. And read a clinical case on left-ventricular non-compaction by Vi Tang and colleagues.                                                 Finally, we also have a scientific statement on the use of induced pluripotent stem cells for cardiovascular disease modeling in precision medicine by Kiran Musunuru and colleagues.                                                 Moving on to our feature article, Andrew Landstrom, an early career member of the Genomic and Precision Medicine Council, formerly FGTB, talk to Guillaume Paré and Sébastien Thériault about their article published this month entitled, "Polygenic Contribution in Individuals with Early Onset Coronary Artery Disease". In this paper, Dr. Thériault and colleagues report the use of the genetic risk score which improves on our ability to predict very early onset CAD. Listen on to the authors talk more about the background to this study and what they learned along the way. Andrew:              Welcome. My name is Andrew Landstrom, an assistant professor in the Department of Pediatrics, Section of Cardiology at Baylor College of Medicine. I am a member of the early career committee of the American Heart Association Council on Genomic and Precision Medicine, previously the Council Functional Genomics and Translational Biology. I'm joined today by Sebastien Theriault, assistant professor in the Department of Molecular Biology Medical Biochemistry and Pathology at Laval University, and Guillaume Pare, the Canada Research Chair in genetic and molecular epidemiology, assistant professor in integrative health bio-systems and associate professor of medicine at McMaster University. Guillaume:          Hi. Good morning. Andrew:              Well, I'm wondering if we could just start by introducing ourselves maybe a little bit more thoroughly than I just did and talking a bit about your research paper and what brought you to this as a research question. Guillaume:          Absolutely. So, this … [inaudible] and thank you for having us.                                 My name is Guillaume Pare, and as stated, I'm an associate professor at McMaster University, and I would say like my longstanding clinical interest is about individuals and families with very early coronary artery disease and heart disease. And this really was the basis for this project and to try to understand why do some people in family are afflicted by this disease when we cannot find any of the conventional risk factors. And as Sebastien came to join me and this endeavor, and spent two years with us here at McMaster and was instrumental in getting this project off the ground. Sebastien:           Yes, exactly. So, I was a physician trained in Quebec City and I went to McMaster University as a research and clinical fellowship. And that's where I did some cardiovascular clinics with Dr. Pare and that's when we noted that some patients with early coronary artery disease didn't have much explanation for their disease. So, that's how this project stem, that we wanted to understand what was going on and we thought that really genetic factors could be involved. Andrew:              And speaking of these genetic factors, in fact, you established a genetic risk score as sort of a way of aggregating a large number of genetic variants into a single prognostic risk indicator. How did you come up with the score, and where did these genetic variants that you aggregated come from? Sebastien:           So, the results of many of our studies looking at the association between common genetic variants and coronary artery disease have recently been released. For this study, we use the variants identified in the latest CARDIoGRAM for C4D consortium meter analysis, which includes more than 60000 individuals with coronary artery disease and 120000 individuals without coronary artery disease from a total of 48 studies. Most of the participants in these studies were European. And so we decided to use the independent variants that were associated with the disease in that very study and look if we could predict early coronary artery disease in some patients. Guillaume:          Andrew, maybe I'll backtrack a little bit. The initial idea about the gene score, first of all came from the observation that a lot of the patients who we're seeing do not have any traditional risk factor. The second observation is that we already knew that genetic risk scores are predictive of coronary artery disease. But the key question is, is it possible that there are people at the extreme of severity of a cardiovascular or genetic risk score that could be at much, much higher risk of having the disease. And this is what the hypotheses really that we wanted to test is whether these genes scores they could identify people that clearly have outlying risk, outlying genetic risk of having the disease.                                 And to explain, the patients that we were seeing a deflation in the clinic will clearly have an outlying risk of disease because they have a First Earth attack or multi vessel disease in their 30s or 40s, and we thought that this cannot be just like bad luck, there had to be some ... and this something is really most likely genetics. We cannot put a finger on it because all the known mutations that we know could cause this, well, we're just not finding them. Andrew:              Sure, sure. And there's certainly having a large number of genome association studies, which have implicated a number of common variants and not so common variants in coronary artery disease. So, is this where some of this idea behind the genetic risk or was initially thought of? Guillaume:          Absolutely. And I think you know ... and this is where Sebastian really came in and to really like look at this literature, to feel like the variants that went in into the score. Andrew:              And certainly to go to your earlier point, it seemed like you were saying early on that coronary artery disease would be a great phenotypic model to explore this question in, mainly because it would seem that at that age, with that severe disease, that it must be something innate to that person, and genetics would certainly play a role. Guillaume:          Absolutely. And to me, it's more than simply scientific because we see these patients at our clinic and we've got a lot of ref roles for these patients, and we really feel for them because they're really young people, and I think like when we think about genomic and like preventative medicine having an impact, I cannot see a greater impact than preventing a first heart attack in the 30s or early 40s. So, this is a ... it's a very vulnerable patient population. It's also a patient population that has a lot of questions about why this might be happening to them, and often what we see is that, I think everyone feels that clearly there's a genetic component, and one, a loved one has first attack in his or her 30s, this raises questions for the whole family really, and it clearly sends a shock wave in the family, and everyone, I think rightfully, is quite scared of having the disease and the fact that there is no answer for these people, to me is a huge unmet clinical need. And it's just for the sake of providing people with answers. Andrew:              Yeah. Absolutely, I think it's certainly a clinically relevant question that you attempted to answer. And to try to get to this a little bit, and you utilized a large UK-based biobank as your primary study population to establish this risk score. Can you tell me more about this biobank and what sort of data you were able to obtain from it? Sebastien:           Sure, I can speak a bit about it. So, the UK biobank is a large prospective cohort of about 500000 individuals between the age of 40 and 69, with an average of 58 years, and they were recruited from 2006 to 2010 in several centers in the United Kingdom, and the general objective is to study the effect on the environment and genetics on health. And what's interesting is that the data is made available to the research community worldwide following registration process. And the data in that includes a very vast amount of information, from questionnaires, specific evaluations, such as height, and weight, and aging data, and the diagnosis from the participants, medical charts, in addition to the genetic data of course.                                 And for this study we used the first release of the genetic data, which included information on about 40 million variants in about 150000 individuals, and selected the individuals who had a diagnosis of early coronary artery disease, so aged 40 or less for men, 45 or less for women, and then it underwent a reversed relation procedure in order to identify patients with obstruction in coronary artery disease, and we used all the other participants as controls. And that's basically leveraging this huge amount of data that we were able to confirm the fact that patients with early coronary artery disease, some of them very high and pathogenic components of their disease. Andrew:              That certainly sounds like a really amazing, both biobank and cohort of information that could be utilized. Such a huge sample population with so many clinical variables as well as genetic variables and collected prospectively. What a great resource. Sebastien:           Yes indeed. Guillaume:          It's a fantastic resource and to me, this type of initiative it's a game changer to accelerate research, because with these data being made available, then it's really up to testing new bold ideas to try to improve our understanding of this disease. So, I think you know we have to say kudos to United Kingdom for financing this this great cohort and making it available to researcher worldwide. Andrew:              And you didn't just stop there. You also utilized a local cohort as a foundation cohort for your study. Could you speak a little more about that? Guillaume:          So, that's interesting because this cohort really stems from the patients that we've seen at the clinic. And essentially, we felt this was this huge unmet clinical need. To better address causes of disease, and these roles that's barely a disease. And then we said, well, if we were to do this, let's do this formal, and let's do this properly and collect the information and samples and everything, and we had a very enthusiastic response from our cardiologist, and international cardiologist colleagues that really helped us identify these early cases and send them to us and in our study. And so these are local patients. These are people that we care deeply about, and that's really want to make a difference. And again, you know, when Sebastian was with us at McMaster, we were seeing these patients together, and maybe he can add some of the details there if you want. Sebastien:           Yeah. Just to specify again, these were patients at the very early coronary artery disease, for age 40 or less for men, and age 45 or less for women. And these were patients without the clear secondary cause of their disease. Most of them were clueless about what were the factors that caused the disease outside a few risk factors such as smoking or hypertension, there wasn't clear explanation as to why they had such early disease, and we could see that it was a struggle to try to understand and then see if there is a risk for their family also. So yeah, it was really interesting to find an explanation for some of them, and we did report the findings to a few of them who seemed to have polygenic contribution to their disease, and it did make a difference. They were quite happy to at least have some kind of an explanation to what was happening to them. Guillaume:          And I think that one thing that I think was striking to me when doing this is that when we started to formally collect family history in these individuals, we just realized that and in many, if not most of them, the family history is really striking. And these are folks that clearly has a very severe individual disease, but when we start asking about their brothers, and sisters, and parents, and uncles, you just realized that coronary artery disease was just all over the place and was very aggressive and early. And I think to us, this gave us purpose in this project to say that, 'Yes, we have to do something about this,' but also, I think it also reassures us that our primary hypothesis was right in thinking that there has to be a genetic component that goes beyond just having bad luck, and this genetic component was expressing itself by the family history that we saw.                                 And a further clue that I think we might be on the right track is that the pattern of inheritance didn't shift one of the single mutations that aggregates in a family and that can explain the disease. So, the disease was more diffuse and oftentimes it was both from the paternal and maternal branch of the family without a clear genetic pattern that would be more in line with the so-called mendelian disease, where a single gene mutation causes the disease. And I think really that puts to us in the mind that we might be looking at the different modes of inheritance, and this is partly how we came with this idea of looking at gene scores in these individuals and families. Andrew:              So certainly a close clinical connection to the patients and their families that you're trying to risk stratify and certainly, it sounds like clinical suggestion that you were dealing with something genetic and inheritable, but not necessarily mendelian, where one gene defect leads to say an autosomal balanoid express disease, more of a polygenic family history exactly. Guillaume:          Exactly. Andrew:              And so with these two scores and this genetic risk score, what exactly did you all find? Sebastien:           So first we found that participants from the UK biobank who had this early coronary artery disease had a very significantly higher number of common genetic risk variants. So the score was very significantly higher in these patients. And what was interesting too is that the increase in risk that was associated with the score was independent from traditional risk factors such as smoking and high blood pressure. And when we looked in the local cohort with early coronary artery disease, out of 30 participants that were involved, we found seven with a significant polygenic contribution, which we define as, a two-fold increase in risk, and one of the participants actually more than six-fold estimated increase in risk. So we really did identify an explanation for some of these participants with the early coronary artery disease. Guillaume:          And I think this was maybe a bit of a eureka moment to see that some of these individuals actually had a much, much increased risk of disease based on the polygenic risk score, and this really was the primary hypothesis that when looking at extreme of disease, which is what we're looking at, we might find extreme of genetic predisposition. But the one thing I thought that's quite striking is then we went back to think all that. And to try to put this in perspective with what we would usually do in these patients that we've done already, and to look for mutations that cause familial hypercholesterolemia.                                 Familial hypercholesterolemia is a disease of cholesterol metabolism that leads to a much increased concentration of cholesterol and early coronary artery disease, and a discovery that led to a Nobel Prize for Goldstein and Brown, back in the day, and really like, up to this point, when we see people with early disease clinically, this is what we will be looking for. And certainly, there's a lot of these individuals that have very high cholesterol and a lot of them is due to familial hypercholesterolemia. But it's a minority of patients really. It looks like we're having an association and this gene score concept is really panning out. But I wouldn't compare to familial hypercholesterolemia, and I guess that the results were kind of surprising to us and I think we had to take a step back and think about the implications. And I don't know, Sebastien do you want to describe these results or ... Sebastien:           Yeah, sure of course. So we've looked at how frequent this polygenic contribution to coronary artery disease could be. So we look at the prevalence of high genetic risk or that would cause a risk similar to familial hypercholesterolemia see the ratio about 3.7, and we realized that one in 53 individuals had an increasing risk that was similar. So that's almost 2% of the population, and that is way more frequent than the actual prevalence of familial hypercholesterolemia, which is one in 250. So in other words, the polygenic contribution could be almost five times more frequent than familial hypercholesterolemia. Andrew:              But yet not all of those individuals manifest as disease, which sort of hits as something that's a common thread in genetic association studies where we're trying to describe sort of multifactorial disease en points with finite genetic and a whole spectrum of acquired disease, required lifestyle modifications and things. So no model is 100% perfect, and so where do you think that additional variation lies, either in the reduced penetrance of some of these disease phenotypes, or are there other genetic loci, or are these all secondary to acquired changes that happen, or where does some of that variation lie? Guillaume:          Well, to me I think there's two parts to this question. The first one is that I see the cells study as in some sense, proof of concept, to look for the concept of very high burden of polygenic risk as a mendelian equivalent really. But the fact is that, especially with the new discoveries and the genetics of coronary artery disease, the gene scores that we've been using for this study could be much improved. And I think the concept is there, but the gene score could be improved, and I think they will be improved and I think in three, four, five, ten, years from now, they're going to be even better because we will have many more variants that we know are preceded with coronary artery disease and that might be upwards to 1000 variance, for example will have much better gene score I think we'll have much more predictive gene scores.                                 So I think the concept is there, but I think it's going to improve, with the years is only going to get better. And I think part of this missing risk, if I may, is due to the fact that we're missing a lot of genetic variants associated with coronary artery disease, and I'm very confident that the community will find them in the years to come.                                 I think the second part of the study is that, that being said, I think genetic risk is obviously important but we shouldn't neglect also classical risk factors. And a lot of [inaudible] … they did have the classic risk factors and that was a fairly high proportion of smokers, and a few cases of diabetes, and I think that individually, this risk factor wouldn't be enough to explain the aggressiveness of this disease. But I think the fact that we do find an enrichment for these factors also give us ... I think it feeds the idea that it's not only genetics and that even in these individuals classic risk factors do matter and trying our best to decrease the burden of these risk factors on a community and its role family level is probably also very important. Sebastien:           I'd also want to know that there's an environmental part that's involved even in these individuals with high genetic risk. And as he just mentioned, we did notice a high proportion of traditional risk factors in patients with early coronary artery disease even in some of them with high polygenic score, some of the environmental factors seem to be also involved in their disease. Guillaume:          And to some extent I think that's going to be an interesting research question, in these individuals with very high polygenic burden, do traditional risk factor, do they at the time, are they stronger or weaker, is there a synergistic effect between, for example, smoking and being at this extreme of the polygenic risk? And these are kind of open questions that we couldn't address in the current study but I think will be interesting to see in the years to come. Andrew:              Absolutely. I think there's definitely a road ahead of us but this is definitely a step in the right direction. What are some of the practical applications of this genetic risk scores, either from your study or from others in the identification of individuals? Is it something that could be used for primary production? I mean, in theory, this could be done at birth. You could be screened for these genetic variants and the risks will be calculated within the first days of life. What do you think are the practical applications of this and where is this fit into a rapidly expanding world of clinical genetics? Guillaume:          Well, I think you know what you've just described is exactly how I see the future, and I think that if we want to be consistent, and we consider folks with a familial [inaudible] mutation to be at higher risk, I think that someone with a predicted polygenic risk of twofold, threefold, or fourfold increase risk of coronary artery disease should definitely be put in a higher risk category when it comes to primary prevention irrespective of other risk factors, or maybe like in combination with these other risk factors, and I think should be treated accordingly. And as we see, these are people are very aggressively affected by the disease, and I think the sooner we could identify these individuals at high risk and try to intervene to lessen as much as possible this risk, I think we will do these individuals and families a great service.                                 So I think it's definitely a case for primary prevention and especially in a world where genomics is more clinically prevalent and used, also we see a role for this and the role that's already affected. And to me personally, I see great value in providing people with answer on why they've had an event and probably providing an answer not only to them, but also to their families. Andrew:              And so if something like this were to be able to be applied broadly in the clinical arena, what sort of steps do you think need to happen from this point forward to make this sort of testing ready for prime time? Guillaume:          This is a great question and I have to say that my passion I would say is to bring genomics to the clinic. I think there's a long road ahead to make this happen. But I think there's two main obstacles. The first one is that I think there's a knowledge gap between people that do this 24/7 like me, and I think you know the rest of the community and that there's been so much rapid progress in the field of genomics in the last few years that I think there's a lot of education to be done for people to catch up and just the concept of polygenic risk.                                 I think only a minority of clinicians will know about this and very rightfully, because right now it's in the realm of research papers. So I think to make this happen, there's a huge role in education and awareness. I also think that our hospitals ... or maybe it's a Canadian thing, are not prepared just for the flow of information and how to derive the routines commercially, and probably how to handle these highly multi-dimensional data and to be able to take the right information out of them and I think in this world, I would think that probably the best way to do it is to do it in a way that these gene scores can be updated, the science progress. But we're so far away.                                 Sometimes I feel that our hospital system is struggling to provide [inaudible] time to clinicians. And I'm just thinking without the prevention or how to handle something as complex as polygenic score, in this case we barely had like all the plugs in 200 variants, but you could clearly imagine like genetic risk scores being done with hundreds of thousands, if not millions of variants and will bring a whole new set of challenges. Andrew:              And Sebastian, do you have a perspective on this? Sebastien:           Yeah. I would just add that this knowledge is in the research community but to really put that into the clinic there's old setting, you have first to interpret the results and also to disclose the results to patients in a way that they can understand and that wouldn't create unnecessary anxiety, but more give them informed and an informed view of their health. So there's this also translation to the patient that needs to be evaluated and developed for it to be used to mainstream I would say. Guillaume:          And I think the classic tools like publications also presentations and meeting and even reaching out to the cardiology community to start discussing these concepts will be important. And clearly it's a big shift from just classic genetics and even familial hypercholesterolemia, I think there isn't a lot of awareness, I don't think there's enough awareness as far as I'm concerned. And then we're bringing new concepts that might be even further remote from what people have been taught about genetics and score, it's going to be a huge challenge, but we have to. And I think the great thing about the medical community as far as I'm concerned, is that every time that there's been something that was worthwhile to do clinically, the community has always come around and making sure that these things are implemented and made available and everything. So I'm also very confident, but I think there's a great challenge ahead as well. Andrew:              It sounds like the challenge has a potential for great benefit and if proper partnerships between the clinicians, and the geneticists, the scientists, and the patients and their families can all sort of come together to establish a path forward for this type of information to be applied clinically. Guillaume:          Yeah, absolutely. And I really like that to add there that you've put the clinicians, geneticists, and patients as well. I think it's very important, patient advocates are a very important part of the equation here. Andrew:              Going forward, are other disease processes besides early onset coronary artery disease that you all feel might benefit from a similar polygenic risk? Sebastien:           The recent studies show that a lot of complex traits seem to have polygenic origin. So traits like hypertension, diabetes, obesity, atrial fibrillation, for example, they show a similar genetic architecture where there seem to be combinations of a very large number of common variants that explain the genetic risk. So it's a big number of variants with smaller effects that seem to be responsible for the appearance of these complex traits. So this concept could potentially be applied to a lot of different diseases. Guillaume:          I think I would maybe just go even one step further, but I really have the feeling that most late onset disease actually has a polygenic architecture, which means that similar polygenic risk score could be done targeting the extreme of distribution to look into this. I mean obviously, I think metabolic traits, diabetes, hypertension as Sebastien mentioned, but probably why not some cancers, or [inaudible] or any of the large number of disease where a polygenic inheritance either has been proven or is highly suspected.                                 So I think that we will hear a lot of polygenic risk score in the future, and I might be biased here, but I think it might become a staple of clinical practice that people will be looking at polygenic risk for a number of disease. And I think the great thing is that now that we've got genome-wide genotyping that is really affordable and we can type with statistical imputation and tens of millions of variants, then I think one concept is that we only have to genotype once and then we can derive these polygenic risk scores for ... why not a dozen diseases that are important and are actionable and really like turbo charge primary prevention by using this information. I might be getting ahead of myself, but I really think that this is something that we might see and that for us, we should see. Andrew:              And certainly that seems to be the way that at least the literature is trending, definitely towards more, and more data and more, and more exploration into a number of diseases that may have mendelian inheritance pattern but may also have a significant component that's polygenic, particularly like you were saying in those individuals that present at the extremes of severity. So I think it's certainly where we're heading.                                 Is there anything else that either of you would like to share about the study that you feel be important? Guillaume:          I think we've covered a lot of ground here, but perhaps the one thing is just to reiterate that this is a proof of concept, but I really think that the act of polygenic risk score will continue to improve for quite a while, and as it improves, it will only get better. So we can only move forward with this in terms of the accuracy of the prediction, and I think that that's a great thing and hopefully with this we'll be able to better predict risk. And the other thing as well is that, I would say that at this point we can identify people at risk. And I think it's great because it provides answers, we can target known risk factors.                                 But I think a big part that's still open is, can we use this risk to derive like more individualized treatment, or to actually choose what should be the best way to prevent events in these individuals. And again, I don't think we're there yet but this is something that I think it's worthwhile investigating in the future and maybe trying to dissect this polygenic risk and to see maybe it falls in one or two categories or maybe it's a global risk, and these are all open questions that I think are important, but that are still very much of a mystery right now. Andrew:              Sebastian? Sebastien:           I think we've covered a lot of ground like you said and I don't have too much to add. Otherwise, I think we'll see a lot of these polygenic risk scores in the future and for risk improvement even to understand better the physiology of disease. These are very important concepts. Guillaume:          And I think you know the common approach of physiology is good because these gene scores they don't seem to be associated with classical risk factor. In our study, rather weak association with blood pressure and families history. Now, family history is kind of logical. Blood pressure suggests that perhaps there's an overlap between the two pathways, but clearly adjusting for blood pressure like that only slightly attenuated the predictiveness.                                 So basically what this is telling us is that this polygenic risk score seems to be acting through pathways that we don't know of, that we're not measuring clinically, and I think that’s a big part of the future would be to say, 'well, what are these pathways, and can we actually assess them? Are there other cholesterols out there?' Cholesterol is great because it's causal, we've got synthetic pharmachemicals, you've got tools to decrease it, and we've got fantastic evidence that decreasing cholesterol decrease risk. Is it possible that there's other pathways that are there and that we could do to sign, and I think all of this gives us great clues that this might be so.                                 I think as happens quite often in science, we start with an hypothesis and we try to address it the best we can, and at the end of the day, here I guess we've been lucky because it kind of panned out, but it also opens so many more questions about; So what are these other pathways that these genetic risk scores are capturing that we're not capturing clinically right now. And how could this lead to better treatment, and how to implement this and everything, and I think this is really what's so exciting about doing research, and as far as I'm concerned, doing research that has an impact on people's lives and trying to improve people and provide answers to people. Andrew:              Sounds like a great summary of the rationale for doing this. Thank you very much for joining me and for sharing your work. Guillaume:          My pleasure. Sebastien:           Thanks.   Jane Ferguson:                 Thanks for listening to "Getting Personal: Omics of the Heart." You can subscribe on iTunes to get each new episode delivered straight to you. And we'll be back with more next month.  

Jane Ferguson:                 Hi everyone. Welcome to Getting Personal: Omics of the Heart. This is episode 11 from December 2017. I'm Jane Ferguson, and this podcast comes to you courtesy of Circulation: Cardiovascular Genetics and the AHA Functional Genomics and Translational Biology Council. I'm particularity excited about our interview this month. Doctor Kent Arell from the Mayo Clinic talked to Doctor Jennifer Hall from the University of Minnesota about her role as Chief of the AHA institute for Precision Cardiovascular Medicine. This is a really exciting initiative, which is bringing together researchers, patients and stakeholder to foster the growth and development of cardiovascular genomic and precision medicine. Through their Precision Medicine Platform you can access a virtual big data research hub and find data, tools, and collaborations. The institute also provides funding for projects related to precision cardiovascular medicine. You can find out more and register to access the platform at precision.heart.org. And listen on to hear Jen describe the platform and how this initiative first came about. Dr. Kent A.:                         Hello. My name is Doctor Kent Arell. I'm the Chair of the American Heart Association's FGTB Council early Career Committee, and a proteomic and network systems biologist in the Department of Cardiovascular Medicine and the Center for Regenerative Medicine at Mayo Clinic in Rochester, Minnesota. As part of a highly collaborative team, my research efforts focus on omic space systems approaches for heart failure prediction, diagnosis and therapy. Specifically regenerative approaches to cardiac repair.                                                 This November during American Heart Association Scientific Sessions 2017, FGTB Early Career Committee programming complimented the FGTB Council Precision Medicine Summit with a hands-on bootcamp on network systems biology, followed by a session introducing online computational portals and tools designed to enhance and facilitate basic and clinical cardiovascular research. A highlight of the second Early Career session was an introduction on the use of the Precision Medicine Platform of the American Heart Association's Institute for Precision Cardiovascular Medicine presented by the Institute's lead bioinformaticist, Laura Stevens.                                                 With these recent presentations in the many ongoing developments in precision molecular medicine combined with my own research approach and interests, I'm especially delighted today to be speaking with Doctor Jennifer Hall, Chief of the American Heart Association's Institute for Precision cardiovascular Medicine. Doctor Hall received her PhD from U.C. Berkeley and completed post-docs at Stanford and Harvard prior to taking up her first faculty position. Welcome, Doctor Hall. Is there anything I've missed that you would like to add? Dr. Jennifer H.:                  No, you're doing a great job, Kent. Dr. Kent A.:                         All right. Doctor Hall, the Institute for Precision Cardiovascular Medicine is one of the newest additions to the American Heart Association. And as far as I'm aware, you've been involved with the Institute since the beginning. Could you first tell us a little bit about the history of how the Institute came to be, and perhaps how you first became involved? Dr. Jennifer H.:                  I would be very happy to do that. So the Institute actually started in 2013 in the Fall, when the American Heart Association Board of Directors made an initial investment. And the vision for the Institute for Precision Cardiovascular Medicine was Nancy Brown’s, our CEO. And in the early years it was really led by the Chief of Staff, Laura Sol. And our lead strategist, Prad Presoon. And the original grants came out from the institute but was then called the Cardiovascular Genome-Phenome Center, or Study. Dr. Kent A.:                         Right. I'm familiar with that. Okay. Dr. Jennifer H.:                  Yes. That was the very early history. And it was rebranded in 2015. So that's the history. I joined about a year and a half ago now. And I'm just thrilled to be part of the team. Dr. Kent A.:                         Well, there's exciting things on the horizon, definitely. So what is the mandate of the institute? And what are its principal or primary objectives then? Dr. Jennifer H.:                  So the mandate is to really provide a better understanding in the area of precision cardiovascular medicine to all individuals. And that means participants across the United States and across the globe, patients and those that are healthy individuals as well. And finally to scientists, researchers and clinicians. Dr. Kent A.:                         Okay. Dr. Jennifer H.:                  And the five things that we are really focused on in terms of, I would say our principal objectives are really to convene people all over the world, experts, students, trainees. Provide transformative grants and this is at least half of our budget every year, if not up to 80% of our budget. Enable data discoverability and access. And that's through some of the new tools that we are creating. Act as a translation agent. And we can talk more about that if you'd wish. And finally to offer research enabling services or tools to our young trainees as well as our established investigators. Dr. Kent A.:                         Okay. Perfect. Well, I think I had some questions to cover most of those topics. But the translation agent would be interesting, if you wouldn't mind sort of expanding on that a little bit right now maybe, perhaps before I get to the other questions I had. Dr. Jennifer H.:                  Yes, absolutely. I think this means a lot of things to a lot of different people. Like one of the ways it can help people are, scientist that have been very focused in academics for a long time has not thought about intellectual property, or ways to really begin to commercialize their ideas and take them forward. So we used to think about bench to bedside. And the institute is focusing on helping these individuals. Helping these grantees really begin to think through how to talk about their ideas and to take them to market. So I'd say that is one way of thinking of the translation agent.                                                 The other is to really begin to take participant data and to bring new people into the field. So not only will we think about volunteers as being Heart Walkers anymore or being Go Red for Women volunteers, or one of the many 30 million volunteers we have throughout the United States within the American Heart Association and around the globe. But we want to ask them to contribute to precision medicine, and ask them to be a part of this new exciting movement as well and contribute data, interact, provide answers to surveys, and be educated a little bit more in the area of precision medicine. So those are the two big ways that we think about acting as a translation agent. Dr. Kent A.:                         Okay. Excellent. With respect to intellectual property, is there an intermediary that you connect researchers to, to facilitate that? Or is it more of an awareness of how to approach your own institutional technology transfer offices, or what have you? Dr. Jennifer H.:                  Well, you're going right where I would've led the conversation. So we're trying to do both I should say. Dr. Kent A.:                         Okay. Dr. Jennifer H.:                  It's just originally getting off the ground and talking to our volunteers at those academic institutions and figuring out the best way to do that. And then talking to some people in the industry as well to figure out how does it work best coming from that side. Dr. Kent A.:                         Okay. Excellent. Well, I think the next thing that I might transition to is the grants, because points one, three and five that you made about convening people, enabling data access and enabling services and platforms may all sort of relate to the Precision Medicine Platform. And we'll definitely be speaking about that quite a bit. But one thing I wanted to mention is the variety and the number of grants that have been awarded by the institute since it inaugurated grants I believe in 2015, is that correct? Dr. Jennifer H.:                  Yes. That's exactly right. And- Dr. Kent A.:                         Yeah. Dr. Jennifer H.:                  ... I think- Dr. Kent A.:                         Go ahead. Dr. Jennifer H.:                  ... we're quite happy. We've given over 72 grants and that totals, I think, just over 15 million. But more importantly the grantees have just done a remarkable job I think. The running total I have, which I know is a little bit out of date is 98 publications, and many of those in high impact journals. And certainly in new areas that AHA has not been in the past, which is artificial intelligence, machine learning, and creating new tools. Dr. Kent A.:                         So you're also trying to bridge multiple disciplines then I guess as well with some of these grants, correct? Dr. Jennifer H.:                  Yes. And many of the fields like you're working in as well, thinking about systems biology and proteomics, and bringing the field to beta science along with that as well. So exactly. And in many cases we have private partnerships or strategic partnerships with others like Amazon Web Services. So many of the grants at least, one of the portfolios we have has many grants in it in which the tech credits, or the cloud computing credits if you will, are given to us by our strategic partner Amazon Web Services. So AHA provides the salary support for the PI and others on the grant, as well as any supplies that are needed. And then those tech credits for cloud compute and storage come from Amazon. Dr. Kent A.:                         Excellent. Okay. And then there are other collaborative efforts with other institutions as well, is that correct? Like the Broad Institute? Dr. Jennifer H.:                  Yes, exactly. We have a great relationship with the Broad Institute as well. They've been extremely helpful in helping us get our direct to participant recruitment program off the ground. And we are co-investigators with them on an upcoming NIH data platform grant. So we're extremely excited to be working with them and their team. We've worked with several people, and Doctor Anthony Philippakis' team, and with Noel Burtt and David [inaudible 00:11:24] and others there. So we just couldn't be happier with that relationship. Dr. Kent A.:                         Perfect. Perfect. I know the current round of funding is defined as Uncovering New Patterns, and there are fellowships in grants available for that round of funding. What is the scope of the Uncovering New Patterns effort here? Dr. Jennifer H.:                  There's a lot of both science and tech built into that. And so, one of the ideas from the institute executive committee ... So one question people might ask is, "How do these ... How do you come up with these grants?" And it's really talking people, finding a need, listening to a lot of people no matter where we're traveling. And then bringing that up to our Institute Executive Committee who makes the final call on these grants.                                                 In this case, we were looking for a way to bring science and technology together. So if there was a way to combine datasets that hadn't been combined in the past, which creates some new data harmonization standards, perhaps there's some new methodologies around that. And that's one way to uncover new patterns that we were thinking about. That was the- Dr. Kent A.:                         Right. Dr. Jennifer H.:                  ... original example that came to mind. Dr. Kent A.:                         Okay. Or some new algorithms for- Dr. Jennifer H.:                  Yeah. Dr. Kent A.:                         ... interpreting data or what have you. And it sounds like there's goo dialogue back and forth and between the institute, and those who are sort of interested in the topic as well from the researchers' standpoint both clinically and for basic researchers. Dr. Jennifer H.:                  Yeah. We try. Dr. Kent A.:                         Yeah. Yep. Oh, I think you do more than try. I think it's a wonderful operation. So I see on the website that there's a new round of funding opportunities are soon to be announced. Can you give us any hint as to what the focus will be for the upcoming round or other things that may be in the pipeline in that regard for the upcoming months? Or do you want to keep it a secret? Dr. Jennifer H.:                  I can't disclose that. But there will be some things that are a little bit similar to what we've done in the past. Our focus is to really be on the cutting edge, and we are democratizing data on this Precision Medicine Platform making it open in a controlled access way. So you have to fill out a form. But we're really trying to get to those forms and turn over access to people, qualified researchers and scientists as quickly as possible in a very responsible way. And so we're piloting some new objectives around that. We'd love to bring all cardiovascular and brain health data together. And so people can identify and find new discoveries in this area. Dr. Kent A.:                         Right. Dr. Jennifer H.:                  And use new technique to do it. So the grants will be focused in that particular area. Dr. Kent A.:                         Okay. Well that give me an easy segue then into my next question, which is focusing on some of the resources that are currently available to basic and clinical investigators. And after maybe perhaps describing those, where or how can individuals access these particular resources? So I know definitely the Precision Medicine Platform is one that we'll hopefully hear quite a bit about. Dr. Jennifer H.:                  Yes. That's something that's been underway for about a year now. And we started with two beta testers that you can find the site at precision.heart.org. And we had two beta testers who Gabe [Lucenero 00:15:07] and Laura Stevens, and they were absolutely fantastic and gave us a lot of feedback. We've since hired Laura. Gabe runs his own company up in Canada in the area of bioinformatics. And they've just been fantastic. And since those two, in the last year we've grown to over a 1000 registered users. And once you register on the platform, you can register with your Google account, or however you ... It's a very simple process.                                                 Then you can search across all the datasets. Once you do that, you can access a workspace. And once you request access to the data, the data's put into a workspace for you that is really in the cloud. And allows you to use the power of cloud compute, meaning something that might've taken you weeks on your supercomputer back home in your institution, once you got in the queue, takes a day of a couple of hours using the power of the cloud and the compute power behind it.                                                 Each workspace also has over 85 analytical tools from visualization to statistics, to simple things. And we understand everybody doesn't know how to code. And so we're trying to make that as easy as possible as well. So there's some new things there. We're coming out with new things every week, and looking for ... If people are looking for help, we're taking emails and questions by phone and trying to reach as many people as possible. So we're looking for people to both contribute data and utilize that data to accelerate new discoveries. Dr. Kent A.:                         Perfect. Well, I know, I'm working with Laura actually on trying to implement Cytoscape within the cloud space as well. So it's exciting opportunities. And I'm not a coder myself, but for those that are I know most of the applications that Laura is looking at are based I believe in terms of the language that they're using, is that correct? Dr. Jennifer H.:                  That's right. Dr. Kent A.:                         I think so. Dr. Jennifer H.:                  So most are in R, or Python. Dr. Kent A.:                         Python. Okay. Dr. Jennifer H.:                  And they're on Jupiter Notebooks today. So the nice thing about that is that you can take that Jupiter Notebook, and those are being published in many journals today. So it's really a quick way to analyze the data, get feedback on the data from the community if you're looking for that, that's another piece that we're building in today. And you can have your collaborators work on that same workspace with you. And then just turn that Jupiter Notebook and publish it with all your methodology and code on it. Dr. Kent A.:                         Oh, okay. Excellent. Dr. Jennifer H.:                  And we're also establishing drag and drop code, so if you are just learning, you can take kind of from the recipe book and drag and drop, and practice and learn. And soon we should be hopefully working on some educational tools with our strategic primary, Amazon. So we're really excited about that coming out in the near future. Dr. Kent A.:                         Excellent. And we're doing our best at the council level to try to help spread the word as well with respect to the platform, and that was part of our offerings as I said, at Scientific Sessions back in November. Laura did a wonderful job presenting the platform and introducing it to a wide variety of backgrounds. I think in a way that is really ... It's really helpful bringing these people together with different backgrounds, with different abilities. People are ... Regardless of your background, people are coming up with wonderful ideas and ways to implement these applications. Are you taking say, feedback in terms of how people are using these tools and how they can utilize different applications in terms of making adjustments and modifications as time proceeds? Dr. Jennifer H.:                  Yes. We're really excited about that, because we'd like to keep all of that data like any scientist, and publish that. So not only ours, it's better, but others doing similar things also will improve. And this platform is meant to work with other groups and to interact with other platforms as well and be part of the NIH data commons as well. And so we're really working to interact a use standard language and standard systems to interact globally as well as within and U.S. So we're trying to make it simple for all scientists. But gather as much data as we can. And a big shout out and thanks to you, and Functional Genomics Council for allowing us to be part of that workshop at Scientific Sessions. Dr. Kent A.:                         Oh, we really appreciate the opportunity to be involved with it as well. I think one of the ... Another strength that as you mentioned is that collaborators can access the same workspace from different institutions so that they can be working together and collaborating without having to download an entire dataset in one space, and have someone working on it and modifying things, and then finding out that your collaborator at the other institute has modified something else. And then trying to- Dr. Jennifer H.:                  Right. Dr. Kent A.:                         ... sync those version together can be quite troublesome at times. Dr. Jennifer H.:                  And you weren't sure you had the right version, and datasets are so large now that- Dr. Kent A.:                         Yeah. Dr. Jennifer H.:                  ... really people think about it in terms of the researchers going to the data, instead of the data going to the researchers. Dr. Kent A.:                         Yeah. Excellent. Dr. Jennifer H.:                  [inaudible 00:20:55] the new ways. Dr. Kent A.:                         That's perfect. Well, I'll maybe just reiterate that website. It's precision.heart.org. For the Precision Medicine Platform. And while I'm mentioning websites, maybe I'll mention institute.heart.org, which is the homepage for the institute for Precision Cardiovascular Medicine, I believe. Is that correct? Dr. Jennifer H.:                  Thank you for mentioning that. Dr. Kent A.:                         Yeah, 'cause if I'm wrong, please correct it. Dr. Jennifer H.:                  Well, you see, I'm a scientist, not a marketer because I never mentioned that at the beginning. But thank you for doing that. And we'd love any feedback that anybody has of other things they'd really like to see there, or how we can be more helpful to the community. Dr. Kent A.:                         Definitely. And how would people go about contacting you in that regard or in terms of setting up a workspace, or accessing a workspace? Would they go through the institute homepage? Dr. Jennifer H.:                  In terms of setting up a workspace, you can do that directly from precision.heart.org. Dr. Kent A.:                         Okay. Perfect. Dr. Jennifer H.:                  And once you request access to data, if you check on a box and hit, "Request access," it starts you down that road. It doesn't mean you have to commit. But it will start you down that road. Dr. Kent A.:                         Yeah, it will set it up. Okay. Perfect. Dr. Jennifer H.:                  It will begin to set up the process for you. It takes ... You can put in there your grant, it takes tokens, it takes all sorts of things, because there's a small cost that is a pass through really from ... It's like if you were just to go directly to Amazon Web Services to create that workspace. Dr. Kent A.:                         All right. So it's a small cost incurred for ... Okay. Excellent. Dr. Jennifer H.:                  Yeah. For what you're doing. Dr. Kent A.:                         Okay. Dr. Jennifer H.:                  And so everybody should be aware of that. Dr. Kent A.:                         Sure. Dr. Jennifer H.:                  But the institute is giving the grants for that. NIH gives grants and tokens for researchers to allow to use that as well. And like I said, the cost is a couple dollars an hour really. So it is not something that's gonna break the bank. And the AHA has built some stopgaps to try to keep those costs as low as we can. Dr. Kent A.:                         Perfect. Well, that's much more reasonable than some of the bioinformatic applications I work with these days. So that's good to know. Dr. Jennifer H.:                  Well, just the license fees alone can be challenging [crosstalk 00:23:06]. Dr. Kent A.:                         Exactly. Well, even institutional licenses, you still- Dr. Jennifer H.:                  Yeah. Dr. Kent A.:                         ... you're paying a portion of, and it can add up quickly when you're on there frequently. So- Dr. Jennifer H.:                  Yeah. Dr. Kent A.:                         ... a couple dollars an hour is quite reasonable. Dr. Jennifer H.:                  Yeah. Dr. Kent A.:                         Well, I don't wanna take up too much of your time. I know you probably have another meeting scheduled right after this. So I wanna thank you for taking the time today to talk with me and discuss the AHA's Institute for Precision Cardiovascular Medicine. I seem to have trouble pronouncing, "Precision," today for some reason. And for sort of introducing the Precision Medicine Platform for our listeners. So thanks again for bringing these important advances to the American Heart Association membership, and for sort of introducing them to us in today's podcast. Dr. Jennifer H.:                  Oh, thank you, Kent. And thank you for all you do for functional genomics and the American Heart Association. Dr. Kent A.:                         Thanks Doctor Hall. Much appreciated. Dr. Jennifer H.:                  Take care. Dr. Kent A.:                         Take care. Jane Ferguson:                 Thanks again to Doctor Hall for joining us, and to Doctor Arell and the FGTB Early Career Committee for supporting this podcast. And as we bring 2017 to a close, I want to thank all of you out there for subscribing and listening. We're excited to come back in 2018 with even more content and would love any feedback or suggestions of topics you'd like us to cover. You can leave a comment or review through iTunes or other podcast aggregator, or contact me directly at jane.f.furgeson@vanderbilt.edu. Whishing safe and happy holidays for anyone who celebrates. And we'll talk to you again in 2018.

Jane Ferguson:                Hello, I'm Jane Ferguson and you are listening to Getting Personal: Omics of the Heart, the podcast from Circulation: Cardiovascular genetics, and the functional genomics and translational biology council of the AHA. This is episode ten, from November 2017.                                            November is always a big month for AHA and the annual Scientific Sessions were held in Anaheim, California, November 11th through 15th. For those of you who were able to attend, hopefully you came away feeling refreshed and invigorated and with your desired level of Disney merchandise. For those of you who could not attend, or who didn't make it to all of the genomic sessions, this month's episode should catch you up.                                            For the past several years, the FGTB Council has been organizing boot camps at AHA sessions to give people a chance for hands on learning in a flipped classroom model. This year was no exception and in addition to a clinical genomics boot camp focused on patient centric genomics including single gene testing, whole genome sequencing and pharmacogenomics there was also a new boot camp focused on tackling big data network systems analysis for high input data interpretation.                                            These boot camps are always very well attended and popular, so if you're interested in attending one next year, make sure to get in early and sign up during registration. There was also a hands on session in collaboration with the AHA's Precision Medicine Institute to teach people how to use the precision medicine platform to further their research.                                            In addition to this, there was a full day of programming related to precision medicine in the precision medicine summit, which is held on the Tuesday of Sessions. That covered topics ranging from big data, electronic health records, collaborations and the All of Us initiative to rapid fire reports from ongoing consortium, large scale analysis to disease specific approaches in cardiomyopathy.                                            We were planning to have an in depth focus on the Institute for Precision Cardiovascular Medicine in a future podcast episode, so stay tuned for more on that coming soon. There were a number of individuals who were recognized for their contributions to science and we would like to congratulate all of these outstanding individuals.                                            The FGTB medal of honor was awarded to Stuart Cook from the Duke National University of Singapore. The FGTB mentoring award was awarded to Robert Gerszten from Beth Israel Deaconess Medical Center. The FGTB distinguished achievement award went to Sekar Kathiresan from the Broad Institute. And the functional genomics and epidemiology mid-career research award went to Kiran Musunuru from the University of Pennsylvania. Congratulations to all of these.                                             One of the highlights for the FGTB council at sessions is the FGTB young investigator award. This award celebrates early career investigators and recognizes outstanding research in basic science, populations science, genetic epidemiology, clinical genetics and translational biology. Four finalists presented their research on the Sunday afternoon sessions and I had the chance to chat with all four of them before and after their presentations. So listen on for a behind the scenes over view of the finalists research and the announcement of the winner.                                            Mark Benson is a cardiology fellow at Brigham and Women's Hospital and is working on post-doctoral research at the Beth Israel Deaconess Medical Center in Boston with Dr. Robert Gerszten. His talk was entitled "The Genetic Architecture of the Cardiovascular Risk Proteum." Mark Benson:                  My name's Mark Benson. I'm just finishing up a cardiology fellowship at Brigham and Women's Hospital and am in the middle of post doc in Robert Gerszten’s lab at Beth Israel. Jane Ferguson:                Great, and congratulations on being chosen as a finalist for the FGTB Young Investigator Award. We would love to hear a little bit more about what you’re working on and what you're gonna be telling us. Mark Benson:                  Yeah, absolutely. So the goal of the project was really to integrate proteomic data with genomic data, with the idea that we may be able to use the overlap between those data sets to identify potentially novel biological pathways that underlie very early cardiovascular disease risk.                                            And the thinking behind that was that the lab had just finished up applying DNA-aptamer-based proteomic platform to profile over 110 proteins and the Framingham-Offspring Cohort and from that work, we had identified a very specific signature of 156 proteins in plasma that were each very strongly associated with cardio-metabolic risk.                                            The idea was while those associations were very strong, it was unclear if we were capturing cart or horse or how these associations were fitting together. We wanted to incorporate the genomic data to try to get a better handle on that, to try to connect those pathways to see how these proteins might actually associate with the end phenotype of risk. Jane Ferguson:                It's a sort of Mendelian randomization-esque. Mark Benson:                  Exactly, yeah. So what we were able to find in doing this, we were able to use peripheral blood samples from participants at the Framingham-Offspring study. With a validation in participants of the Swedish Malmo Cancer and Diet Study. Then we did protein profiling using commercial DNA aptamer platform, soma scan. What we were able to find is we were able to detect very strong associations between these circulating cardio metabolic risk-proteins and genetic variance.                                            What was fascinating was we were able to see many things. We were able to start mapping where are these associations, where are these genetic variance in relation to, for example, the gene that's coding the protein that we're measuring. That had some interesting implications because for about half of the protein that had significant associations, we could track those genetic variance back to the gene. It was coding the protein that we were measuring, which was interesting because it's validating the specificity of the proteomic platform that we're using. Jane Ferguson:                Right that's nice, because so often you found a gene that's nothing related to what you think it's going to be so it's nice actually the gene you expect. Mark Benson:                  Yeah, it's very reassuring too when you're looking at rows and rows and rows of data. When the top association of the p value of 10 in the minus 300 is the actual gene you thought would be coding the protein that you're measuring. So that was very reassuring, but we also found dozens and dozens and dozens of associations that were totally unexpected and that may point to completely unexplored biological pathways in cardiovascular disease. So that was obviously very exciting.                                            That actually led us to do two things. One was to make all these data available publicly on dbGaP because as a resource for cardiovascular research there is just way too much data for one group or a handful of groups to digest. The other thing that was fun about the project, is we were able to take one association that was particularly interesting for a number of reasons and experimentally validate it in a tissue-culture model. Jane Ferguson:                So how did that work? Mark Benson:                  So this was an interesting challenge where we all of a sudden got all of these hits back, which was probably to be expected, but to try to figure out which of these dozens and dozens and dozens of new, unexpected hits, what do you do? There was one hit, one association, that was particularly strong and it was between several variance around this gene. That's a phosphatase called PPM1G. It's a transcription factor.                                            These variants, which was interesting, were associated with several different circulating cardio metabolic risk proteins. So our idea was, isn't that interesting? Is it possible that this is mapping to some central regulator? And so it fit that that would be ... that the nearest gene to these variants was a transcription factor and could be a central regulator.                                            What made it more interesting to us was that several variants in the GLGC had recently been described that were highly associated with circulating levels of total cholesterol and triglycerides and they were located around this PPM1G locus as well. The association between those variants and circulating cholesterol didn't have a clear biological connection.                                            So what our work had shown is that those same variants were associated with circulating levels of apolipoprotein E. So wouldn't that be interesting if these variants mapped to PPM1G, the transcription factor, this PBM1G in turn regulated circulating apolipoprotein E and that would provide some insight into the biology behind the GLGC findings.                                            So sure enough we were able to knock down PPM1G using SRNA and hepatocytes and then see that that led to a significant down regulation of the transcription of Apo-B and extra-cellular presumably secreted Apo-B in this model, which is kind of a nice proof of principal that this idea of integrating proteomics and genomics may lead to some novel biological pathways. Jane Ferguson:                Yeah, it's really interesting. So what's next. There are probably a lot more associations that you're going to have to go after? Mark Benson:                  Yeah, I think that what this showed us is that this seems like a powerful tool. Joining these orthogonal data sets to find new pathways and so we're continuing to pursue that with an increasing number of proteins for example, so we're doing genome-wide association studies and x-gamma rays. We've gone from 156 to 1100 to 1300 and are now going beyond that and so as those numbers get higher, you start to see these central nodes come together and more interesting targets and potential pathways. It's also interesting to use these data to find new associations or new tools that you would never think to look for as ways to modulate protein levels.                                            So you can imagine, for example, one thing that we've been exploring for the last few months is can we identify, for example, SNP associated with an interesting circulating protein. That SNP maps to an enzyme or some other druggable mechanism and very preliminary studies, it seems like the answer is probably yes, but there is still a lot of work to be done. Jane Ferguson:                Well that's cool. That sounds really interesting. Mark Benson:                  Yeah, I think the key thing is that all these data will soon be out there and so it's a very rich data set and I think there are many ways that we could use the data. Jane Ferguson:                So is that the genomic data and all the proteomic data or it's the summary of the those associations? Mark Benson:                  All the genomic data, all the proteomic data and the associations as well. You can do the associations yourself if you'd like to. Jane Ferguson:                We can find that  dbGaP. Awesome, well thank you for talking to us. Mark Benson:                  Thank you. It's been fantastic. Jane Ferguson:                Congratulations again. Mark Benson:                  Thanks so much. ... Jane Ferguson:                Jenny Lin is an instructor at the University of Pennsylvania, working with Dr. Kiran Musunuru. Her presentation was entitled, "RNA binding protein A1CF Modulates Plasma Triglyceride Levels through Transcriptomic Regulation of Stress-Induced BLDL Secretion".                                            Jenny, can you take a moment to introduce yourself? Jenny Lin:                          Yes, hi. Thank you for this opportunity to participate. I'm Jenny Lin. I'm an instructor of medicine at the University of Pennsylvania, a nephrologist by clinical training, but training in cardiovascular research in Kiran Musunuru's lab. Jane Ferguson:                So congratulations for getting selected as a finalist for the Young Investigator Award. We'd love to hear a little bit more about what you've been presenting and what you've been working on. Jenny Lin:                          Thank you. So basically, what I've been working on over the past year is functional follow-up of this A1CF locus, which is a novel locus for triglycerides. So say Sek Kathiresan's group recently published in Nature Genetics and x and y association study on plasma lipids involving more than 300,000 individuals.                                            One of the key findings from that study is this strong association between a lo-frequency coding variant and elevated plasma triglycerides. So we wanted to delve more deeply into the biology for why we have that genotype/phenotype connection. One of the key things that we wanted to do was ... A1CF is not a stranger to lipo-protein metabolism, but we wanted to see what else it may be doing outside of its canonical role of facilitating the editing of Apo-B messenger RNA.                                            It really took us on a little bit of a wild journey using different unbiased approaches to try to figure out some of the mechanisms that could be behind it. Jane Ferguson:                So you had to do a lot of different types of experiments to really get at this question. Jenny Lin:                          Yeah. So again, one thing we wanted to see was: if you lose A1CF function, whether or not you would have differences in Apo-B 100-B48. We actually found that A1CF isn't even needed for that editing reaction and that our mice that we were able to create with crispr cas9 genome editing, so knocking in the mutation and knocking out the gene, actually have the phenotype even though they don't have changes in editing.                                            But what surprised us was that we know that A1CF as an RNA binding protein binds Apo-B transcript, yet it somehow does not alter transcriptional abundance of the Apo-B messenger RNA. And it has nothing to do with Apo-B synthesis so we basically had to think, what is A1CF doing outside of Apo-B biology?                                            We found that you have A1CF loss of function, you have increased triglycerides secretion. There is more Apo-B secretion, but that seems to be a downstream effect of other processes going on in the cell and to really try to figure out what those processes are, we had to take an unbiased approach using enhanced clipseek to figure out binding targets and also doing some transcriptional profiling with RNA sequencing and found that it's not necessarily regulating that transcriptum on a differential expression level, but there are some key alternative splicing events as well as messenger RNA binding to affect translational efficiency of some key targets that could be driving the biology. Jane Ferguson:                That's really interesting and you wouldn't have been able to find that by just looking at levels of protein or levels of mRNA, you really had to do these additional clipseek and some experiments to really get at this splicing. Jenny Lin:                          Yeah, so it's been interesting. Clipseek is not as commonly performed method, so we had to collaborate with some brilliant people over at UCSD, to help us facilitate this. But again, finding that A1CF binds many more transcripts than Apo-B itself is a novel finding and the fact that it can regulate alternative splicing is also a very novel finding as well. Jane Ferguson:                So what was the most challenging part of this whole project? Jenny Lin:                          I think the challenging part was that when we saw there wasn't necessarily a direct effect on Apo-B abundance and having to then cast this wide net and then figure out from all of the different unbiased data we have and integrating it find different pathways that may be relevant. In this case, it may all be relevant to ER stress, which is a field that is a little bit controversial in VLDL secretion in terms of directionality, but certainly is important in the biology. Jane Ferguson:                So is that something that you're going to have to start doing in the future? Are you going to start looking at ER stress or what kind of other experiments do you think you're going to keep doing to move this project forward? Jenny Lin:                          Yeah, so actually, I think focusing in on A1CF as an RNA-binding protein and pursuing some of these additional targets will also be relevant, so I think in terms of ER stress, we could be looking at different targets, but there other processes going on in the cell that's mediated by A1CF, that could contribute maybe doing some isoform specific studies just to really prove that these alternative-splicing changes are driving some of the biology.                                            There's a lot of work to do as I would joke to anyone on study section listening to this, perhaps four to five years of work for an RO1. Jane Ferguson:                Sounds very appropriate. Jenny Lin:                          Yeah, there's a lot of exciting work to do. A1CF is actually also a locus for other cardio-metabolic relevant traits such as uric acid, gout and kidney function so there could be something very interesting going on. There could be cross talk among cellular processes that could lead to these different phenotypes. Jane Ferguson:                Really interesting project and a lot of really great work. Congratulations again on being selected as finalist and on this really interesting paper. Jenny Lin:                          Thank you. Jane Ferguson:                Thanks.                                            Sarah Parker is based in Cedar Sinai Medical Center in LA and her mentor is Dr. Jenny Van Eyk. The title of her presentation was "Identification of Putative Fibrous Plaque Marker Proteins by Unsupervised Deconvolution of Heterogeneous Vascular Proteomes ". And I apologize in advance for the quality of this recording. The background noise wasn't that noticeable at the time, but that recording really gives you that full immersive audio experience of a busy hotel lobby.                                            Hey Sarah. Thank you for joining us. Could you just take a few moments to introduce yourself to the audience? Sarah Parker:                   So I'm Sarah Parker. I'm a project scientist at Cedar Sinai Medical Center where I'm doing work to study the basic mechanisms of vascular biology of various indolent conditions. Jane Ferguson:                So congratulations on being selected as a finalist for the Young Investigator Award. It's a great achievement. I'd love to hear a bit more about your project, how that started and what you found. Sarah Parker:                   The work that I did was under the overarching umbrella of a project called the Genomic and Proteomic Architecture of Atherosclerosis. So with this project, we're using tissues that we're able to obtain from individuals who are young and have passed away from traumatic and violent and so non-cardiovascular causes of death. Because of the presence of atherosclerosis in the population, we get this range of lesion, both fatty streak and fibrous-plaque lesions in these asymptomatic or non-diseased individuals and this gives us this opportunity to do some molecular profiling to really try to find protein-signatures of early stage plaque formation, that could ultimately and hopefully be used for biomarker development. Jane Ferguson:                That's really cool and that's such a valuable sample resource. Sarah Parker:                   Yeah so we've essentially, in this project I was able to set up a pipeline that enabled us to do these proteomics on such a large scale, because that's actually really difficult in label free quantitative proteomics and to use other forms becomes very expensive and cost-limiting.                                            So we were able to find a panel of proteins that we think are a putative early set of fibrous plaque markers and with this panel, we took them to see if any of these tissue derived markers would then be detectable and informative in plasma, because that's the next really big translational leap with these discovery-type data sets. Of our 58 initial candidates, we were able to detect 39 of them and about a handful 10-13 are showing informative behavior in the plasma of initial cohort of women with known coronary-artery disease. Jane Ferguson:                So out of the 58 that you first found, how many of them were potentially known to be involved in disease and how many were novel? Sarah Parker:                   I would say, going through the list, it was probably about 50/50 in terms of background data that shows role as a biomarker, so there are a lot of apolipoproteins, which have all been characterized as potential biomarkers. There were a lot that could feasibly be linked through the literature to atherosclerosis. Most of them made a lot of sense, but having been proposed as potential biomarkers, some of them were more rare. Jane Ferguson:                Were there any of them that were sort of in different directions, let's say were elevated in tissue, but then were lower in plasma? Sarah Parker:                   Funny you should ask. That actually has us scratching our heads a little bit right now. There were a couple of apolipoproteins that are more associated with HDL biology that we saw as being elevated in the tissue but then lower in the plasma [inaudible 00:23:34] so that's a really interesting observation so something about the role of these proteins to scavenge cholesterol and then once they're in the blood, they're cleared really quickly relative to normal, or something. So we're really trying to figure out what that biology means. Jane Ferguson:                Maybe if they're building up in the tissue, that's bad. But while in circulation, they're fine. Sarah Parker:                   Yeah, maybe they're trapped in the circulation. We have a lot of exciting hypotheses to test along that front. Jane Ferguson:                So what's next? Are you following up some of these proteins? Sarah Parker:                   Yep, so we have a huge discovery arm to the project where we're looking for more molecular mechanisms like why do we have these things in the tissue versus plasma and then we are working to really validate and optimize these multi-plexes in much more generalized large-scale populations to determine whether this strategy of instead of one or two biomarkers, more of a signature-style panel can be informative, especially as we try to press towards a precision medicine approach where different substratum might be informed by different protein signatures. Jane Ferguson:                Right, so you might have to have a specific panel based on sex or age or race or some other demographic. Sarah Parker:                   Yes and to find those signatures, it's going to be very big numbers, with very accurate, careful quantitation. Jane Ferguson:                So you have a lot of work to do. Sarah Parker:                   Yes. Jane Ferguson:                Alright, well thank you for talking to us and congratulations again.                                            Louie Wang, a cardiologist and PhD student came all the way from the Victor Chang Cardiac Research Institute in Syndey, Australia. His mentor is Dr. Diane Fatkin. The title of his talk is "A novel zebrafish model of human A-band truncated titan exhibits alternated ventricular diastolic compliance in vivo and reveals enhanced susceptibility to the effects of volume overload in mutation carriers.                                            So thank you for joining me. Could you take a few minutes to introduce yourself? Louie Wang:                     So I'm Louie Wang. I'm a cardiologist based in Australia. I work and live in Sydney. I'm a PhD student at the Victor Chang Cardiac Research Institute and I'm an NHMRC (National Health and Medical Research Council and National Heart Foundation of Australia post-graduate scholar). I have previously been based at St. Vincent's Hospital. Jane Ferguson:                Great. So we'd love to hear a little bit in advance of what you're working on and what you're planning to present. Louie Wang:                     So basically what I'm presenting is what I think is a different form of functional of genomics. What we're actually looking at is the impact of genetic changes, specific genetic change on function of the heart at an organ level. So there is a problem out there that is very common in cardiology and it's a big problem in cardiology and that is there are mutations in the sarcomere protein titan, truncating variants which actually are associated with dilated cardiomyopathy.                                              Now they're pretty common in idiopathic dilated cardiomyopathy, present in about 15-20% of the cases depending on which cohort study you look at. But they're also widely prevalent in the general population. Somewhere between 0.3 to 1% of the general population carries this truncating variants or various forms of this truncating variant.                                            So it's not sure whether these are disease-causing in their own right or if it's just a genetic susceptibility factor for heart failure and so what our work involves is that we actually, by chance, at St. Vincent's Hospital and at Victor Chang Cardiac Institute, two families who had the identical genetic truncation in the A-band region of his human titan gene where the individuals in the family, typically who carried the gene, typically developed systolic heart failure, which is a mild phenotype and occurred at middle age, but in two individuals, they developed severe onset accelerated disease trajectory in a very severe phenotype when exposed to conditions associated with chronic volume overload.                                            We suspect and this was a hypothesis, not only was this genetic-truncation disease-causing, but at volume overload was disease-modifying and given that volume overload is a very common condition present in birth, a lot physiological processes like lung endurance, exercise, pregnancy as well as a lot of pathological disease states in cardiovascular disease, this was actually a very important modifiable factor.                                            So what we did, was we created a novel zebrafish model of this human A-band truncated variant. We then studied the animals when they became adults to look at their heart structure and function and we used zebrafish echocardiography. So reversed translated all the techniques you can do in human echocardiography so they can be used in the zebrafish.                                            What we found was, yes, this animal, or heterozygotes developed dilated cardiomyopathy but also the volume overload exacerbated this condition. So this is a phenomenon that has conserved this by four hundred million years of vertebrate evolution so this is a pretty important mechanism. Jane Ferguson:                So what kind of next steps do you see for this project? Louie Wang:                     So one thing is that we obviously have shown that there is an association with volume overload in precipitous disease. The corollary of our work is that perhaps interventions that could reduce volume load in these genetic susceptible individuals or alternatively in people who can't avoid volume overload. Because a lot of volume overload conditions can be modifiable and perhaps this could be protective and that would have wide-ranging population benefits. Jane Ferguson:                Thank you for sharing that soundbite of your work and good luck. Congratulations again on becoming a finalist. Louie Wang:                     Thank you. ... Jane Ferguson:                Each of these four finalists gave compelling presentations of their research and the judges were highly impressed of the quality of the research and level of accomplishments of these early career investigators.                                            Just getting selected as a finalist for this award is a huge accomplishment. But there did have to be one winner. I'm delighted to announce that Jenny Lin was selected as the 2017 FGTB Young investor award winner. Congratulations, Jenny, and thanks to all four finalists for agreeing to appear on this podcast.                                            And that's all for this month. We'll be back at the end of December with a new episode. Subscribe to the podcast through iTunes or your favorite podcast app. to get new episodes delivered automatically and thank you for listening.

Jane Ferguson:                  Hi Everyone. Welcome to Getting Personal: Omics of the Heart, your podcast from Circulation Cardiovascular Genetics. I'm Jane Ferguson, an assistant professor at Vanderbilt University Medical Center and an associate editor at Circ Genetics. This is Episode 9 of the podcast from October 2017.                                                 This month we were on the road and traveled to sunny Orlando, Florida for the annual Scientific Sessions of the American Society of Human Genetics. While there, I had the chance to talk to some of the researchers presenting posters in the sessions on cardiovascular genetics and genomics, which you'll hear in just a moment. While at ASHG, we had the chance to organize a CRISPR-Cas9 genome editing boot camp. Those of you who attend a JR ATVB/PVD Scientific Sessions might have had the chance to participate in a boot camp in previous years, and this is the first time we were able to offer a boot camp at ASHG. These boot camps are based on a flipped classroom model in which the participants do some preparatory learning in advance of the meeting, and then have the chance to do hands on activities with immediate guidance from the onsite instructors. It's a really nice way to learn more about a topic, so if you're attending AHA meetings in the future, look out for the option to sign up for a boot camp while you're registering.                                                 If you haven't been able to attend a boot camp but are interested in CRISPR-Cas9 genome editing, you can access video and slide materials on the Circ Gen website at http://bit.ly/CRISPRbootcamp and the CRISPR is capitalized, so capital C-R-I-S-P-R boot camp.                                                 Moving on to the virtual poster session from ASHG, you may notice a little more background noise than usual, which will hopefully make you feel like you were right there with us at the poster session.                                                 First up, Dr. Gemma Cadby is a research fellow at the University of Western Australia and she presented a poster with data from her ongoing research into heritability of lipid species, measured through lipidomic analyses and their relationship with cardio metabolic risk traits, including blood pressure and HDL/LDL and total cholesterol.                                                 I'm here with Gemma Cadby, whose poster is entitled "Genetic Correlation of Human Lipidomic Endophenotypes and Cardio metabolic Phenotypes in the Busselton Family Heart Study". Hi Gemma, can you tell us a little about your poster? Dr. Cadby:                           Sure. So what we've done is we've taken about four and a half thousand people from an epidemiological study called the Busselton Health Study, so that's a group of people from Busselton in western Australia who were recruited initially in 1966 and they've been followed up every couple of years, and their blood was taken in 1994 and 1995. So the great thing about the Busselton Health Study is that there are a lot of related individuals, so it wasn't recruited as a family study but because it's a small town, a lot of people are related. So we didn't want to exclude those people from our analysis. Jane Ferguson:                  Right. Dr. Cadby:                           And because we don't really trust family records, because the study wasn't recruited as a family study, what we've done is we have empirically derived their relationship using the LDAK software. Jane Ferguson:                  Okay. Dr. Cadby:                           And then what we've done is we have performed targeted lipidomic profiling to quantify 530 lipid species and those are from 33 lipid classes. Jane Ferguson:                  And that's all from plasma samples? Dr. Cadby:                           Yes. And then what we did is we estimated the heritabilities. At this stage we've just done the heritabilities of the total of the sort of, of the 33 lipid classes, so those 530 species break down ... sort of can be combined into 33 classes. So we estimated the heritability of those, and then we also looked at the genetic correlation between those lipid classes and some cardio metabolic phenotypes. So, we found that 98% of our lipid species was significantly heritable, so those of the individual 530 species, and those heritabilities ranged from .12 to .52 and all of our lipid classes were also significantly heritable, with heritabilities between .15 and .5. Jane Ferguson:                  How does the LDAK software work? Do you put in genotypes, like were these subjects all genotypes- Dr. Cadby:                           So, they were genotypes on the Illumina ... Was actually on two different chips, the 610 and the 660, but we checked them in a batch of facts, and we combined them into one sample- Jane Ferguson:                  Mm-hmm (affirmative)- Dr. Cadby:                           Yep, and then LDAK adjusts for linkage between the variants, and then we used that to estimate their relatedness. And what we also did is we removed any relationships that were ... We said any relationship less than .05 to 0 so that ... With the idea being that the snips on the chip should estimate the whole genome relationships, but anything less than .05 might just be due to sort of, chance. Jane Ferguson:                  Right, right. Okay. Dr. Cadby:                           So we ran the genetic correlation between nine cardio metabolic phenotypes and the 33 lipid classes, and we found 155 of these genetic correlations who were statistically significant. Probably not surprisingly, so dystonic blood pressure wasn't genetically correlated with any lipid class, but we did find that systolic blood pressure was genetically correlated with eight of our lipid classes. Jane Ferguson:                  Did you notice any difference between the highly-heritable lipids and the non ... like the less-heritable lipids and their association with phenotypes? Dr. Cadby:                           Surprisingly, the most heritable lipid class was [asoplanetine 00:06:09] and that wasn't genetically correlated with any of our cardio metabolic phenotypes, which was quite surprising to me. Jane Ferguson:                  Right. So your next steps would be data- Dr. Cadby:                           So, what I've actually done, but is not showing on this poster, is I've now run the genetic correlation between each of the 530 lipid species and their cardio metabolic phenotypes to see whether the genetic correlations we observed were just due to, sort of a subset of lipids within that class or whether it was across all of the lipids species in that class.                                                 And we've also, I guess the exciting part, we've also got 500 whole genome sequences that we've just performing QC on at the moment. So then what we want to do is we want to see if we can use our lipid species to try to identify any genetic variance that are coarsely associated with the lipid endophenotype and then ... which would then go on to be associated with cardiovascular disease outcomes. Jane Ferguson:                  Cool. Very interesting. Have you published this or are you working on a manuscript now? Dr. Cadby:                           No I just working on it at the moment. We only got the lipidomic data in maybe June. So it's been sort of a quick ... just trying to get it done at the moment. Jane Ferguson:                  Thank you.                                                 Next I talk to Doctor Sylwia Figarska post doctorate fellow at Stanford University. She presented her research on proteomic profiling in several Swedish cohorts using the Olink platform and looking at the association of cardiovascular risk proteins with triglycerides, HDL, LDL, and total cholesterol.                                                 So I'm here with Sylwia Figarska from Stanford who has a poster entitled, "Associations of Circulating Protein Levels with Lipid Fractions in the General Population".                                                 Hi Sylwia. Thank you for agreeing to this. I would love to hear a little bit more about your poster. Dr. Figarska:                       Yeah, so I work with a Dr. Erik Ingelsson at Stanford and we were interested in pathways of association between circulating proteins and lipid levels to better understand [inaudible 00:08:30] of cardiovascular disease. Because both protein biomarkers and lipids are associated with cardiovascular disease, which is main cause of death world-wide. And so, in this study we investigated association between protein biomarkers and triglycerides, cholesterol, LDL, and HDL levels in population based cohorts.                                                 So as we study population, I used a Swedish cohort, [Epi Health - inaudible 00:09:04] cohort. So the cohort size is a little bit more than two thousand individuals. Associations at the p value collected for [FDR - inaudible 00:09:19] lower than five percent. We tested in a validation step, which was [inaudible 00:09:29] cohort also, turning a population based Swedish based cohort, and associations at P value lower than 0.05. We considered my results. Jane Ferguson:                  Okay, so what kind of things did you find? Dr. Figarska:                       Yeah, so we tested 57 proteins and 42 of them were successfully replicated for association with at least one lipid fraction. So, we found 55 blood associated with triglycerides. 15 proteins associated with cholesterol. 9 protein associated with LDL cholesterol and 24 with HDL. And then we were interested in overlap between protein biomarkers and lipid fractions. And indeed we have found some proteins that were associated with all of the lipid fractions or with, for instance, HDL cholesterol and triglycerides, because this is ... and we also looked at the parting of these associations, so this is a hit mark showing them directions of association. It's because some older proteins will associate with increase of triglycerides and at the same time will lower HDL, which is kind of expected pattern ... Jane Ferguson:                  Right. Dr. Figarska:                       ... and also this increase triglycerides and decrease HDL level is a phenotype that also is associated with insulin resistance, another phenotype I'm interested in. Yeah, so far good finds. Some interesting associations and further studies are needed to have a closer look at these patterns. Jane Ferguson:                  Right. So do you think ... are you going to follow it up with more functional analyses with these proteins to see sort of what are the functional relationships between these proteins and the lipid traits? Dr. Figarska:                       Yeah, yeah. We also might look at genetic background of these to see, which part of genetics will determine both of these proteins and lipid levels. Jane Ferguson:                  Right, interesting. So have you published any of this yet or are you working on a manuscript? Dr. Figarska:                       No, I'm working on a manuscript right now. So it's not published yet. It's new data, the results. Jane Ferguson:                  Yeah, very interesting. And I guess ... so tell me a little more about this Olink platform? So is this ... was this selected specifically for proteins that are known to be involved in cardiovascular disease? Dr. Figarska:                       Yeah, yeah. So, Olink is a Swedish company that offers partners to test protein levels and it's highly sensitive and specific assay. So for each panel you might test 92 proteins and using one microliter of blood sample, which is [inaudible 00:12:48]. Efficient and as I said it's very specific and sensitive method. And Olink effects panels of 92 proteins and for this study we used cardiovascular panel one and cardiovascular panel two and three. So, it means proteins that were like expected to be related to cardiovascular disease. And because significant panels were used in different cohorts, for this study we used those that were overlapping between these three panels to ... because then we could check them. We could check the ... validate the result. Jane Ferguson:                  Thank you.                                                 Dr. Marketa Sjogren is an associate researcher at Lund University in Sweden and spoke to me about her project investigating genetic risk scores for coronary artery disease could predict overall hospitalization burden and mortality in over 23,000 individuals from the Malmo Diet and Cancer Study.                                                 So I'm here with Marketa Sjogren from Lund University and her poster is entitled, "Elevated Genetic Risk for Coronary Artery Disease Increases Hospitalization Burden and Mortality".                                                 So, Marketa, I'd love to hear a little bit more about your research. Dr. Sjogren:                        So what we have done here is to take about 28,000 or 23,000 individuals from a study called Malmo Diet and Cancer Study, which has been previously published  [inaudible 00:14:33]. And we constructed a way to genetic risk score consisting of 50 snips for coronary artery disease as a risk. Jane Ferguson:                  And was this from previously published studies? Dr. Sjogren:                        Yes, those are from previously ... those are GWAS identified and previously published for different stuff. So these are basically at that time up to date, I think. There are some more to be included now, but at that time this was up to date.                                                 What we have done is to look whether we can, in a population based study, that is prospective study, whether we can predict if this genetic risk score, increased genetic risk score, could predict hospitalization and/or mortality. And what we see is that, that actually higher genetic risk score. So if you are in the top quintile of a genetic score, your risk of every being hospitalized for any reason increases by about 10% ... actually about 30% when it comes to cardiovascular causes. At the same time we also can see that increased genetic risk actually increases your risk to die both of any causes and particularly of cardiovascular mortality. And the strength of our study, I think, is that we actually have electronic health records, which include 100% of the population. So that we are actually sure that these people were increased and we also have the good sort of diagnosis for those, because those are hospital diagnosis. Jane Ferguson:                  Right, right. Interesting. So, even ... so for people who were hospitalized for CAD but did not have a high genetic risk score, where you able to sort of tease that out? So people who had CAD but didn't have ... had a low genetic risk but got CAD anyway? Dr. Sjogren:                        Yeah. No, we haven't actually quite look at that, but that's an interesting question because that would of course be interesting to see what else to they have and what are the environment factors that would influence the low genetic risk. Because, of course there are people with low genetic risk that will also ... Jane Ferguson:                  Yeah, they must exist. It's probably relatively small numbers. Dr. Sjogren:                        Yeah, they are probably smaller and the risk is lower, but I'm guessing that when you combine these genetic risks you can actually see quite strong with the risk of ever getting C-A-D, or CAD, or any of those other ... or any cardiovascular complication increases. Jane Ferguson:                  Yeah, interesting. So what are you hoping to do next with these data? Dr. Sjogren:                        What are we hoping to do next? Well, publish of course. That's our first step. That's the first part. And now we are actually looking into other kinds of genetic predisposition for different cardiometobolic traits. So we are currently proceeding with BMI and also type two diabetes and related phenotype. So that's our next thing, to sort of explore what kind of ... maybe what kind of hospitalization for the different cardiometobolic traits are most common for individuals for different genetic risk. Jane Ferguson:                  Yeah, yeah. That'll be interesting. That's probably people who have increased risk for genetic CAD, they also have increased genetic risk for related things, like ... Dr. Sjogren:                        Yes. Jane Ferguson:                  ... obesity and type two diabetes. Dr. Sjogren:                        That will probably be a huge overlap. But even if you look at them separately, because we have quite a big data, so you can distinguish those [inaudible – pieces]. Of course, we haven't actually looked what happen if you would, which would also be interesting to see sort of a combined cardiometobolic genetic risk. That would be an interesting challenge. Jane Ferguson:                  Right. Plenty of work to do. Dr. Sjogren:                        Yes, always. Jane Ferguson:                  Alright, thank you.                                                 Dr. Jessica Van Setten is an assistant professor at University Center Utrecht. Studying the genetics of rejection of heart transplant. She presented novel genetic loci from donors and recipients associated with acute rejection. As Jessica mentions, she's actively building a resource of data for transplant donors and recipients. So if you have access to data or samples and are interested in furthering the efforts of the International Genetics and Translational Research and Transplantation Network Consortium, you can find more information at wwww.iGeneTRAiN.org or by contacting Jessica directly.                                                 I'm here with Jessica Van Setten from Utrecht and her poster is entitled, "The Effect of Genetic Variation in Donors and Patients on Rejection After Heart Transplantation".                                                 So, Jessica thanks for talking. I would love to hear a bit more about your research. Dr. Van Setten:                 So, yeah, we are a ... I'm part of iGeneTRAin, which is an international consortium in which we try to collect as many transplants cohorts as possible that may or may not have genetic data. And so for we have genotyped over 40,000 samples of which 12,000 full donors  and recipient pairs. So this means we have DNA of the donor and of the recipient. So we can actually do ... we can check how this matches. Jane Ferguson:                  That's a really cool resource. Dr. Van Setten:                 Yeah, I think it's really exciting. It's one of the very first things I think in the world that actually does this type of research and we do need large samples size in people studies and other studies. Jane Ferguson:                  Right. Dr. Van Setten:                 So, I'm really excited to be able to show now our very first results of GWAS actually in donors and we ... so last year we have also shown the very first results of the GWAS recipients and we are working on loss of function study. So this means we are interested in genes that are absent in the recipient but are present in one or two copies of the donor. Jane Ferguson:                  Okay. Dr. Van Setten:                 So we can see if this actually ... if this specific genes pose rejection after transplantation. Jane Ferguson:                  Interesting. Okay, so what kind of things did you find? Dr. Van Setten:                 So this is actually very novel. These analyses were run only like one or two weeks ago. So, these results are the reason I didn't put the gene names here. Right now we have only a thousand donors [inaudible - in pairs for hearts]. Jane Ferguson:                  Okay. Dr. Van Setten:                 But we aim to have another at least 500 pairs by the end of next year. We will use another 600 for replication probably later this year. So what we find so far is basically a bunch of common snips that associated with rejection at year one. Jane Ferguson:                  So do you only included pairs where there was rejection at some point? Then you excluded pairs where there was a successful transplant? Dr. Van Setten:                 No, no. So this is ... actually I think we are doing pretty good at transplantation. So we have on average less than 30% rejection across all cohorts. And what we do is in this case for disposition we did genotype association to see if there was rejection at year one. Like within the first year after transplantation yes or no. And then it was basically a case control study between those.                                                 So what we aim to do in the near future is also do time to first biopsy to rejection and hopefully get more powerful analysis there. Because then you get actually time to advance as your outcome. Jane Ferguson:                  Yes, interesting. Dr. Van Setten:                 Yeah. We're really excited about it. Jane Ferguson:                  Yeah. Yes, so it looks like you found, you know, like a number of signals that genome-wide significance. Dr. Van Setten:                 We do. Yeah and that's only with a thousand samples. So, of course we do need replication to see if they are actually true, which I think is really nice. And what we aim to do after is our next sequencing experiments to see if it actually, you know, these things are expressed in the heart. So for this we have ex-plant hearts of the recipients but we also have the heart biopsies of the donor. So I work at Utrecht and there we do regular biopsies. So the first few months is actually almost every week and then it's once ... I think every six months. So we can also use those for our next sequencing experiments. Jane Ferguson:                  Wow. So they can look at like the changes in expression over time. Dr. Van Setten:                 Exactly. Jane Ferguson:                  And sort of do like as a temporal EQTL to look at its genetic predictors of expression over time. Dr. Van Setten:                 Yeah, so there is so many very nice things we can do with this. And we need a consortium, we're not the only ones doing this but we are also working on other markets like cell-free DNA and protein expression in the blood to see if we can have markers for rejection there. So we can hopefully in the future, even weeks before you can actually see the rejection in a biopsy, already prove that it's going to happen based on blood. So you don't need those invasive biopsies, you can just take a little bit of blood and check that and then say, okay, we actually need a bit more immunosuppressive drugs. Or you know, it's all fine. Maybe you can lower it a little bit and see where that ends. Jane Ferguson:                  Right, right. That's really cool. So was this done mostly in European ancestry populations or is this ... Dr. Van Setten:                 Yeah, it's mostly European. We used mixed models and we just included all we have. Because we only have such a limited sample size we decided to just go for everything and use mix models. Jane Ferguson:                  Right. Dr. Van Setten:                 So I think about the thousand samples, it's probably about 700 European samples. And the others mostly African-American ancestry and then a few Asian and other ethnic populations. Jane Ferguson:                  Yeah, really cool. So you probably ... this is like hot off the press so it's not published yet. Are you planning to write it up soon and ... Dr. Van Setten:                 Yeah. No, we are planning to write it up soon. So we may want to combine this with our loss function results and we really hope to have everything ready before, let's say, the first of January and then write it up. Jane Ferguson:                  Very cool. Dr. Van Setten:                 Yeah. Jane Ferguson:                  Anything else you want to say? Dr. Van Setten:                 Well, I do ... this is also on my poster, we really want to invite other people who may have transplant data, even if you only have phenotypic data data. You know, you have large transplant cohort collected over the years, but you don't have genotype data yet. Please do contact us, because we are always in need of more samples. Especially for heart, because right now we only have a thousand. And even if ... like in the Netherlands we were one of the largest transplant centers but we only do 12 to 15 transplants a year, heart transplants a year. So we know how difficult it is to get higher numbers of samples and we know how it must be the same for all other cohorts. So we really hope with these types of collaborations we can actually start doing genetic studies in heart transplants. Jane Ferguson:                  Interesting. Okay, so can people go to iGeneTRAiN.org ... Dr. Van Setten:                 Yes. Jane Ferguson:                  ... and then find your contact details? Dr. Van Setten:                 For sure. Jane Ferguson:                  Or maybe email you directly and ... Dr. Van Setten:                 That's also fine. Yeah. So you can email me on j.vansetten@unu.transplant.nl. Jane Ferguson:                  Awesome. Alright, thank you Jessica. Dr. Van Setten:                 Yeah. Jane Ferguson:                  I'd like to give a special thanks to all the poster presenters who agreed to share their unpublished research with you via this podcast. And I'd like to thank you for listening. Talk to you next month.  

Jane Ferguson:                Hi, everyone. Welcome to Episode 08 of Getting Personal: -Omics of the Heart. I'm Jane Ferguson, and this podcast is brought Circulation Cardiovascular Genetics and the AJ Functional Genomics and Translational Biology Council. This is the September 2017 episode, and this month we delve into some of the newest research coming out in the October 2017 issue of CircGenetics. If you go on to the CircGenetics website at circgenetics.ahajournals.org, you can see the table of contents for the latest issue, and see sneak previews of upcoming papers that are published online in advance of the next issue. You can also more in-depth materials for each paper, like editorials and other resources, so it's a really nice way to keep up with the newest cardiovascular genomics research.                                            One particularly interesting paper included in the October 2017 issue is entitled "Diminished PRRX1 Expression Is Associated With Increased Risk of Atrial Fibrillation and Shortening of the Cardiac Action Potential," from Elena Dolmatova, Nathan Tucker, Patrick Ellinor, and colleagues. This is a really nice paper which highlights some beautiful approaches used to go from a GWAS hit to functional understanding. This type of research is challenging but really crucial as we move on from the GWAS discovery era, and I recommend you go online to read the whole paper. I talked to the first authors, Elena and Nathan, to find out more about their work.                                            So I'm here with Doctor Nathan Tucker and Doctor Elena Dolmatova, they're the first authors on a recently published paper. So, welcome and thank you for joining us. Nathan Tucker:                Thank you. Jane Ferguson:                So, for the benefit of our listeners, could you tell us a little bit about yourselves? Nathan Tucker:                Sure, so my name is Nathan Tucker, PhD, researcher, instructor of medicine at Mass General Hospital and the Broad Institute in Boston. Elena Dolmatova:           And my name is Elena Dolmatova, if you could probably tell, I'm Russian by origin, currently I'm a internal medicine resident at Rutgers University and I'm in process of applying for a cardio research fellowship. Jane Ferguson:                And so the two of you co-led a really interesting publication that came out this month, so congratulations on that. Nathan Tucker:                Thank you. Jane Ferguson:                So, some of our listeners may not have had the time yet to read your paper, so I was hoping you could give us just a brief summary of what this publication was about. Nathan Tucker:                Sure, I'd be happy to start. So the focus of this paper, and a lot of the other work that goes on in our group, is genetics of what's the most common cardiac arrhythmia, which, atrial fibrillation. So, really over the past decade or so, once these large Genome-Wide Association Studies have been performed, in order to identify regions that are associated with disease, and then we followed up on that, to try to determine some of the mechanism that underlies those loci. So this is an example of that type of study. So, I think for the vast majority of these regions, and this is not exclusive to our disease at all, but the loci that are associated reside in what we used to refer to as "junk DNA" or intergenic DNA, that we now know is regulatory DNA. But the important point is, we have no ... for the majority of these loci, we have no idea of the mechanism through which they confer risk. So the point of this study was to examine a single locus for atrial fibrillation, which we'll call AF for the rest of this, and try to determine the mechanisms through which is might confer that risk.                                            So, kind of the start, the study started back in an era where we were using, you know, genotyping chips, and large cohorts of cases and controls to identify variation then impute variants to see what's associated. But we wanted to go into this study with a comprehensive understanding of what's at that locus. So to do that, we performed sequencing and a pretty modest cohort of 500 cases and 500 reference from Framingham Heart Study. And although it didn't really change what we knew about the landscape of that region, we were able to go in with a confident understand of what variants might be associated with disease risk at that locus.                                            So then Elena really spearheaded a lot of the work to identify which of those variants might be important at the locus, so I'll let her take over from here. Elena Dolmatova:           So, as Nathan mentioned earlier, many of those intergenic regions contained enhancers of regulatory elements and a lot of data was coming up about the genetic loci in the genome. And we wanted maybe to narrow down that region, down to some of the pieces that could be active, or could be functional. So we used the activity markers that [inaudible 00:05:20] modifications and DNA hypersensitivity, to identify those potentially active elements. And then we tested them on zebra fish [inaudible 00:05:31] to see if they're actually active in the heart. When we realized that they are active in the heart, we were able to then do a little bit more targeted [inaudible 00:05:42] after that, identify the ones that are actually differential between the risk and non-risk allele. So in that some of the SNPs can be actually changing the enhancer function. So this is how we actually identified the SNP that was actually functional.                                            Then next what we wanted to do is to link this enhancer to the gene. And initially we performed a Hi-C analysis, which is a chromatin conformation capture. Which is actually captures a 3D structure of the DNA and shows what regions are interacting with what regions. And we were able to see that this SNP was within the same block as the PRRX promoter. To maybe narrow down and to identify the interaction a little bit better, we performed 3C analysis. That allowed us actually to link the enhancer directly to to PRRX promoter. So, we have the SNP that would change the activity of the enhancer, we have the enhancer linked to the promoter, we wanted to see if the change in the SNP would have any functional consequences on gene expression. And we performed a QTL study.                                            So what it was, is we looked at the genotype of the SNP and related it to the expression of the genes within that region. And among all the genes that we actually tested, only PRRX1 expression was affected, with the risk allele conferring decreased expression of the gene. However, the consequences of gene decreased PRRX expression were yet to be revealed, and that was part of the critical experiment that Nate focused a lot of his efforts on. Nathan Tucker:                So, we found the gene that was important, we knew the directionality, but a lot of times, with these type of functional genomics where you, which I hope we can elaborate a little bit more later, is that the results given, like, what gene you identify and the direction, aren't as clear as you would sometimes think for a given disease or trait. So, for example, a lot of the coding variation for AF is identified in ion channel genes. It's thought to be an electrophysiological disease. But here we identified a transcription factor, which is what we actually thought to be a developmental transcription factor. So, you kind of went in from a functional angle and say, "Alright, what are the consequences of this alteration?"                                            So we used two different models, the first was zebra fish, which I had reasonably strong background in. And we knocked the gene down, examined the development of the heart, everything seemed reasonably normal, and then we actually examined the electrophysiology of that heart by optical mapping, and we looked at the action potential duration. Which is basically the cellular phenotype for ... that governs depolarization, re-polarization and thus contraction of any given myosin. And found that that action potential duration in the zebra fish was shorter. We wanted to follow that up and confirm it in a different model, we actually created a CRISPR/Cas9 media knockout of the gene, and embryonic stem cells, and differentiated those into cardiomyocytes, and then saw that similar decrease in action potential duration.                                            So, kind of altogether, I mean, a paper that spans a lot of different techniques, but what we did, we took associates in locus for a human disease, we found a variant at that locus that seems to drive differential expression of a nearby gene, and then modeled that gene effect in order to give a physiological phenotype that matches with the disease of interest. Jane Ferguson:                Something that struck me, I think you sort of touched on this a little bit earlier, is, you know, the SNP that you end up showing to be causal, are S577676. It's not necessarily the one that you would have picked sort of a priori, by going through the GWAS strength of association, and you know, I know we sort of all know that we shouldn't place too much weight on the specific P value of an association when we're doing GWAS, but I think a lot of the time, that sort of ends up being a screening mechanism, and people look at sort of the strongest SNP and think that's probably going to be the most biologically relevant. But do you think that we're sort of, you know, by relying on this relative strength association the GWAS to pick targets, we're really missing a lot of the potential biology that's underlying these diseases? Nathan Tucker:                The way you look at a normal GWAS locus is, we've always traditionally marked them with what we call a sentinel SNP which is a SNP that's most associated, and then other times, act as though that one might be mediating the function? Whereas in reality, you'll see a block of roughly equivalently associated SNPS that rely or lie within the same linkage to [inaudible 00:10:42] block. And, at least for our cases, when we move forward we really wanted to treat all of those SNPS to be equivalent. And in this one, the SNP that turned out to be functionally active was actually below, a little below that, what we would call that sentinel SNP.                                            So I think there are a couple different explanations for that. One is, there could be more than one functional variant at a locus, and the LD structure kind of heightens that. The other could be that the sample you're using in order to identify the SNPs of interest or the SNPs that are functionally associated may be biasing you a little bit, particularly with a smaller cohort like this. But I will say, for our SNP, when you look at it in the larger GWAS studies, it's again roughly equivalently associated, is what we'd call a top SNP. So, to answer your question briefly, we always look at all of them. We have to be inclusive when we're trying to find functional variants. Jane Ferguson:                Yeah, no, absolutely agree. And that's one thing I absolutely loved about your paper, was how you, you know, pulled together all these different data types and used as many different resources as you had access to to really tackle this question. So I wonder, out of all of the different things you did, what was the most challenging aspect of this study? Elena Dolmatova:           Well, that was something that nobody's really done before. It was something that there were few studies out by when, the time we started, that would tie some of the GWAS hits with the mechanism of the disease development in [inaudible 00:12:16] in other conditions, but there was really no paved road to take to get an answer to our question. Nathan Tucker:                For me, personally, I mean, I really started this project, which, you know, this project took a considerable amount of time, and I started as a cell biologist, and modeling gene function in zebra fish, and by the end, we ended up using so many different techniques, and integrating so many new types of data into this study, that I don't even know what I would define myself as anymore. So I think it's a, it's challenging to learn how to use all of these new data, and to generate these new data both. That's the kind of, I don't know, that's why we got into this business. That's why people want to do research. So that's, it's challenging, but it's rewarding too. Jane Ferguson:                Absolutely. And so, to look at the converse aspect, then, was there anything that was easier than you expected? You know, did you have a eureka moment where you sort of said, "Yes, now everything is falling into place."? Nathan Tucker:                So, I think, yeah. I've been part of studies where I've really felt that that's happened. And given all of the kind of independent moving parts that were in this study, it was, it's really hard to think of one thing that clicked. You know, every sub-component had its own individual moment where it may have clicked, but really, until they all started, all the pieces of data started to come together, you never really felt that eureka moment. And, you know, I think that's part of what science is in normal ... I mean, this paper was a lot of sweat. And not only mine and Elena's, you have all of our collaborators as well. But I will say, you know, at least using the genetics as a basis, and the GWAS data as a basis, we knew that something was there, going in. We knew that we weren't on some wild goose chase, but really we're filling in a gap knowing that we have a strong basis to build on. Jane Ferguson:                Yeah. It's good to hear from you, sort of that, you know, you had to do all of those experiments, they were all necessary, because I think, a lot of the time, when people are trying to follow up GWAS findings, they're really, I don't know, they sort of have a preconceived idea maybe of what path they want to go down, and I think that's not the answer. I think we have a lot of GWAS hits now, and I think the sort of approach that you did to do all of these different experiments and to just do the hard work that's required to figure this out, I think is really necessary and very laudable. Nathan Tucker:                Thank you. Jane Ferguson:                So, was there anything that surprised you along the way? Elena Dolmatova:           Well, Nathan touched a little bit on that. It was nice to see all the electrophysiological phenotype, that was quite amazing. And the fact that the directionality of the effect was ... fit with what we expected to be, with the risk allele, and how we were able to demonstrate it both in zebra fish and human cells, and they were, again, matching. Seeing how those results could tie to the genetic data and what we know about atrial fibrillation susceptibility, was great and rewarding. I wouldn't call it surprising. More like rewarding. Honestly, we were concerned that we wouldn't be able to observe any physiological phenotype. Because, I mean, we didn't even have a good reason why PRRX would be involved in atrial fibrillation, that was a transcription factor, not an ion channel, like everybody thinks about, everything is an ion channel, by the way, not the same. So it was great that we were actually tie the transcription factor to the disease when we not even quite sure that it would happen. Jane Ferguson:                Yeah. Yeah, and I suppose, you mention the ion channels, and of course there has been several other loci that have been identified for AF, and from your work, how important do you think PRRX1 is, compared to these other loci, and, you know, do you think that this sort of study has to be done for every single one of these loci to really understand what's causing the disease in different people? Nathan Tucker:                First of all, I think the answer to that question depends a little bit on what the person asking it would deem to be important. So, if we're looking at GWAS signals for effect size, generally the effects of each given locus are pretty modest, and PRRX1 locus isn't even at the top for AF. So if you were looking for, like, clinical risk stratification, then it's not going to be the most important locus for AF. But I think what looking at these types of stories does, is identifies novel mechanisms for disease pathogenesis. I think they're often unexpected, it steps outside of the pathway analysis, and candidate gene approaches that have been used in the past. And a really unbiased way to look at, you know, why the disease risk has changed. I think if our ultimate goal is to develop new therapeutics, you know, we don't know which one of these loci might give us that hook into developing the new therapeutic.                                            So the second part of the question, I guess you'd say, does it need to be done for each locus? So, yes, I guess, given what I just previously said. I think we've invested a lot of time and effort and resources into identifying all these loci, to really, really large discovery efforts, but if we want to really maximize what we've done there, with that discovery effort, then I think we owe it to ourselves as a field to identify mechanism, and see which one of these are going to give us that hook to make that next big clinical therapeutic discovery.                                            But, that being said, you know, this study, as much as we love it, it was really laborious. And it was a lot of moving parts. And it was a lot of work from a lot of people for a lot of time. And if we're going to have to do this for every locus, not only for AF but for all of the other GWAS that have been performed ... it's just an unacceptably slow rate of discovery, so ... What we've been doing since this one has been completed is, you know, trying to find some higher throughput ways to screen through what might be functional variants, to integrating or generating new transcriptional data sets, so we can better predict what might be the chain at a given locus, and working on our models as well for when we want to look at physiology. So we hope that we can talk more about these briefs soon, a lot of them are in the works, so we'll update soon. Jane Ferguson:                Oh, that's exciting, yeah. And I think you've laid out a really nice blueprint, how you can do these kind of experiments, and how to follow up a locus, and, you know, I'm sure you learned a ton a long the way, and you both mentioned some of these, you probably can't talk about everything you're working on, but I suppose with the benefit of hindsight now, is there anything specific about sort of the study design or the methods along the way that you would change for future studies? Elena Dolmatova:           One of the things that when we started, we started having one toolkit. And when we're finished, we had a completely different toolkit. And it's all because the science is developing every day, so every moment, something new comes up, it's ... In the beginning, there wasn't enough epigenetic data to, for us to guess about the enhancers. And it was coming in almost on a weekly basis, and we were trying, and pretty successful, implementing it, all the knowledge that was acquired and published, almost immediately. We almost had to implement CRISPR/Cas to knockout PRRX in the embryonic stem cells, and they were the five cardiomyocytes, after that it's from them. So all of that knowledge was not there when we started the study. So we actually implemented them almost immediately. But in hindsight, if we had all these tricks up our sleeves back then, of course it would be much more efficient and finish it much faster. Nathan Tucker:                I'll follow up on that, too. It's like, is one thing we learned too, which Elena mentioned, all of epigenomic data sets that were updating, and all the techniques that were updating, I mean I really think one thing that we learned was, our prediction is really only good as the data that we put into it. And I think our plan to learn, particularly for all the other loci, is we really need to understand the epigenomic landscape in relevant to [inaudible 00:21:23] and cells, so, you know, moving towards that first, before screening on what variant or what transcript might be important is a really important step for us, and one that we've used as we've moved forward. Elena Dolmatova:           Mm-hmm (affirmative). Jane Ferguson:                So, what do you think would be the ideal follow-on study to this paper? Elena Dolmatova:           Well, we know that diminished PRRX expression shortens the action potential, but we have little idea about how it is happening. Is it acting through the changing cardiomyocyte state? Is it altering maturation or development of cardiomyocytes? Is it governing ion channel expression? Or maybe changing something with intracellular calcium regulation. Transcription factors can have many targets, and we're not quite, quite sure what the targets are in this particular case, so that would be a nice study, thought that I, to follow up on this study. Jane Ferguson:                So I suppose just to wrap this up, is there any message that you're hoping that readers will be able to take away from your paper? Nathan Tucker:                Sure, I think from, if we're going to look from a disease standpoint, I think the finding regarding the relationship between the gene and atrial fibrillation is important, but I think, I hope we've also illustrated somewhat through this study how complex the genetics of the disease are. I mean, it's ... so much of the focus in the past has been, really, on ion channel regulation, but there's so much more to this condition that can really, is yet to be discovered. So I hope we shed a little bit of light on a path forward for how to uncover some of this other, these other mechanisms, over the next few years.                                            And then I think, hopefully the other thing, well, at least, that we hope gets relayed through this and other similar studies from other groups, is the importance to fill in this knowledge gap between the population genetics stories, the GWAS studies, and that basic biology. And I think there's a lot of potential for making important discoveries, for human health and clinical intervention, in that space. So hopefully, us and other groups can use some of the things that we did in this paper. And hopefully improve on them, to address this in other GWAS loci, to keep the field moving forward. Jane Ferguson:                Yeah, I couldn't agree more. I think that's a really important message, and I think you've done a fantastic job on sort of starting us down that path, to really translating these GWAS findings into more meaningful biology. So, Elena, Nathan, thank you so much for taking the time to talk to me. Nathan Tucker:                Thanks a lot for having us. Elena Dolmatova:           Thank you. Jane Ferguson:  And that's all for this month. Thank you for listening, and we look forward to getting up close and personal with -Omics of the Heart, and with you, next month.

Speaker 1:                        Hi everyone. As a quick introduction, this is the full length recording of Anwar Chahal's interview with Calum MacRae from August 2017. A portion of this interview was included in episode seven of the Circulation Cardiovascular Genetics podcast "Getting Personal: Omics of the Heart". As we couldn't fit everything into that regular podcast episode, we've released the unedited version as a special, feature-length podcast. Enjoy. Dr Anwar Chahal:            My name is Dr. Anwar Chahal. I'm a Cardiology Fellow in Training from London, U.K., and I'm doing my research fellowship here at the Mayo Clinic, and I'm very honored and delighted to have our guest, Dr. Calum MacRae. I searched for Dr. Calum MacRae's biography online and it came up with a Wikipedia page talking about somebody who's a rugby coach. So, Dr. MacRae, I hope that's not another one of strings to your bow, that's something else that you manage to squeeze in amongst everything else that you do in your busy and punishing schedule. Dr Calum MacRae:          I did play a little rugby in my day, but I haven't coached any, I can assure you. Dr Anwar Chahal:            So, you are the Chief of Cardiovascular Medicine, you are an MD, PhD by training, and you are Associate Professor at Harvard Medical School, and your expertise, amongst many other things, internal medicine, cardiovascular diseases, but in particular, inherited cardiovascular conditions. Is there anything else that you would add to that? Dr Calum MacRae:          No, I'm a big fan of generalism, and I am quite interested in cardiovascular involvement in systemic disease as well, but largely as a means of keeping myself abreast with the biological mechanisms in every system that seems to be relevant to cardiovascular disease. Dr Anwar Chahal:            So, that reminds me. Once I heard you talk, and you mentioned to all those people that were considering cardiovascular genetics the importance of phenotype and actually how people have become increasingly super-super-specialized, becoming the bundle branch block experts or the world's authority on the right coronary cusp of the aortic valve, and how things were now going full-circle as people actually need better and better, more general understanding so that we can accurately phenotype. And you once joked that you'd actually done residency three times, so you know the importance of having a good generalist base, so could you expand a little bit on that? Dr Calum MacRae:          Well, I have to tell you, it wasn't a joke. I did actually do residency three times. But, I think the most important element of that theme is that biological processes do not, unfortunately, obey the silos in which medical subspecialists operate. So it is increasingly important to have a broad-based vision of how phenotypes might actually impact the whole organism. That's particularly true because it helps us ratify disease, so that there are mechanistic insights that come from the different cell types and tissues and biological processes that are affected.                                            I think, in general, that is something that we've all appreciated, but as time goes by and people become more and more specialized, it's less regularly implemented in day to day clinical practice. And so, particularly as molecular medicine becomes more and more penetrant in clinical disease management, I think you're going to see a return toward some generalism. Obviously, procedural specialties are the exception in many ways in this setting, because you need concentrated procedural skill. But in general, particularly for translational scientists or scientists who are interested in the underlying mechanisms of disease I think, I see a general movement towards a degree of generalism. Dr Anwar Chahal:            Indeed, and in terms of, as you say, trying to understand those disease processes and trying to, let's say for example, make sense of the incredible amounts of information that can now be gathered with genomics and high throughput omics, you believe that it is actually more of a requirement to be able to understand that now that we can gather this high resolution and broad depth of data? Dr Calum MacRae:          Yes, I agree. I think one of the core elements of modern clinical medicine is that the phenotypes have, in the last 50 to 100 years, we've really focused more on improving the resolution of existing phenotypes than expanding the phenotypic space. To be completely frank, I think we've extracted a lot of the information content that we can from the phenotypic space that we've explored, and what we need to begin to do is to find ways to systematically expand that phenotypic space.                                            I think there are a lot of reasonable ways of doing it just by thinking about other subspecialties. So, for example, in cardiovascular disease, we've focused very heavily on anatomy and physiology, but we haven't really done much in the way of cell biology. Whereas, in immunology, partly because there's access to those cell types, it's possible to do much more detailed cellular phenotyping. In neuroscience, we're now doing functional MRI, and looking at individual subsets of cells in the brain, and their function in the context of particular challenges.                                            My general thesis would be that the type of strategy would serve us well and that there's also, I think, an important mismatch between the dimensionality of phenotyping that we currently undertake and the scale of the genome and epigenome, transcriptome, et cetera. So, it's not surprising that we can't be convoluted genome of 10 to the nine variants with a phenome that are present only really has about a 10 to the four phenotypes. And so, I think some systematic right-sizing of that balance will be necessary.                                            There are lots of things that we record that we don't even think of as phenotypes, and there are phenotypes that we record that we don't really think about how to optimize the information of content. And so that's one of the things that we have begun to invest time and energy in. And thanks to the support of the American Heart Association, Verily, and AstraZeneca, as part of the One Brave Idea, we have elected to fully focus on that area in particular in coronary disease. But I think it's a generalizable problem with much of modern medicine that we tend to have focus on phenotypes that, in many instances, date back to the turn of the last century rather than to modern molecular and cellular biology. Dr Anwar Chahal:            So, you beautifully brought us to the first question, which was to ask you about One Brave Idea. Could you just, for our listeners who aren't familiar with that, just give a little bit of a background on One Brave Idea, and you've already thanked the people who have funded that, but how did you actually reach the point where you thought that this is something that really, really needs to be done? What's the process of reaching that point of bringing this idea to fruition? Dr Calum MacRae:          I think we had recognized in many instances that the families that we were seeing in cardiovascular genetics clinics were much smaller, the diseases appeared to be less penetrant than the original families that we studied when we cloned many of the disease genes. This was work that I did as a post-doctoral fellow in John and Christine Simons lab many years ago.                                            One of the things that was pretty obvious was that there were subtle pre-clinically or sub-clinically affected individuals in almost every family. And that made me ... That implies that the average family is so different from the extreme family. Is it something to do with either the resolution with which we were assessing disease or are we actually just measuring the wrong elements of the underlying genetic trait? So that, for example, is a dilated cardiomyopathy family actually a family that is susceptible to dilated cardiomyopathy in the context of some unmeasured conditioning variable, maybe a viral infection or an exposure. And because we're not measuring the exposure, or we're not measuring the underlying diaphysis, we're only measuring the final state, so we only classify people as being affected if they actually have an extreme phenotype. Are we, therefore, missing the core elements of the biology?                                            As part of doing that, we began to look outside the heart for other phenotypes, and one of the things we recognized ... This was in cardiomyopathy ... Was that different cardiac phenotypes were really aggregates of much more granular, multi-system phenotypes. So there would be families who would have dilated cardiomyopathy, but they would also actually have abnormalities, for example, of the distal interruptus muscles, and no other muscle group in their entire body. And in fact, the distal interruptus muscle phenotype was much more obvious than any cardiomyopathic phenotype.                                            So you start to understand that either other extra cardiac or electrical phenotypes, or maybe even sometimes neurofunction phenotypes are more penitent features of some of these disorders, albeit rare disorders. And so that immediately leads you to think are most of the common traits that we look after really aggregates of things that really only share the relative frequency of the core phenotype, which often dates back to decades earlier when phenotyping was at a much more superficial level.                                            So that vicious cycle perpetuates itself if we never look more deeply or look outside the constraints of a particular subspecialty. And so we have begun many, probably almost four years ago, to build a sort of next generation phenotyping clinic where we tried to bring either cell biology or molecular biology from outside the heart into phenotyping patients in a cardiovascular clinic. That idea was in our DNA, that's probably not the right way to say it, but it's something that we had worked on in a cardiomyopathy setting. Dr Anwar Chahal:            Right. Dr Calum MacRae:          And so then when the RFP for One Brave Idea came out, it seemed like a natural expansion of that to try and think about how you could apply new phenotyping in current disease. One of the inferences from that line of thought is to move, essentially, beyond ideally much upstream of the shared final common pathway so that you can begin to identify discreet underlying mechanisms.                                            And then, given the success of cardiologists, and cardiology in general, in prevention, it became obvious that really what we wanted to do was to try and understand not just disease, but also wellness. And to do that in a way where we could potentially detect the transition from wellness to the very first stages of the disease or the diseases that we have labeled as atherosclerosis or coronary artery disease.                                            That was the genesis of the central idea of the application and something that, obviously, we were excited to get the chance to pursue as a result of the generosity of the funders, and the vision of Nancy Brown at AHA and Andy Conrad at Verily, to not only award funding in a different way, but to also really try and drive us to think differently about how we executed on a research product. How we move forward, not with a five-year plan, but with a rapid cycle early hypothesis testing, fail fast and fail early, if you are going to fail, strategy. Rethink not just the focus of the research project, but the mechanisms by which you execute on it.                                            I think one of the core elements of this is, obviously, we want to make sure in doing this that we build on all of the incredible work that's been done in the last 25 or 30 years in coronary disease, whether it's the pharmacologic work, or the genetics work that has emerged in the last few years. Those are all important building blocks, and what can you do that leverages all of that existing data and adds to it? Phenotype is obviously one of the most important areas where you can bring something to the table that add to existing genotypes and also layers in on top of existing pathophysiologic models.                                            From my standpoint, it was an efficient strategy, and one that we hoped would also help us engage the people throughout the community in different ways of using data that might already have been collected or we were going to be able to collect for the first time. Dr Anwar Chahal:            In terms of One Brave Idea, where is that right now in terms of execution, as you mentioned? What's the progress so far, and is anything that's come out already that you can share with us? Dr Calum MacRae:          Yeah, of course. So we have begun a variety of different approaches to thinking through the best way of exploring this phenotypic space. One of the obvious things is you can take a couple of strategies to move into this unknown unknown. One of them is to take an incremental approach to move slowly from the areas where we have already established knowledge, and to move into new areas from that home base. And the other is to take a more agnostic strategy, which is to say are there orthogonal ways of thinking where you could look at a particular type of biology in a very focused way in coronary disease. You can define that in lots of different ways. You can say maybe we do it at an organelle level, or maybe we do it at some orthogonal component. The microbiome might be an obvious one. Another one that has been considered would be nutritional or other common environmental exposures.                                            The nice thing about the flexibility of the funding is that we can afford to test multiple different hypotheses early on, see which of them has the best signal, and then invest more deeply in those that have shown early signal. At the moment, we have multiple active projects that are really testing those initial hypotheses. Is there a way of moving from the known genes that cause coronary artery disease and trying to understand are there novel phenotypes that are associated with those. And then another approach would be to take people with very early or pre-clinical disease and test areas of biology that have never been tested in atherosclerosis or in coronary disease in a systematic way.                                            We could imagine lots of ways of doing it, but you might think about, lets say, looking at endocytosis, a process that we know already is affected by the core genes in familial hypoglycemia, but we've never really found ways to measure that in a rigorous fashion. In large populations of individuals, are there different ... Well, we know already there are different forms endocytosis, but are there discreet port ablations that might affect those.                                            Another way of looking this might be to pick an organelle. Pick the peroxisome, or pick the nucleolus, pick some other element and ask how does the function of this organelle change in individuals who have early coronary disease. Where its boring each of these types of things systematically, and trying to learn not just which are the most important areas to focus on, but also trying to learn are there strategies that are useful that you could use in another disease. In other words, are there generalizable approaches to expanding phenotypic space that makes sense.                                            I think one of the things that perhaps we underestimate about a genome is that it is the only bounded dataset in all of biology at the moment. There are no other bounded datasets. There is an infinite number of potential exposures. There's an infinite number of potential phenotypes that we could record, or at least as far as we know, are there ways of beginning to establish the boundaries of the phenome, the boundaries of the exposure or the exposal and how do we begin to do that in a way that efficiently yields new information. That's where we, as a consortium, have focused in the last few months.                                            We're also, obviously, investing time and energy in thinking how do we begin to remodel the way in which research is evaluated and funded. The strategy that we've taken there is almost like a not-for-profit venture fund where we try and bring in ideas that we think might be able to leverage what's known already and move the field faster towards new pathways or new approaches to prevention, which are the core deliverables of the One Brave Idea award. As part of doing that, we obviously get the chance to interact with lots of exciting and creative scientists and that's something we're looking forward to doing in lots of different venues. We're reaching out to lots of people and lots of people are reaching out to us. We're trying to find ways to evaluate and prioritize science and then bring that science to fruition through novel approaches to funding it, either directly or as a joint venture with a foundation or some other funding source, or even as a joint venture with a commercial partner to try and move the field forward as efficiently as possible. Dr Anwar Chahal:            Thank you very much for that, and I'm sure we all eagerly look forward to the results that are going to be coming out from One Brave Idea over the next few years. I'd like to now move on to genomic medicine training and you were involved in a statement that was put out regarding this. I think training across the world has increasingly recognized the importance of genetics and genomics, but I just want to share one little anecdote.                                            My wife is a primary care physician, and I was visiting the GP practice where she works, and she'd mentioned that I had an interest in genetics and genomics. One of the partners came out with one of these reports that a patient had sent their sample to a private company, got this analyzed, brought it in to the clinic appointment and asked for an interpretation. The GP partner said to me, "I've absolutely no idea what any of these numbers, values, et cetera, mean, and I actually am looking forward to my retirement, because I really don't want to have to cover all this. Can you help me with it?"                                            I sort of remember hearing Dr. Weinshilboum talk here at Mayo Clinic, who's really pushed forward pharmacogenomics, and he's been arguing for quite some time, as I've heard you say as well, that genomics and genetics is just going to be a part of the medical record in the same way that hemoglobin or a chest x-ray is. People better catch on because it's here, it's available commercially. People can send their samples directly, without the doctor's involvement, and then it's trying to make sense of all of that.                                            I think, as a community, research and clinical, we have to take this very seriously. I'd be grateful for your insights on that, and then if you could then tell us what would be the best way for the up and coming generation and for programs to incorporate that into their training? Dr Calum MacRae:          So, I think you're right. There is a general tendency in the public domain to test a variety of different genotypes. And in many instances, I think, the key elements are how do we as a profession, conceive of these tests? I think one of the things that we forget, perhaps at our peril, is that many of these things are problems that we've encountered before. There's a natural cycle of different tests in medicine where they start off in the academic medical centers, they propagate into the periphery, and then eventually they're assimilated as part of internal medicine.                                            I think the scale of genomics is obviously somewhat broader than many individuals have seen in the types of data that they deal with on a day to day basis. But I think that's something that's happening in everybody's life. In every aspect of your life, you have many more channels to deal with. You have many more choices in the supermarket to deal with.                                            So, I don't see this as a sort of existential challenge to medicine. Quite the opposite. In my experience, the core things that we need to remember is that DNA is no different from any other assay except for the fact that it's relatively straightforward to do DNA diagnostics. It's technically not as sensitive a set of biochemical issues, as are many other assays that we use in day to day clinical practice.                                            The other thing that I think is perhaps a key element is it, as I said a few minutes ago, it's a bounded dataset, and it's stable for your whole life. You only need to have it tested once. So, to sort of invert the typical diagnostic paradigms, instead of a primary test being interpreted in the context of an ongoing clinical event, the test may have been present for decades, and the result will evolve over time, in light of the changing phenotype or some new information with respect to that genotype.                                            What I've actually looked on genomics as is almost an organizing principle for the way that you build care. In fact, I see quite frequently, we now probably have an average one or two new patients a month in my clinic who bring their entire whole genome with them, either an axiom or a whole genome. And so, we've begun to really get to know quite well how to manage patients. Obviously, there are a selective of patients. But one of the things that I have found is that patients are really quite astute in understanding that genotype and phenotype are not deterministic relationships. What you have to do is always interpret these things in context of a probabilistic understanding.                                            Most patients, I think, when they're told this, understand that we're going to learn much more about genomics going forward than we will ever imagine we could know at the present. That will involve lots of different things. It will involve new ways of displaying data, new ways of thinking about the data in the clinical context. I actually think one of the most interesting things about genomics, and to be honest, any assay is that they rarely reach any form of maturity until they are used in the clinic, until they are actually used in implementation. For example, many genetic tests at the moment, don't change therapy and they don't change outcomes. But partly, that's because they've never been studied in that context.                                            One of the things that I think Glen [inaudible 00:26:58] has to be really congratulated for is his focus on pharmacogenomics as being one of the early areas in which this will really move forward. I believe that by immersing ourselves in it, by actually trying it in the clinic, we're going to learn much more.                                            Part of that gets back to the original topic that we spoke about, which is phenotype. The only way to really begin to understand collection of phenotype is if you do it in the context of existing genotype, I think. And so, as we move into new phenotypic areas, we're not going to be able to test everything and everybody. I think there, the genome will end up being an important framework, lifelong framework for the management of a patient's diagnosis, prognostication, and then therapy, potentially in that order.                                            I think you need a whole different set of skills. You need a whole different set of technologies. But most importantly, you need information that you can interpret in the context of the person in front of you. Until you can make mechanistically important insights with one person, it's going to be very difficult for genomics to really change medical care. That's something that I think we should be focusing on.                                            I think we've tended to have an associate of strategy for genetics. We haven't driven it into the clinic. As we drive tests into the clinic, whether it's troponin T or whatever, you begin to understand much better how to use them. Although, sometimes, that can also go in quite extreme directions that you may not necessarily anticipate. Troponin originally was a stratification tool for acute coronary syndromes, and now it's virtually a diagnosis in its own right. And I think you'll see that tendency revert over time as people begin to understand the biology of troponin, of isoform switching, and peripheral tissues of the way in which troponin may represent very different disease biologies.                                            At the moment, it seems like it's a very simple and straightforward yes/no type of test. There's no such thing in medicine, and I think that's what we're learning about genomics. Instead of conceiving it as a series of ten to the nine yes/no tests, we're going to end up with a very different vision and view of how it can be implemented in clinical practice. And that can only come from having clinicians and geneticists work together on this. In fact, one of the things that we've been doing in the partners environment with some of our colleagues, and I have NIH funding to do this with Heidi Rehm, with Sandy Aronson, and with Sean Murphy, is to think about how we display data, but also how we collect information in light of that genomic data that helps in an iterative way and a learning fashion, informed genotype/phenotype relationships in a much more probabilistic manner than we have done to date. There are lots of efforts in that space, that just happens to be one that I'm involved in. But I think it's a generalizable approach that you're going to see moving into the clinic in the next few years.                                            From the standpoint of training, I think what you want to do is to get exposure to all types of genetic information so you understand common alleles, rare alleles, genomics, and individual panels. I think the best way of doing that is to have that be part of training programs. In fact, with one of my junior colleagues, Dr. Aaron Aday, we recently wrote a short piece highlighting how important it will be for all of us to come together to think about how do we start to introduce the concepts of genomics into standard clinical training programs. And that's something we're working on fairly avidly at the Brigham, and I'm sure there are ... I know there are efforts at many other institutions to do similar things. Dr Anwar Chahal:            That article was published in Circulation in July of this year, if anybody wants to download that. I think if we talk to clinical trainees and ask them what are their concerns about training, as you know, training can be very long in cardiology, which is a procedurally based specialty, whether or not you become an invasive proceduralist at the end of it, there is that component at the beginning. Do you think a standard, in the U.S. a standard three-year program with two years of clinical and one year of research, can incorporate that at a sound enough level to allow somebody to practice? Do you think we're going to look at increasingly a one-year, or a six-month, sort of add-on fellowship for those interested more on the inherited side or more on the genomic side?                                            I, like yourself, trained in London, and the training programs are longer in the U.K. It was probably six years when you were there, it shortened to five, and now increasingly, it's going to become six and maybe even more with a general fellowship for five years, and then a super-advanced fellowship. Inherited cardiovascular conditions, certainly there, has become a module that is encouraged for people to take and then become somewhat certified in inherited cardiovascular conditions. What do you think there, in terms of incorporating all of that as well as learning basics of echo, and device therapy, and catheterization, what are your thoughts? Dr Calum MacRae:          Again, I look at this as a spectrum. There's a trajectory for all of these types of innovation and knowledge. It starts off being super-specialized, it goes into a more general location, and then eventually, it's an integral part of everybody's clinical practice. I do think that what you're going to see is rather than, and this is already, I think, the case in many elements of medicine. Medicine has already exceeded the knowledge base, even when I was training, by probably a log order in terms of the complexity and extent of content, not that I trained that long ago.                                            One of the core elements that I think that we're seeing is that we need to move medicine from what I believe has become somewhat deprofessionalized state, to one where you're actually focusing not on the actual core knowledge that you bring with you to the table, but actually the way in which you integrate knowledge. So, I think the focus of training is going to change somewhat. It has had to change in other fields. Medicine, I think, for a long time favored that sort of single, comprehensive approach in one mind. And medicine is going to become more of a team sport, and it's also going to become more of a knowledge integrator profession that it has been for some time.                                            It's interesting, when medicine started, there was so little knowledge that you really had to have almost every physician be an experimentalist using [inaudible 00:34:48] of one experiments in front of them. I think the way that I see medicine evolving is that as the knowledge base and the rigor of that knowledge base improves, many of the things that we think of as professional activity today, will actually devolve through primary care and, to be honest, into the community. There are many things where the rigor of the underlying [inaudible 00:35:12] are as such that there's no reason for a licensed provider to be involved. We allow our patients to install their own wireless networks without a technician. I'm sure most of them could look after their own lipids pretty effectively if they were given the right information.                                            So, a lot of stuff will begin to move in that direction. And as that happens, I think the way in which information is displayed, the way in which data are collected, and the workflow around integrating information will change. That doesn't get past the point that you brought up, which is that that will probably take a couple of decades, and in the interim, I think people are going to end up training in modules of subspecialty, but I think one of the things that I sometimes like to ask myself is what's the end game? Where is this going to end up? And can we build systems that train directly for that end game, rather than going through these intermediate steps. I think that's something where I think we tried, in the short piece that we wrote in Circulation, to argue that everybody should have some exposure, and that that exposure can change over time. We should be equipping people, not to know genomics, but to be able to learn how genomics is impacting their patients for the next 50 years.                                            That model of professional training is actually the one that really was the dominant model until maybe 100 years ago. And then, for reasons that don't quite seem obvious to me at least at the moment, we sort of tended to slowly move to more of a learned knowledge base that was then applied. Physicians sort of steadily got to the point where we're now data entry clerks. The actual amount of professional and intellectual engagement has, I think, slowly diminished in many medical subspecialties and medical specialties.                                            The opportunity that genomics and other advancements in technology in medicine bring is the chance to, I think, reprofessionalize ourselves to move from just simply defining ourselves in terms of the knowledge base that we each bring to the table, but defining ourselves rather in terms of how we put the knowledge together around individual problems and individual patients. It's a very much more patient-centered biological approach than perhaps we've had over the last couple of decades.                                            I think these are ... I'm obviously stating a lot of this somewhat in extremes, but I think that these are general trends that you see in medicine. They've happened in other fields as well, and people have overcome them. It's usually a function of changing the workflow itself, of changing the way in which the information ends up in the professional's hands and how you collect the data that you use, then, to interpret the existing knowledge. That, I believe, we haven't really reworked probably since Ozler's time.                                            It is amazing that we still have workflow ... I mean, it's amazing in lots of ways. It's an amazing tradition, but it is quite interesting that we still have workflow that is probably largely dependent on what Ozler liked to do when he was growing up in terms of the times of day that he got up and his workflow. That's sort of instantiated in many ways in everything that we do. Nothing entirely wrong with it, but there's a lot happened since then that we haven't really changed. Medicine is not yet, in many instances, a 24/7 profession, and yet most other things that have much less in the way of impact on society, are already 24/7 professions in many settings.                                            So, I think you're going to see a lot of demographic changes in medicine that come from the advent of technology and other industries. And I think those will all transform the way that we imagine training in medicine, along the same sort of timeline as some of the traditional approaches that you described, building out a training module and then having a subgroup of people do a six-month or a year of extra training. I see that as a short-term solution. I think, ultimately, longer term solutions are changing the whole workflow of medicine. Dr Anwar Chahal:            What have you done in your own program at the Brigham to introduce genomic medicine training for fellows? Dr Calum MacRae:          We are building out ... Obviously we have a fairly large cardiovascular genetics clinic. I think probably the largest in the world. We have now seven, soon to be eight, providers working only and wholly in cardiovascular genetics. We therefore have the ability to have our fellows rotate through our genetics clinic. We have inpatient and outpatient genetics services. And we also, obviously, involve our fellows in a lot of the academic pursuits going on in both our genetics and genomics programs in the cardiovascular clinic.                                            As we do, our colleagues are no longer in training. We have regular, in our clinical conference slot, we have, several times a year, a genetics component. And then, what we have also, is an integrated training program with clinicians and pathologists that is really bringing the individuals who are understanding the technical aspects of the genetic testing with the individuals who are learning and understanding the clinical aspects of that testing. And so, we imagine over time that this will evolve into potentially the type of specialist module that you described. But also, into a fixture that goes all the way through our two-year clinical training program.                                            We've sort of taken the point of view that we probably need to do a bit of both. We need to, given what I've said in the last few minutes, that we need to take a thread that recognizes a short term and intermediate term need for specialization, but also recognizes that we have to equip every one of our trainees, and every one of our physicians with the ability to begin to learn the underlying sides of genomics, and the underlying approaches to using genomics in every aspect of clinical cardiology. And so, we're doing both of those things, and have active efforts in both. Dr Anwar Chahal:            You mentioned integration with pathologists, but for our colleagues who are not clinicians, what about the research angle, and the scientists, when they're in training? Is that integrated so that we are getting this meeting of minds that is essential? Dr Calum MacRae:          Absolutely. In fact we, thanks to a variety of efforts at Brigham Women's, we have now at least three separate venues in which this occurs. I mentioned cardiovascular genetics clinic. We also have a genomic medicine clinic, which I'm one of the clinical co-directors for, where we actually have cases that come through routine clinical care that seem as if they would benefit from whole genome or whole axiom sequencing. And then we have a weekly conference that's actually led by Dick Maas and Shamil Sunyaev, two of our genetics colleagues, and taped in specialists from Althrop Medicine as well as scientists from the entire Harvard Medical School environment. So we bring everybody together around mechanistically solving individual clinical cases.                                            And then the third venue is one that's part of a national network, the Undiagnosed Diseases Network. We are one of the sites on the national NIH-funded UDN network. And there again, one of the themes is identifying individuals or families who would benefit from both rigorous genomic analyses as well as much deeper phenotyping. That's been a program that I think has been very exciting, and one that we, again, have learned a huge amount from in terms of how do you begin to build the infrastructure that brings, not just the fresh clinician to see the patient, but somebody who ... A whole team of people, who understand and can evaluate all the biological aspects that are relevant in that patient.                                            It also brings to bear the scientific expertise that you might need in order to make a mechanistic connection between genotype and phenotype in that one individual. And some of that involves animal remodeling. In cancer, for example, there's a concept that has emerged over the last two to three years of what's called co-clinical modeling. Once you've identified some of the genomic features, it allows you to begin to model in an animal, in parallel with the trajectory of the patient, and individual [crosstalk 00:44:54]- Dr Anwar Chahal:            As some people call them. Dr Calum MacRae:          Exactly. Creating an avatar. And in many instances, that's an avatar that includes multiple different disease models. We have begun to do that in the cardiovascular space. I think, obviously it's early days yet, but I think there are lessons to be learned about how you build the types of infrastructure that allow people to move beyond this state where a patient's outcome is dependent on him seeing the right doctor, on the right day, at the right time.                                            There are actually systems that funnel the patients into the right venue based on objective criteria at every stage. I think that's the type of reorganization, re imagination of the medical system that we need. We sort of duplicate things in lots of different areas, and you're still dependent on hitting the right specialist, on the right day, at the right time. Or not seeing a specialist. Seeing a generalist on the right day, at the right time, who is able to put everything together. Or even hitting somebody who has the time to listen to your story in a way that helps you identify the exposure or the genetic basis of your condition.                                            If we recreate the professional environment that I talked about earlier, I think in ways that are both traditional and novel at the same time, I think we will do ourselves a great service and build a platform that lets all of the technologies, including genomics that we've talked about today, begin to impact patients in a real way on a regular basis. Dr Anwar Chahal:            Thank you for that. One question I think is important to look at from the other side, you've gone from One Brave Idea to one revolution in medicine if I can be so bold. You mentioned so many other services are 24/7. You give an example, you can book your hotel in Shanghai sat in the Midwest, and you can change your booking on an app on a phone, and yet in medicine, it's so difficult to arrange an appointment. We have resisted that 24/7 service, aside from the acutes. But for the sort of chronic workload that we have, the 24/7 model has been resisted. What do you think are some of the challenges? Because I can almost hear members of our profession saying, "Well, who wants a 24/7 service and who wants to provide that 24/7 service?", and is it always necessary to have that 24/7 service?                                            As you say, so many things, such as hypertension treatment, you mentioned lipid management, could actually be done reasonably well by patients who are well trained. And certainly in heart failure, you can teach patients to take their Furosemide or their Lasix by weighing themselves and adjusting it, and can do it relatively well, and relatively safely. What do you think are the challenges to get the profession to realize that this is what's going to happen, and they've got to get on board? Dr Calum MacRae:          Well, I don't think you want to make it somehow mandatory. I think there are elements. Every patient is different. I think that's something we've used as a chivalrous for many decades as a profession. The reality is that we don't do very well. It takes, from the time a medication hits the guidelines, not the trials are finished, but the time that it gets accepted into the guidelines, let's say as a Class I recommendation. The average time to reaching equilibrium in the population is 12 to 15 years in cardiovascular disease. So you'd hate to be the person who got that drug in the 11th year, if you actually end up having your event in year three or four. And yet you can upgrade software for your phone, and hundreds of millions people upgrade it in the first couple of days after a release.                                            So, we have to build systems that allow us to be as efficient as every other element of our lives, and yet don't, in any way, diminish the importance of the personal interaction, the healing interaction that comes from a patient provider encounter. I think we do ourselves a disservice if we just imagine everything in exactly the same way as it's always been. A lot of it just requires us to make relatively modest changes to the types of things that we do, and to cede some control over some elements of it.                                            People are not dependent on making cyclical appointments to have doses of drugs tritrated. But once we've identified that a drug needs to be on board as a result of a primary indication, that we allow the titration to take place in an efficient and cost-effective manner. I think a lot of what we do is driven by how we get paid. A lot of ... And that's not criticism, it's natural in every single profession on the planet. You do things the way that the system is set up to have them be done.                                            And so, I think with relatively little in the way of systems engineering, you can have a 24/7 system without having 24/7 physicians. There are some areas, obviously intensive care units, where you do have 24/7 coverage already, but people are so used to having asynchronous care that being able to literally come home after a night shift and make their reservation for a restaurant the following evening, on their phone, often on another continent, it is a little bit strange that we literally can't book patients into your own clinic without calling up a couple of people.                                            I just think that some of this is resistance for resistance's sake. Some of it is people actually simply restating the things that we all believe are important parts of medical encounters. I think we just have to be creative about how we move from here to there. I think the thing that I find perhaps most interesting is that somehow the creativity of physicians is not fully exploited. We haven't really asked doctors and patients to come up with new approaches to how care is delivered, to how patients are seen. But I think if we allowed venues where that could happen, that would be actually the way in which we would evolve a very different system.                                            I think some of that, as I said, just goes back to the way in which everything is structured. All of the payment models, all of the ... Even the types of places that we see patients, are very much anchored in history. They're legacy items and there are lots of reasons why that's the case. Medicine, you can't show up with a minimally viable product. You need something that works perfectly day one, because of the liability. And so, what we need are just to rethink the way in which we even move medicine forward. What we know we can't do is just keep doing what we're doing, and changing modestly, rearrange the deck chairs.                                            What we need to actually be able to do is find places where we can actually, or venues where we can change things and test new models of care in a relatively low risk situation. I think you already see lots of payers, the federal government, and the NIH all thinking about how you can do that. Some of the [inaudible 00:52:55] efforts, some of the ... Even the NHGRI efforts in genomics. One of the nice things about genomics is because it's a new tool, it allows you to reinvent the way in which medicine is delivered. And so, I believe things as diverse as the precision medicine initiative, and as some of the most fundamental ways in which NIH funding is being restructured, will all potentially impact the way in which creativity and innovation start to evolve within the healthcare system.                                            I don't want to sound revolutionary. We're all doing all of this, all of the time. It's just not structured in a way that seems to very efficiently reach reduction to practice across the entire medical ecosystem. Part of what I think we need to do is, as a profession, build better ways of identifying where the innovation is occurring, and I will tell you I think it's occurring almost evenly across the entire medical universe, it's just that it doesn't propagate. All medicine, at the moment, is quite local. I think the things that you start to see happening in the industry that will change it are the fact that medicine is becoming much more like every other area of endeavor. It's becoming linked by technology. And once information flows more efficiently, I think a lot of the things that sound as if they're revolutionary, will end up actually just seeming like a series of obvious conclusions, based on the information that we've gleaned from early outlets or success stories.                                            Many of the things that I've mentioned today, they're not revolutionary at all. There are entire healthcare systems that use these approaches. But they just haven't become generalized because of the way that medicine works. And so, I think that's one of the reasons that I'm a believer that technology in particular will have a transformative effect, just on the way that doctors talk to other doctors or relate to their patients, and the way in which creativity and innovation propagate through the medical system will change very rapidly as a result of that.                                            And that's one of the great benefits of the electronic health record. I don't think EHR's now are perfect. In fact, in many ways, they're where other industries were 15 or 20 years ago. The supply chain in many large retail organizations was much more sophisticated in the mid-80s than the average EHR is. But what they've done is begin to collect the data in the right place, and in the right way, in a structured format. But as technology begins to cut across different EHR's and across different healthcare network, you'll see things, synergies begin to emerge that will accelerate the pace of change.                                            It's not by chance alone that medicine has attracted different types of people over the last 50 or 100 years. I think they'll just see the types of individuals that come to medicine be more diverse and more distinctive, and that also I think will help. More distinctive in their skillset, and that will help accelerate change in ways that again, will seem far from revolutionary fairly quickly. Dr Anwar Chahal:            Thank you for that. I wanted to come to the last section of the podcast, and sort of back to where I said it was joking, and you said I wasn't joking about doing three residencies. So, could you tell us a little bit about your own training and your own path? Originally from Scotland, through to London, and then over to the U.S.                                            And also, if you could share some of those pearls that you've picked up that aren't obvious to us in books, or sometimes are so obvious that they're elusive and not always apparent to young, up and coming trainees, both on the research side as well as the clinical. Dr Calum MacRae:          Yeah, sure. I trained in [inaudible 00:57:15] which had I think a very healthy attitude to specialism and generalism, and the relationship between them, and instilled in all of the specialists the need to always maintain some general medical capability. To this day, I still intend on general medicine for that reason.                                            I then moved, I did cardiology training in London, and was fortunate to work in a couple of hospitals, one of which had a very interesting, I supposed, quaternary care clinic which had extremely complicated patients. That's where I did my second internship, at the Ross Graduate Medical School in Hammersmith. And everybody who was an intern in that setting had already basically been board certified in internal medicine, so they'd all finished their medical training, come back to do an internship in that setting.                                            And there, I saw some amazing cases. There was an entire service for carcinoids, there was an entire service for many rare and wonderful diseases. At that point, you began to see how super-specialist knowledge can be incredibly helpful. But it can also be restrictive if it's not applied in the right way.                                            And then I did cardiology training at St. George's Hospital in London with some amazing mentors. John Camm, who many people will know from his work in atrial fibrillation and sudden death. David Warr, another very well known electrophysiologist, one of the early pioneers. Bill McKenna was my primary mentor, and he was somebody who had worked on the very earliest descriptions of hypertrophic cardiomyopathy when he had originally been at the Hammersmith, and then moved to St. George's.                                            He taught me a lot about, well many things. First of all, focus in your career, understanding the skillsets that you needed to accumulate in order to a) build a distinctive portfolio and b) to maintain your relevance by accumulating new skillsets as you move forward. And he had actually established a collaboration with Simon's. That was one of the reasons that I ended up moving to the U.S., and had a fantastic time with John and Cricket, at one of the earliest times in genetics moving into cardiovascular disease.                                            I learned a huge amount from colleagues, at that stage, both at the bench. Hugh Watkins is now chair of cardiology and lecturer of medicine now in Oxford, was a bay mate who was there a couple of years ahead of me and I learned a huge amount from him. I realized ... My wife is from New York City, from Long Island rather, and I realized I had to probably stay in the U.S. for those reasons, and I retrained at that stage in internal medicine again at the Brigham where mentors such as Marshall Wolf and, actually cardiology mentors at that stage were people like Punky Mudge and Pat O'Gara, who then helped me to adapt to the U.S. system.                                            The only thing I will tell you is that I don't think I ever learned as much as I did in each of my internships. I think the learning curve is incredibly steep. I'd been out of clinical medicine for four or five years, focusing on the lab, before I went back to my third internship. But I still think it was one of the most amazing experiences, largely because of the fact that you learn from every colleague, and you learn from every patient. I think if you go through most of your life thinking like that, I think you can end up doing very well.                                            Actually, one of the other things that's really important is actually emphasizing those personal connections. The first fellow I had at Brigham and Women's when I was an intern was Joe Hill, who's now the editor of Circulation, the chair of cardiology at UT Southwestern. Almost everybody that I know in cardiovascular medicine, I've encountered in those types of settings. Either in training settings, or in research collaborations, or at research meetings. You just begin to see a whole list of people that have worked together in different ways, and have learned from each other. I think that's one of the most powerful things to take away from research or clinical training.                                            I then was fortunate enough to get the chance to do a second cardiology fellowship at Mass General. There, I went to Mass General actually because of the focus on zebra fish genetics. I realized at that stage to really be able to study things at the scale that I thought was going to be necessary, I needed a high [inaudible 01:02:40] system, and Mark Schwartz, before he went to Novardis, on the zebra fish and the cardiovascular system, was very inspiring and I had a great time there. And then, ended up spending some fantastic years at Mass General where I eventually became the program director. But again, there I learned an incredible amount from people like Bill Dec, from Roman Desanctis, from Dolph Hutter. All of whom had very strong clinical presence, as well as from the researchers. Mark Fishman, the late Ken Bloch, and many others.                                            And then also, perhaps one of the most important people in my long term training was Peter Yurchak, who had been ... He had actually defined, I think, the training programs in U.S. cardiology about 35 years earlier. He had been the program director since its inception in the 50s until he retired in 2005 I think it was. And then I became the program director and was there until I moved back to the Brigham in 2009, and became chief in 2014.                                            I think the trajectory is really, I outline it only to highlight the fact that it took me a long time to get where I was going, but that I spent most of my life enjoying the journey. And I think that's actually one of the most important lessons I took away from it. You can end up finding situations where you feel like you might become frustrated, but in fact, if you go into them with the right attitude, and not only that, if you do it with the right people, you can take a huge amount out of it.                                            I was incredibly fortunate in the fellowship class that I had at Mass General. Mark Sabatine is now the chair of TIMI, Patrick Ellinor, who is the head of EP and a pioneer in atrial fibrillation genetics. Stan Shaw, who is now the chief scientific officer with me in One Brave Idea. Danita Yoerger, who's the head of ECHO, and an outstanding ECHO researcher at Mass General. Mark Rubenstein, who's a very successful cardiologist, and a fabulous clinician. That group of people actually, I think, together helped me realize how much you could take from training no matter how old you are, and no matter how grumpy you seem when you don't get the full nights sleep.                                            In the research side, I think the other thing that was obvious was that so many people bring so many different things to the table in research that you should never over or underestimate any aspect of the entire profession. I think I still get remarkable insights into research questions from colleagues who are clinicians, who've never done any research, just from astute observation and declaring a problem in a way that encourages investigation. I think that's one of the most important elements of training is how do you work out what you need to do, and how do you make sure that everything that you do between the start and the finish of that journey is used to help and to improve the way in which you end up doing what you ultimately find as your sort of settling point in your career.                                            I think the other thing that I will say from the standpoint of research is it's always best to try and think about blending different fields together. What you don't want to do is end up being a clone of one of your supervisors or your mentors. It's really an important thing, and I encourage this in all of our trainees the importance of being a bridge between different disciplines. I think that's something that requires real emphasis.                                            And then, finally, never ever forget that the single most important thing in all of this, whether it's the reorganization of clinical care or the core research environments, is the biology in the patients in front of you. And so, one of the things that I'm particularly and acutely aware of almost every time I see patients is that the patients often know much more about the condition that they have than you ever will. Listening to them is actually very important piece of everything that you do.                                            In fact, one of the reasons that we began to move outside the heart in our heart failure research was talking to patients about their pre-clinical elements that they found in their families. So, often, when you see a family with inherited heart disease, before the gene is identified, before anybody has a phenotype that you recognize, the patients themselves can assess who's likely to develop the disease from their intrinsic knowledge of their siblings, and their cousins, and their other family members.                                            So, for example, one of the families that I've worked on intensely, there's a anxiety disorder that is a much more stable and much more specific part of the phenotype than any of the cardiac arrhythmias, and it's actually turned out to be quite a difficult anxiety disorder to define using even DFM criteria. But when we asked the family, they were very able to tell the people in the family who just were at the normal edge of neurotic from those who truly had the anxiety disorder that co-segregated eventually with the arrhythmia.                                            The lesson I've learned time and time again is that patients always are a vital and central part of the answer. And it's a pride thing to say, but particularly in genetics and genomics, I think, and particularly with the reemphasis on phenotype, that I believe is necessary, I think we do well to try and make sure our research and our clinical care, our discovery, and our disease management are very tightly aligned. And I think technology is one of the ways that will help that happen. That actually is part of what being a professional really is. If you go back to the early professional guilds, that's exactly how they were formed. It was groups of experimentalists who were interested in particular problems that formed the original professions in European cities during the Renaissance. I think that's something that we would do well to think about as we continue to remodel medicine in the 21st century. Dr Anwar Chahal:            Thank you for that. Lots of important points there, and I guess your emphasis that enjoying the journey rather than thinking about the destination, but did you always know where your destination would be? And, in fact, that brings me to another question. Have you actually reached your destination, or is your journey still ongoing? Dr Calum MacRae:          So, exactly. I think that's the key thing. You don't need to necessarily know where you're going to stop. You just need to know where you're headed. That's something I actually tell people as they're interviewing for fellowship or residency, that part of what people are looking for when they talk to you is that you have thought through and organized your life around your goals. And those goals can change. Nobody's going to hold you ...

Jane Ferguson:                Hi everyone. Welcome to episode seven of Getting Personal, -Omics of the Heart. I'm Jane Ferguson, an assistant professor of medicine at Vanderbilt University Medical Center and the chair of the Publications and Professional Education Committee of the Functional Genomics and Translational Biology Council of the American Heart Association.                                            This month I'm particularly excited to announce a new venture. We have teamed up with the journal, Circulation: Cardiovascular Genetics to bring you this and future podcasts. CircGen publishes a lot of the most compelling research in cardiovascular genetics and genomics and precision medicine in cardiometabolic disease. We've already featured a lot of the research in previous episodes of the podcast. With this new collaboration, you can look forward to even more in-depth features of the newest research published in Circulation: Cardiovascular Genetics.                                            This month, Anwar Chahal, a cardiology fellow at the Mayo Clinic, talked to Calum MacRae, the Chief of Cardiology at the Brigham Women's Hospital in Boston. The interview covered a variety of topics and we couldn't fit everything into this half-hour podcast. What you will hear in this episode is a discussion related to a recent publication by Dr. MacRae and colleague Aaron Aday in Circulation published in July 2017 entitled Genomic Medicine in Cardiovascular Fellowship Training. Dr. MacRae expands on the topic, How to Train Clinicians to Deal with Advances in Genomic Medicine and What we Can Do to Improve Implementation of Knowledge from Genetics and Genomics to Helping Patients with Cardiovascular Diseases. If you'd like to hear more from the interview, including discussion of Dr. MacRae's bold One Brave Idea project, and to hear more pearls of wisdom and career advice from Dr. MacRae, you can download the full interview in the special hour-long podcast.                                            First, I do want to highlight one recent paper from the Functional Genomics and Translational Biology Council which was published in the June 2017 issue of Circulation: Cardiovascular Genetics. This clinical genomics paper was published by Laura Zahavich, Sarah Bowdin and Seema Mital, all from the Hospital for Sick Children in Toronto. The paper is entitled Use of Clinical Exome Sequencing in Isolated Congenital Heart Disease and describes the case of an infant with congenital heart disease where a pathogenic mutation in the notch one gene was identified through whole exome sequencing. The paper highlights the utility of whole exome sequencing when candidate gene panels are negative, allowing for increased understanding of causality and the ability to make risk predictions for future offspring. At the same time, this approach reinforces the importance of well-trained clinical personnel including genetic counseling, to appropriately interpret and disseminate findings from whole exome sequencing.                                            A little aside, in case you are not aware of this, Circulation: Cardiovascular Genetics has a really nice feature on their website where you can apply filters to see specific types of publications. From the toolbar along the top of the homepage, you can go to Browse Features and then you can select Options from the dropdown menu, so if you click on Clinical Genomics Cases, for example, you will see all of the genomic case reports, which may be of particular interest to this audience. Dr Anwar Chahal:            My name's Dr. Anwar Chahal. I am a cardiology fellow in training from London, UK, and I am doing my research fellowship here at the Mayo Clinic. I'm very honored and delighted to have our guest, Dr. Calum MacRae, so you are the Chief of Cardiovascular Medicine. You are a MD PhD by training and you are associate professor at Harvard Medical School and your expertise amongst many other things, internal medicine, cardiovascular diseases, but in particular inherited cardiovascular conditions. Is there anything else that you would add to that? Dr Calum MacRae:          I'm a big fan of generalism and I'm quite interested in cardiovascular involvement in systemic disease as well, largely as a means of keeping myself abreast with the biological mechanisms in every system that seems to be relevant to cardiovascular disease. Dr Anwar Chahal:            I think training across the world has increasingly recognized the importance of genetics and genomics, but I just want to share one little anecdote. My wife's a primary care physician and I was visiting the GP practice where she works and she'd mentioned that I had an interest in genetics and genomics and one of the partners came out with one of these reports that a patient had sent their sample to a private company, got this analyzed, brought it into the clinic appointment, and asked for an interpretation. The GP partner said to me, "I've absolutely no idea what any of these numbers, values, et cetera mean. I actually am looking forward to my retirement because I really don't want to have to cover all of this. Can you help me with it?"                                            I sort of remember hearing Dr. [inaudible 00:05:48] talk here at Mayo Clinic, who's really pushed forward pharmacogenomics and he's been arguing for quite some time, as I've heard you say as well, that genomics and genetics is just going to be a part of the medical record in the same way that hemoglobin or a chest x-ray is and people better catch on because it's here, it's available, commercially people can send their samples directly without their doctor's involvement and then it's trying to make sense of all of that. I think as a community research and clinical we have to take this very seriously and I'd be grateful for your insights on that and then if you could then tell us what would be the best way for the up and coming generation and for programs to incorporate that into their training. Dr Calum MacRae:          I think you're right, there is a general tendency in the public domain to test a variety of different genotypes, and in many instances I think the key elements are how do we as a profession conceive of these tests? I think one of the things that we forget perhaps at our peril is that many of these things are problems that we've encountered before. There's a natural cycle of different tests in medicine where they start off in the academic medical centers, they propagate into the periphery, and then eventually they're assimilated as part of internal medicine.                                            I think the scale of genomics is obviously somewhat broader than many individuals have seen in the types of data that they deal with on a day-to-day basis, but I think that's something that's happening in everybody's life, and every aspect of your life, you have many more channels to deal with, you have many more choices in the supermarket to deal with. I don't see this as a sort of existential challenge to medicine; quite the opposite. In my experience, the core things that we need to remember is that DNA is no different from any other assay, except for the fact that it's relatively straightforward to do DNA diagnostics. It's technically not as sensitive a set of biochemical issues as are many other assays that we use in day-to-day clinical practice.                                            The other thing that I think is perhaps a key element is that I said a few minutes ago, it's a [inaudible 00:08:35] dataset and it's stable for your whole life. You only need to have it tested once. It's sort of invert to the typical diagnostic paradigm, so instead of a primary test being interpreted in the context of an ongoing clinical event, the test may have been present for four decades and the results will evolve over time in light of the changing phenotype or some new information in respect to that genotype.                                            What I've actually looked on genomics as is almost an organizing principle for the way that you build care. In fact, I see quite frequently, we probably now have an average one or two new patients a month in my clinic who bring their entire full genome with them, either an exome or a whole genome. We've begun to really get to know quite well how to manage patients. Obviously they're a select group of patients but one of the things that I find is that patients are really quite astute in understanding that genotype and phenotype are not deterministic relationships. What you have to do is always interpret these things in context of a probabilistic understanding. Most patients I think when they're told this understand that we're going to learn much more about genomics going forward than we will ever imagine that we could know at the present.                                            That will involve lots of different things. It will involve new ways of displaying data, new ways of thinking about the data in the clinical context. I actually think one of the most interesting things about genomics and to be honest any assay is that they rarely reach any form of maturity until they are used in the clinic, until they are actually used in implementation. For example, many genetic tests at the moment don't change therapy and they don't change outcomes, but partly that's because they've never been studied in that context, and one of the things that I think [inaudible 00:10:45] has to be really congratulated for is his focus on pharmacogenomics as being one of the early areas in which this will really move forward.                                            I believe that by immersing ourselves in it, by actually trying it within the clinic where we're going to learn much more, and part of that gets back to the original topic that we spoke about, which is phenotype. The only way to really begin to understand collection of phenotype is if you do it in the context of existing genotype I think. As we move into new phenotypic areas, we're not going to be able to test everything and everybody. I think there the genome will end up being an important framework, lifelong framework for the management of a patient's diagnosis, prognostication and then therapy, potentially in that order.                                            I think you need a whole different set of skills, you need a whole different set of technologies, but most importantly you need information that you can interpret in the context of the person in front of you. Until you can make mechanistically important insights with one person, it's going to be very difficult for genomics to really change medical care. That's something I think we should be focusing on. I think we've tended to have an associate of strategy for genetics. We haven't driven it into the clinic. As we drive tests into the clinic, whether it's troponin T or whatever, you begin to understand much better how to use them, although sometimes that can also go in quite extreme directions that you may not necessarily anticipate. I mean, troponin originally was a stratification tool for acute coronary symptoms, and now it's virtually a diagnosis in its own right.                                            I think you'll see that tendency revert over time as people begin to understand the biology of troponin, of isoform switching in peripheral tissues of the way in which troponin may represent very different disease biologies. At the moment, it seems like it's a very simple and straightforward yes/no type of test. There's no such thing in medicine, and I think that's what we're learning about genomics and so instead of conceiving it as a series of 10 to the nine yes/no tests, we're going to end up with a very different vision and view of how it can be implemented to clinical practice. That can only come from having clinicians and geneticists work together on this.                                            In fact, one of the things that we've been doing in the partners environment with some of our colleagues, and I have NIH funding to do this with Heidi [Ream 00:13:31], with Sandy [Aronson 00:13:34] and with Sean Murphy is to think about how we display data, but also how we collect information in light of that genomic data that helps in an iterative way in the learning fashion inform genotype phenotype relationships in a much more probabilistic manner than we have done to date. There are lots of efforts and that's great, that just happens to be one that I'm involved in, but I think it's a generalizable approach that you're going to see moving into the clinic in the next few years.                                            From the standpoint of training, I think what you want to do is get exposure to all types of genetic information so you understand common [alleles 00:14:15], rare alleles, genomics and individual panels and I think the best way of doing that is to have it be part of training programs. In fact, with one of my junior colleagues, Dr. Aaron Aday, we recently wrote a short piece highlighting how important it will be for all of us to come together to think about how do we start to introduce the concepts of genomics into standard clinical training programs, and that's something we're working on fairly avidly at the Brigham and I'm sure there are, I know there are efforts at many other institutions to do similar things. Dr Anwar Chahal:            That article is published in Circulation in July of this year if anybody wants to download that. I think if we talked to clinical trainees and asked them what are their concerns about training, as you know training can be very long in cardiology, which is a procedurally based specialty whether or not you become an invasive proceduralist at the end of it, there is that component at the beginning, and do you think a standard, in the US, a standard three-year program with two years of clinical and one year of research can incorporate that at a sound enough level to allow somebody to practice or do you think we're going to look at increasingly a one-year or a six-month sort of add-on fellowship for those interested more on the inherited side or more on the genomic side?                                            I, like yourself, trained in London and the training programs are longer in the UK. It was probably six years when you were there. It shortened to five, and now increasingly it's going to become six and maybe even more with a general fellowship for five years and then a super advanced fellowship and inherited cardiovascular conditions certainly there has become a module that is encouraged for people to take and then become somewhat certified in inherited cardiovascular conditions. What do you think there in terms of incorporating all of that as well as learning basics of eco and device therapy and catheterization? What are your thoughts there? Dr Calum MacRae:          Again, I look at this as a spectrum. I think there's a trajectory for all of these types of innovation and knowledge and it starts off being super specialized, it goes into a more general location, and then eventually it's an integral part of everybody's clinical practice. I do think that what you're going to see is rather than, and this is already I think the case in many elements of medicine, medicine has already exceeded the knowledge base even when I was training by probably a long order in terms of the complexity and extent of content, not that I trained that long ago.                                            One of the core elements I think that we're seeing is that we need to move medicine from what I believe has become somewhat de-professionalized, say, to one where you're focusing on, not on the actual core knowledge that you bring with you to the table, but actually the way in which you integrate knowledge. I think the focus of training is going to change somewhat. It has had to change in other fields. Medicine I think for a long time favored that sort of single, comprehensive approach in one mind. Medicine is going to become more of a team sport and it's also going to become more of a knowledge integrator profession than it has been for some time.                                            It's interesting, when medicine started there was so little knowledge that you really had to have almost every physician be an experimentalist using [inaudible 00:18:37] experiments in front of them. I think the way that I see medicine evolving is that as the knowledge base and the rigor of that knowledge base improves, many of the things that we think of as professional activity today will actually devolve to primary care, and to be honest into the community. There are many things where the rigor of the underlying data are such that there's no reason for a provider to be involved, for a licensed provider to be involved. We allow our patients to install their own wireless networks without a technician. I'm sure most of them can look after their own lipids pretty effectively if they were given the right information.                                            A lot of stuff will begin to move in that direction. As that happens, I think the way in which information is displayed, the way in which data are collected and the workflow around integrating information will change. That doesn't get past the point that you brought up, which is that that will probably take a couple decades and in the interim, I think people are going to end up training in modules of sub-specialties, but I think one of the things that I sometimes like to ask myself is, "What's the end game? Where is this going to end up? Can we build systems that train directly for that end game rather than going through these intermediate steps?"                                            I think that's something where I think we tried in the short piece that we wrote in Circulation to argue that everybody should have some exposure and that that exposure can change over time. We should be equipping people not to know genomics but to be able to learn how genomics is impacting their patients for the next 50 years. That model of professional training is actually the one that really was the dominant model until maybe a hundred years ago, and then the reasons for it don't quite seem obvious to me, at least at the moment. We sort of tended to slowly move to more of a learned knowledge base that was then applied. Physicians sort of steadily got to the point where we're now data entry clerks. The actual amount of professional and intellectual engagement has I think slowly diminished in many medical sub-specialties and medical specialties.                                            The opportunity that genomics and other advancements in technology in medicine bring is the chance to I think re-professionalize ourselves to move from just simply defining ourselves in terms of the knowledge base that we each bring to the table, but defining ourselves rather in terms of how we put the knowledge together around individual problems and individual patients, a very much more patient-centered, biological approach than perhaps we've had over the last couple of decades. I think these are, I'm obviously stating a lot of this in somewhat in extremes, but I think these are general trends that you see in medicine. They've happened in other fields as well and people have overcome them. It's usually a function of changing the workflow itself, of changing the way in which the information ends up in the professional's hands and how you collect the data that you use then to interpret the existing knowledge.                                            That I believe we haven't really reworked probably since Osler's time. It is amazing that we still have workflow, I mean it's amazing in lots of ways. It's an amazing tradition. It is quite interesting that we still have workflow that's probably largely dependent on what Osler liked to do when he was growing up, in terms of the times of day that he got up and his workflow. That's sort of instantiated in many ways in everything that we do. Nothing entirely wrong with it, but there's a lot happened since then that we haven't really changed. Medicine is not yet in many instances a 24/7 profession, and yet most other things that have much less in the way of impact in society are already 24/7 professions in many settings.                                            I think you're going to see a lot of demographic changes in medicine come from the advent of technology in other industries. I think those will all transform the way that we imagine training in medicine. Along the same sort of timeline as some of the traditional approaches that you described, building out a training module and then having a subgroup of people do six months or a year of extra training. I see that as a short-term solution. I think ultimately longer term solutions are changing the whole workflow of medicine. Dr Anwar Chahal:            What have you done in your own program at the Brigham to introduce genomic medicine training for fellows? Dr Calum MacRae:          We are building out, obviously we have a fairly large cardiovascular genetics clinic, I think probably the largest in the world. We have now seven, soon to be eight providers working only and wholly in cardiovascular genetics. We therefore have the ability to have our fellows rotate through our genetics clinic. We have in-patient and out-patient genetic services and we also obviously involve our fellows in a lot of the academic pursuits going on in both our genetics and genomics programs in the cardiovascular clinics, as we do our colleagues who are no longer in training. We have regular, in our clinical conference slot we have several times a year, we have a genetics component, and then what we have also is an integrated training program with clinicians and pathologists that is really bringing the individuals who are understanding the technical aspects of the genetic testing with the individual sort of learning and understanding the clinical aspects of that testing.                                            We imagine over time that this will evolve into potentially the type of specialist module that you described but also into a fixture that goes all the way through our two-year clinical training program. We've sort of taken the point of view that we probably need to do a bit of both. We need to, given what I said in the last few minutes, that we need to take a thread that recognizes a short-term and intermediate term need for specialization but also recognizes that we have to equip every one of our trainees and every one of our physicians with the ability to begin to learn the underlying science of genomics and the underlying approaches to using genomics in every aspect of clinical cardiology. We're doing both of those things and have active efforts in both. Dr Anwar Chahal:            You mentioned integration with pathologists but for our colleagues who are not clinicians, what about the research angle and the scientists when they're in training, is that integrated so that we are getting this meeting of minds that is essential? Dr Calum MacRae:          Absolutely, in fact we, thanks to a variety of efforts at Brigham Women's we have now at least three separate venues in which this occurs. I mentioned cardiovascular genetic clinic. We also have a genomic medicine clinic, which I'm one of the clinical codirectors for, where we actually have cases of [inaudible 00:26:45] through routine clinical care that seems as if they would benefit from whole genome or whole exome sequencing, and then we have a weekly conference that's actually led by Dick [Mass 00:26:58] and Shamil [Sonaya 00:27:01], two of our genetics colleagues and takes in specialists from all throughout medicine as well as scientists from the entire Harvard Medical School environment, and so we bring everybody together around mechanistically solving individual clinical cases.                                            The third venue is one that's part of a national network, the undiagnosed diseases network. We're one of the sites on the national, the NIH-funded UDN network. There again one of the themes is identifying individuals or families who would benefit from both rigorous genomic analyses as well as much deeper phenotyping. That's been a program that I think has been very exciting and one that we again have learned a huge amount from in terms of how do you begin to build the infrastructure that brings not just the first clinician to see the patient, but somebody who, a whole team of people who understand and can evaluate all the biological aspects that are relevant in that patient. Then also brings to bear the scientific expertise that you might need in order to make a mechanistic connection between genotype and phenotype in that one individual, and some of that involves animal modeling.                                            In cancer for example there's a concept that has emerged over the last two to three years of what's called co-clinical modeling that once you've identified some of the genomic features it allows you to begin to model in an animal in parallel with the trajectory of a patient- Dr Anwar Chahal:            [inaudible 00:28:40] as some people call them. Dr Calum MacRae:          Exactly. Creating an avatar. In many instances that's an avatar that includes multiple different disease models. We've begun to do that in the cardiovascular space. I think obviously it's early days yet, but I think there are lessons to be learned about how you build the types of infrastructure that allow people to move beyond this state where a patient's outcome is dependent on him seeing the right doctor on the right day at the right time. There are actually systems that funnel the patients into the right venue based on objective criteria at every stage. I think that's the type of re-organization, re-imagination of the medical system that we need.                                            We sort of duplicate things in lots of different areas and you're still dependent on hitting the right specialist at the right day at the right time, or not seeing a specialist, seeing a generalist on the right day at the right time, who's able to put everything together, or even hitting somebody who has the time to listen to your story in a way that helps you identify the exposure or the genetic basis of your condition. If we recreate the professional environment that I talked about earlier, I think, in ways that are both traditional and novel at the same time, I think we'll do ourselves a great service and build a platform that lets all of the technologies, including genomics that we've talked about today, begin to impact patients in a real way on a regular basis. Dr Anwar Chahal:            Thank you for that Dr. Calum MacRae for giving up your precious time and sharing your thoughts and insights and experience. Dr Calum MacRae:          Thank you for your time and I've enjoyed talking to you. Dr Anwar Chahal:            Thank you Dr. MacRae.

Jane Ferguson:                Hi, everyone. Welcome to episode six of our podcast. I'm Jane Ferguson, the current chair of the Professional Education and Publications Committee of the Functional Genomics and Translational Biology Council of the American Heart Association. It's July as we're recording this, so hopefully all you listeners in the Northern Hemisphere are enjoying the summer and taking a break to catch up on your podcast queue, maybe while relaxing at the beach or while navigating the twists and turns of the airport security line.                                            In honor of summer, we're doing something a little different this month and featuring a bite-sized podcast with some research about how your vacation plans might be affecting your heart disease risk. For all our friends in the Southern Hemisphere, I'm sorry that this may be less relevant to you right now, but hopefully you're having a nice winter and enjoying the ability to go outside without sweating. On to our topic, let's talk about the defining feature of summer, sunlight.                                            Humans synthesize vitamin D in response to sun exposure, and vitamin D deficiency can be associated with multiple adverse health consequences, particularly on bone health. However, there have also been reports of association between vitamin D and cardiovascular health. Prompted by observations that cardiovascular events peak during winter months and follow a geographical gradient with higher event rates at higher latitudes, the hypothesis was put forward in the early 1980s that CVD events are mediated by UV exposure through modulation of vitamin D status.                                            This has been supported by a number of different strands of evidence. Large-scale meta-analyses of population data have found that low levels of circulating vitamin D, as estimated from measurements of serum 25-hydroxy vitamin D, are associated with increased risk of all-cause mortality and with increased risk of cardiovascular events and mortality.                                            As summarized in an article from earlier this month in PLOS ONE, by Lars Rejnmark and Rolph Jorde, meta-analyses of randomized clinical trials have found a beneficial effect of vitamin D supplementation on blood pressure, depression, respiratory tract infections, and mortality. However, most find no beneficial effects, including no effects on CVD or diabetes. Some key limitations of these studies were that they often included a relatively small number of subjects, were conducted in individuals who were not vitamin D deficient, or used relatively low levels of vitamin D supplementation.                                            What was lacking in the field until recently was a large-scale, randomized trial to definitively address whether increasing vitamin D levels in the general population would have a protective effect on cardiovascular health. The results of such a large-scale clinical trial of vitamin D supplementation were recently published in the June 2017 issue of JAMA Cardiology. The first and last authors were Robert Scraggs from the University of Auckland and Carlos Camargo from Harvard Medical School.                                            They recruited over 5,000 individuals aged 50 to 84 for monthly supplementation with a hundred thousand international units of vitamin D compared with placebo control. This dose is sufficient to maintain serum 25-hydroxy vitamin D above 35 nanograms per mil. The study was continued for around three years, and events were ascertained from ICD-10 codes. While baseline 25-hydroxy vitamin D levels were inversely associated with CVD risk during follow-up, there was no significant difference in CVD events between the supplementation and placebo group.                                            There were some limitations to this study, including a lower than expected event rate, a median follow-up time of only 3.3 years, and the study was not powered to analyze effects in subgroups of individuals with vitamin D deficiency. However, overall, this study adds to the evidence against a benefit for large-scale vitamin D supplementation.                                            Another recent clinical trial of vitamin D and calcium supplementation published in JAMA in March of this year by Joan Lappe and Sharon McDonnell found no statistically significant effect on cancer incidents in a four-year, double-blind, placebo-controlled, population-based, randomized clinical trial in over 2,000 healthy, post-menopausal women, although there did appear to be a nonsignificant trend towards lower incidents of cancers in the supplemented group.                                            Gina Kolata of The New York Times wrote a feature on vitamin D back in April of this year highlighting the recommendation to use a cutoff of 30 nanograms per mil to define low vitamin D status has resulted in large numbers of individuals being designated as vitamin D deficient. While levels below 30 nanograms per mil have previously been shown to be associated with diverse adverse health outcomes, causal inference, or evidence for a protective effect of supplementation, remains lacking. Particularly in light of the recent clinical trials showing null effects of vitamin D supplementation, the benefits of increasing serum 25-hydroxy vitamin D through supplementation remain unclear.                                            There may be an important role for genetics in dissecting the link between vitamin D and outcomes. As reviewed in the British Journal of Cancer in March of this year by Peter Vaughan-Shaw and Lina Zgaga, genetic polymorphisms affecting vitamin D metabolism are associated with cancer outcomes. It is possible that vitamin D supplementation may have a protective effect only in individuals with a particular genotype. However, this remains to be tested.                                            However, what none of these studies manages to resolve is whether sun exposure itself has any benefits. Perhaps there is something specific about the process of making vitamin D directly from UV exposure that confers protection. Or, perhaps there are other benefits of direct exposure to sunlight independent of the vitamin D synthetic pathway that we do not yet fully understand. Either way, enjoying a little time in the sun this summer may have some benefits, unless you get sunburned. So, please take advice from the dermatologists and avoid prolonged exposure, seek shade from the midday sun, cover up, and use sunscreen.               Thanks for listening to this bite-sized episode. As always, the links to the papers featured in this episode are posted on fgtbcouncil.wordpress.com. We'll be back with more next month.

Jane:                                  Hi everyone, welcome to episode five of Getting Personal: A Mix of the Harsh. I'm Jane Ferguson an assistant professor at Vanderbilt University Medical Center and chair of the FGTB Professional Education and Publications Committee. This month we start off by discussing a topic that isn't strictly scientific but may have just as big of an impact on your career as your science, mentoring. I'll talk to Anna Pilbrow from the FGTB Early Career Committee on how you can find the right mentor for you. And if you've been around long enough that you know longer need any more mentoring, keep listening as we would love you to sign up to become a mentor and share your wisdom with the next generation. Then I'll talk to Naveen Pereira from Mayo Clinic, about some of the papers we've been reading this month.                                            So I'm here with Anna Pilbrow, who is a member of the FGTB Early Career Committee. So welcome Anna, and could you take a moment to introduce yourself? Anna Pilbrow:                  Thanks Jane sure. So I'm a senior research fellow at the Christchurch Heart Institute at the University of Otago in Christchurch, New Zealand, and I'm really interested in trying to understand the mechanisms underlying inherited susceptibility to heart disease and also trying to find new biomarkers that predict incident cardiovascular events in asymptomatic people. And it's those interests really that lead me to the FGTB Council and to becoming involved in the Early Career Committee. Jane:                                  Yeah that sounds really interesting and as part of your involvement in the FGTB Council I know you've been doing a lot of outreach and today we're here to talk about mentoring. So mentorship is something that I think we all recognize is really crucially important but sometimes the mentor-mentee relationship can fall short of people's expectations, it can subject both mentees and mentors to a lot of frustration. So is this something that you've been hearing from Early Career members? Anna Pilbrow:                  Yes, yes. Unfortunately, stories of mentoring relationships going wrong is something we hear all too often. I should stress that there are many wonderful mentors out there, but there are also plenty of empty mentors and that's something that we all need to be aware of. Jane:                                  Absolutely. You know this podcast is focused mostly on personalized medicine, but I think personalization and precision are things that we also need to apply to our careers. So I've heard the excellent advice that you should seek out multiple mentors, really as many as you can handle. I think most of us would never say, "I already have a friend. I don't need another one." You know but some people think that, "Oh I have a mentor, I'm good." But, I think even if you have a supervisor who does act as your mentor, which not all supervisors do also act as mentors, but even in that case I think it's still important to try to find other mentors who can offer different perspectives.                                            You know even, very wise senior mentors are limited by their own experiences and their own implicit biases, they can never give you everything that you need and I think they shouldn't. That's not what a mentor should do. So in an ideal scenario, people I think would build up their own personalized network of mentors spread across different locations, who can each offer something unique on an as-needed basis. But, as a junior person it can be really intimidating to go up to someone and just ask them to mentor you, which I think it can be sort of like asking a complete stranger to be your new best friend, which isn't always the most comfortable interaction. But, are there any ways to make that process easier? So say you're early in your career and you'd like to find a mentor, how would you go about doing that? Anna Pilbrow:                  Oh gosh, that's a great question. I completely agree with all you've said and this is exactly why the Early Career Committee has initiated the FGTB Mentoring Program. So as an Early Career member, all you have to do is sign up and we'll do the rest. Jane:                                  Well that sounds fantastic. Can you explain more about how that process works and what's required? Anna Pilbrow:                  Sure. So the aim of the FGTB Mentoring Program is to connect Early Career members within the council to a senior or a peer mentor also from within the council, and the senior and peer mentors will have expertise in the field of functional genomics and translational biology, and they'll also have expertise in the area that the individual want mentoring in. So if you're looking for a mentor, or you'd like to be a mentor, all you need to do is fill out a short form on our website. The full web address is really long and I actually find it easiest to get the add simply by Googling AHA FGTB Mentoring Program, or you can go to the FGTB council webpage, click on the early career tab and if you scroll all the way down to the bottom you'll get to a link that will take you to the Mentoring Program page. And so once you're on that page, you'll find the links to the mentee and mentor application pages.                                            And one tip I have for Early Career members looking for a mentor, is to think quite carefully about the kind of mentoring that they want. Is it particular aspects of their career development? Things like grant writing, maybe applying for their first job? Or is it more technical things to do with a particular experimental design or something like that? And Early Career members can be really specific when they fill out that form on the website, that will help us match them with the best mentor that has the expertise in that field.                                            And so once it's set up a match between a mentee and a mentor, what we would typically expect is that the mentoring relationship would last between sort of four to six months, and during that time we'd expect there to be regular contact between the mentor and the mentee, and that can be either by phone or email or some other electronic communication. Ideally, we'd really like the mentor and the mentee to make face-to-face at least one time during that four to six month period. So, a great way to do that is to meet up at a conference such as AHA Scientific Sessions and once the four to six months is completed, then we ask the mentor and the mentee to complete a short exit survey so we can get some feedback on how things have gone. I must say, I've been delighted with the positive feedback we've received so far with the program. Jane:                                  That sounds really really great. So who do you think could benefit from taking part in this program? Anna Pilbrow:                  Everybody. Absolutely everybody. So the advantage of this program is that mentees are individually matched with their mentors so that each match should uniquely address the requirements of that mentee. And so, because you can enter exactly what you're looking for when you sign up so you have the best chance of being matched with someone who can help. So one particularly unique aspect is that we have peer mentors as well as the senior mentors, and sometimes you know someone who's just a little bit ahead of where you are now can actually offer you really valuable advice and give a really neat perspective you know compared with somebody who is many years or decades ahead of you in their career. And the other thing I'd mention is that to encourage FGTB members who live outside of the U.S. to also apply to this program. So I'm based in New Zealand and sometimes that makes me feel a little bit isolated compared with colleagues in other countries around the world, and this program really is a great way you know for everybody to expand [inaudible 00:08:04] and become engaged with the council no matter where you are. Jane:                                  I think that's a really great point and it sounds like a great program. So the peer mentoring thing is interesting to me as well. How would you know if you should sign up as a mentee or a peer mentor? Anna Pilbrow:                  Oh that very much depends on what you need, and it's important to remember that you can actually do both. So, if you're still early in your career, you can still offer something to people who are just behind you and would really love to have you become a peer mentor. But that doesn't also mean that you can't be a mentee yourself and be matched with a senior mentor of your own. Jane:                                  So is there a limit to how many mentors you can be matched with through the program? Anna Pilbrow:                  Absolutely not. So we ask that mentors and mentees try the relationship out for four to six months and to try a face-to-face meeting at AHA Sessions in November for example or some other time. And if that doesn't work out or if you just want another mentor, you can sign up again the following year. So there's absolutely no limit to how many times you can sign up and additionally, if you're looking for mentorship in several distinct areas and need a few mentors simultaneously, that's fine as well. So just let us know that when you sign up and we'll try to find appropriate mentors for you. Jane:                                  That sounds great. So I know registration for the AHA Sessions in November just opened up, but people are probably just starting to plan their trips. So what can they do now? Anna Pilbrow:                  Right. It's a great time to start thinking about this. So if you're early in your career and you want general mentorship on navigating AHA Sessions or career planning or if you are looking for specific mentorship in a given topic area, sign up now to become a mentee. And if you're further along in your career and you've developed expertise that could be useful to others, it'd be great for people to sign up as a mentor. So, make sure that you thinking about Sessions coming up, make sure that you schedule time to meet with your mentor or mentee during the meeting. And also, this is a great time to also sign up and plan to attend the early career day which is held the day before the main meeting on Saturday November 10. Jane:                                  So if when people have signed up, do you have any advice for people who are going through the program? Anna Pilbrow:                  That's a great question. So, for both mentors and mentees it's really important to communicate throughout the process but particularly at the beginning to set expectations. So talk about how often you plan to meet, whether it's going to be by email, phone or in person, and be very clear about what you hope to gain from the relationship. And also, be nice. Like mentors tend to be really busy people so if your mentor doesn't respond to you right away, it's probably because they have a grant deadline or a pile of reviews to get through or a manuscript or back to back meetings or family things are going on. And we've all been in that position starting out, so having lots of questions and not knowing where to start this is all part of the normal process of being a mentee. So mentors need to keep that in mind as well and meet the mentee where they are. I guess it's all about being respectful of that relationship and being very clear about what you want to achieve. Jane:                                  I think that's fantastic advice and of course, as members of the FGTB Council we can assure all prospective mentees that really everybody on the council is already very nice so we think that you won't have any problems being matched with some great mentees, great mentors and we really encourage people to sign up for this program. It's really valuable, you have nothing to lose and lot of potential things to gain from being part of this. So thank you so much for joining us Anna. Anna Pilbrow:                  Thank you very much. Jane:                                  Hi Naveen, how are you doing? Naveen Pereira:              I'm doing well Jane. You know, I was reading "Circulation: Cardiovascular Genetics" and in the April issue of this year, there's a manuscript titled Non-familial Hypertrophic Cardiomyopathy: Prevalence, Natural History and Clinical Implications. The senior author on this paper is Chris Semsarian and he's from Australia. And essentially this manuscript highlights the fact that hypertrophic cardiomyopathy, which in large part is thought to be inherited, is also not inherited and perhaps it's important to differentiate the two phenotypes. And so they studied 413 patients coming to their clinic with hypertrophic cardiomyopathy and they found that 61% of these patients had no familial history and 40% of these patients had no sarcomeric mutations. And so, they deemed these patients to be a form of non-familial hypertrophic cardiomyopathy. These were older patients, males, patients with a history of hypertension and a non-asymmetrical septal morphology. What is important is that these non-familial type of hypertrophic cardiomyopathy patients usually have disease onset at the later stage in life and they also have less severe disease.                                            So, I think when we try and prognosticate these patients and aim certain medical therapies towards these patients, we've got to consider whether they're familial or non-familial. And this work has also been highlighted before in the form of an article by Mike Ackerman and his group in Mayo, where they looked at the yield of genetic testing in hypertrophic cardiomyopathy. And essentially patients who are younger at the time of diagnosis, age less or equal to 45 years, patients who have severe left ventricle wall thickening greater or equal to 2 cm and patients who have familial history hypertrophic cardiomyopathy are more likely to have sarcomeric mutations than those who don't.                                            So, both these papers kind of highlight the fact that we got to start thinking of hypertrophic cardiomyopathy as familial or inherited, or non-familial or non-inherited, because initially people thought, "Well you know, maybe we are missing mutations," but with whole genome sequencing, whole exome sequencing these patients with the non-familial hypertrophic cardiomyopathy tend not to have causative mutations. So I really wonder if it's a different disease entity from a molecular perspective. Jane:                                  Yeah, that's really interesting and sort of raises the question of, "Is this non-genetic? Are there other genes? Is this sort of a multi-genic, poly-genic phenomenon where you know the just sort of whole exome sequencing or whole genome sequencing may not be able to identify the causal genes in a lot of these cases?" It's really interesting. Naveen Pereira:              Right. And then, you know there was this other paper in Journal of American College of Cardiology that was published again in the April 4th issue, 2017, and it's titled "Autosomal Recessive Cardiomyopathy Presenting as Acute Myocarditis." And the senior authors are Bonnet, Gelb and Casanova. They shared equally senior authorship. And really, this paper addresses the issue as to why some children are predisposed to acute viral myocarditis, which can present fairly fulminantly, while some children don't despite a lot of kids having viral infections.                                            And so, they tested the hypothesis that perhaps genetic variation in Toll-like Receptor 3 or in the interferon alpha beta immunity system can predispose these children to developing acute myocarditis. However, when they tested this hypothesis in vitro by using induced pluripotent stem cell-derived cardiomyocytes, looking at expression, looking at these genes, deficient cardiomyocytes, they weren't able to show a definite role as far as predisposition towards developing myocarditis for Toll-like Receptor or interferon. And what they surprisingly found was that 7 of the 42 patients that they studied by holding some sequencing that is about 17% of these patients, actually had a likely pathogenic mutations in six cardiomyopathy associated genes.                                            So this raises the question overall that perhaps if you have genetic mutations in the cardiomyopathy associated genes, could you be predisposed to these cardiomyopathies or cardiovascular specific disorders, and should we be searching for mutations in these cardiomyopathy genes in other types of cardiomyopathy, like tachycardia-induced cardiomyopathy or Takotsubo's disease, etc. Jane:                                  Yeah, that's really interesting. It's sort of a perfect example of a gene environment interaction where you know the genetic predisposition alone is not enough to cause disease, but then when combined with an environmental hit like a viral infection that's when the disease manifests. Very interesting. Naveen Pereira:              Right. And I heard that you have found something interesting as far as machine learning is concerned, Jane? Jane:                                  Yes, yes. So I was reading this paper, which was published in PLOS ONE in April. So the first and last authors of that paper are Steven Weng and Nadeem Qureshi. And the title of that paper was, "Can Machine Learning Improve Cardiovascular Risk Prediction Using Routine Clinical Data?" So, as the title suggests the authors were interested in whether they could improve on standard risk prediction algorithms by using an unbiased approach ... like machine learning approach. So they used the identified electronic health record data for over 350,000 people, and this was in the UK from UK Family Practices, and they took the baseline variables from people who were free of cardiovascular disease at the start of the study and then they looked to see if they could predict the risk of CVD events over the following 10 years.                                            So they decided to compare four different machine learning approaches to see the efficacy of the different approaches and then they used the American College of Cardiology guidelines as the standard to compare these new computer approaches to. So in that model they included eight primary variables, which are included in the ACC/AHA algorithm such as age, sex, smoking, blood pressure, cholesterol and diabetes. And then for their machine learning algorithms, they added additional variables that were present in the EHR. So they had 22 variables in total and that included things like triglyceride, CRP, creatinine, also ethnicity and presence of other diseases such as rheumatoid arthritis or CKD, and then also whether there were certain prescribed medications.                                            They took the first 75% of the sample as a training set, so that was over or just under 300,000 individuals. So they used this to train their various algorithms and then they used the remaining 25% just selected randomly, which was a little over 80,000 subjects, as a validation set to assess the efficacy of these algorithms. So over the 10 year period in total, 6.6% of the subjects developed incident CVD and they found that all of their four different algorithms, so that included random forest, logistic regression, gradient boosting machines and neural network approach, they all outperformed the established risk algorithms. So they all did slightly better than the current ACC guidelines. And the best one that they found was the neural network approach. So that correctly predicted 7.6% more patients who developed CVD compared with the established algorithm.                                            So, it's really quite a significant improvement [inaudible 00:21:46] within their data set there were several hundred additional cases that were identified using this machine learning approach compared with what would have been predicted just using the standard algorithm. So I think it's quite exciting, it shows how using sort of a more unbiased approach, but still using variables that are generally present in the electronic health record can actually improve risk prediction. And this sort of approach might help us to do a better job of identifying you know, the really quite large number of people who do go on to develop incident CVD or have MIs without having the standard risk factors that we know about.                                            Then I actually saw a second paper. So, for people who are interested in this sort of approach, there was a really nice review article that was published recently in JACC and this came out on 30th of May of this year, 2017, so just recently. The first and last authors of that paper are Chayakrit Krittanawong and Takeshi Kitai. The title of their paper is "Artificial Intelligence in Precision Cardiovascular Medicine." So in this review article, they discussed the potential of artificial intelligence to improve cardiovascular clinical care and they highlight both the challenges and the potentials. Overall, they emphasized how important it is for physicians to try to understand these new computational approaches. I think both so that we can harness the potential of these approaches, but also so that you know we who are in charge of patient care can understand the inherent limitations of these approaches.                                            So, the overall message I think from both of these papers is that machine learning, it's really exciting, it has a huge amount of potential, but you know robots aren't going to replace physicians any time soon, so we really need to have physicians working in tandem with these sort of computational approaches to really harness their potential. Naveen Pereira:              So Jane, that is fascinating and it's going to be especially important in the era of big data, where all medical centers eventually transitioning to electronic health records. So we have this wealth of information in the electronic health records, and we should do what large corporations have been doing, that is trying to individualize patient care by incorporating multiple parameters from the electronic health record to understand these patients better and come up with risk scores. About two years ago, we had published in the journal Studies in Health Technology and Informatics in 2015, a similar analysis trying to discern better incorporating multiple co-morbidities from the electronic medical record using machine learning techniques. We could improve predicting prognosis in heart failure patients and we found an 11% improvement in the area under the curve by using electronic health record data and incorporating co-morbidities by using machine learning techniques. So I think there's great promise for the future in medicine for this.  Jane:                                  Yeah absolutely, and as you point out as more and more places are moving towards fully electronic health records, it's something that's actually relatively easy and very cost effective to implement, so it's definitely an exciting approach. Naveen Pereira:              So Jane, this is very interesting talking to you about these various topics, but if I didn't pay particular attention to the author of the publication how can I access these manuscripts that we discussed? Jane:                                  So actually all of the links and links to the full article on the PubMed abstract for all of the papers that we've talked about are on the website. So the podcast website you can access that at fgtbcouncil.wordpress.com and if you go there you'll see a post for every episode of the podcast that we've done. So you can click on the episode you're interested in and then you'll find links to all of the papers and topics that we've discussed. Naveen Pereira:              Wonderful Jane. Look forward to talking to you again next month. Jane:                                  Me too. Okay, thanks Naveen. Naveen Pereira:              Bye Jane. Jane:                                  Bye.

Jane Ferguson:                Hi, everyone. Welcome to Episode Four of Getting Personal: -Omics of the Heart." I'm Jane Ferguson, an assistant professor at Vanderbilt University Medical Center. This month, we have a special feature from early career member, Andrew Landstrom, who went to the Heart Rhythm Scientific Sessions in Chicago earlier this month and talked to some of the scientists who presented their research. So listen on for interviews Andrews conducted with Anneline te Riele, discussing the challenges and opportunities related to incidental findings in genetic testing, with Ernesto Fernandez, describing his research into whole exome sequencing and Long QT syndrome, and with David Tester, discussing novel variance and pathway analysis in Sudden Infant Death Syndrome. Andrew :                           My name is Andrew Landstrom and I am from the Baylor College of Medicine Department of Pediatrics' section on Cardiovascular Disease. I'm here at the 2017 Heart Rhythm Society Scientific Sessions. Anneline, will you tell us a little bit more about yourself, and what brought you to HRS? Anneline:                          Sure. So my name is Anneline Te Riele, I am a physician from The Netherlands. I finished my medical training in 2012 basically, in The Netherlands, and I started doing a PhD on ARVC in a combined project of our Netherlands patient as well as a group at Hopkins. So what brought me to HRS? I think of course the science. There's a lot of very good science. Actually, I think it's the best meeting for my purposes. Andrew :                           Absolutely. So will you just start by telling us a little bit about the spectrum of genetic testing in the clinic and about both the opportunities and the challenges that it brings? Anneline:                          Sure. So what we do in clinic, and I think this is really the challenge that we're facing currently, is we have moved from just testing on gene or one small panel of genes to bigger panels and then to whole exome or even whole genome sequencing. And I think the good part of that is that in certain cases, certain well-selected cases, you'll get a higher change of actually finding that gene that is responsible for disease.                                            On the contrary, it also leads to a lot of incidental findings. So findings that you were not expecting based on the phenotype of the patient and then you need to deal with those abnormalities that you've found and that brings on a lot of challenges as well for the family but also for us as physicians. Do we then need to screen those families, what do we do with this patient, do we treat them with medical therapies or drugs or do we give them ICDs? That kinds of question. So that I think is a virtually important part of what we're currently dealing with in clinical practice. Andrew :                           It does seem to be a very widespread problem. And here in the US of course we have the American College of Medical Genetics guidelines about reporting a variance. How do you think that that plays into the increased genetic uncertainty here in the US at least? Anneline:                          So that's a great questions. In 2013, the ACMG produced a guideline on which genes to report if you find these incidental findings. So 24 of these genes, and that's actually a big number, 24 of these genes are cardiovascular genes and that's mainly because changes in cardiovascular genes may detrimental effects down the line and really cause death or certain morbidities that are really important for the patient so we do need to deal with that.                                            And the problem with the ACMG guidelines and especially the pathogenicity guidelines is that they require two aspects. They basically require first that the variant was seen before in other cardiomyopathies or in this case other patients with disease. And that's really difficult for cardiomyopathy genes because these are large genes, they have a lot of novel or private mutations in there, so it's really hard to fulfill that requirement of having been seen before.                                            And the second thing is that the ACMG guidelines require functional studies as another proof of evidence of pathogenicity and of course, I think we would all like to do that in all of our patients, but it's just not feasible for financial purposes and all that. So that's a problem that we're facing. There are options and solutions but I think we'll talk about that later, but yeah, I think that's a problem that we're facing. Andrew :                           So on the one hand you have the ability to make a diagnostic decision based on a clear finding, but oftentimes the threshold to calling it a clearly pathologic variant is very high and oftentimes it never rises to that so it becomes more genetic uncertainty. Anneline:                          Yeah. I think that's basically right. And of course in an ideal world, we'll have certainty and say this is likely or this is definitely pathogenic, and this is likely or definitely benign, but in the real world, really, I think maybe even 80, 90% of the cases were in that gray zone in between and we need to deal with that. Andrew :                           Yeah, yeah. And you had some great resources that both scientists and clinicians alike can apply to these unknown, uncertain variants that might clarify things at least a little bit, and what are these tools? Anneline:                          So of course, from a traditional perspective, we have always looked at in silico predictive programs, we'll look at segregation data, and I think they're all very important, but they all have limitations, so for example, in silico predictive programs, they likely overcall mutations deleterious and segregation data is nothing more than evidence of pathogenicity of a locus to a disorder, not necessarily that variant, so the new things that are on the horizon, and a thing that could be the future of [inaudible 00:06:04] interpretation is collaborative project so really we should be collaborating, we should not be having our own little islands. The collaboration is the key here.                                            And collaborative efforts in the US have been for example, ClinVar and NHLBI funded effort, as well as ClinGen and ClinGen, or Clinical Genome, is perhaps the, at least it claims to be, the authoritative central resource to go back to that curates variants as being pathogenic yes or no. And I think these databases, ClinVar finally has a database entry, so the variants will be in ClinVar, but ClinGen provides an expert panel of individuals who will curate these variants as being pathogenic yes or no. I think that is a central resource that we should all be aware of. I know these are not the only ones, there are other collaborative efforts out there.                                            I mean, there are ways to connect clinicians, so for example, Match Maker Exchange is a website that you could use to enter your variant and the phenotype of the patient and you submit your own information and then you'll get matches in other databases, but not only your own match shows up. So if, say, two years later, another physician comes up and looks for the same variant, you'll get a pop up, which will actually be very nice for these clinicians to get in touch. So that's, I think, the feature ... future of variant interpretation is collaboration. That's basically my, I think my main important message here. Andrew :                           I think that's absolutely right. I think this has become sort of a big data question that requires many perspectives, and a lot of resources to be able to curate accurately. What are some of the limitations of these tools that you've seen that kind of, you have to keep in mind in terms of trying to determine whether a variant is truly pathologic or not with a patient that you have sitting in front of you? Anneline:                          So that is, I mean, of course, there's many limitations in the things that we currently do because there's so much that we don't know. But for example, to give you an example, ClinVar I think, is one central resource that we should all be aware of and if you go to ClinVar, there is actually data from two years ago, and I'm sure the numbers are high if we would look now, but if we look in ClinVar two years ago, we already saw that of the, say 120,000 variants that were in the database, 21% of these variants were called VUSes but if you look at these variants, 17% of the cases, the labs or the individual submitters of ClinVar didn't agree on the actual classification of that variant.                                            So the limitations that we all should be aware of is that there is not one single solution and you should look for evidence and really research your variants. So look at Popmap, look at what is out there, look the patient of course, look at the clinical phenotype, does it match what you think the gene should be doing or not, or is it completely unrelated? And then of course search these databases but be aware of the fact that there may be errors there.                                            Another thing I want to highlight too is that we typically go to population databases, so Exome Variant Server, ExAC, I think these are very popular databases that we use to look at the frequency of variants in a selected population. But really these databases may have sub-clinical disease patients, so I know ExAC has three NYBPC-3 mutations that are known to cause HCM, so this is something to keep in mind. There's not a gold standard truth if you open these databases, but you should have multiple pieces of information when interpreting your variant. Andrew :                           And that's a good point. I think with a lot of these cardiomyopathies and channelopathies, particularly some of the more frequent ones, when you have a database of 60,000 people, at least a couple of them are going to have disease. Anneline:                          Yeah. I think that is part of the problem. I mean HCM is pretty prevalent, I mean one in 500 individuals likely, I mean these are recent numbers, has the disease. So I think the cutoff of a minor allele frequency of five percent, which is in the ACMG guidelines, I think is way too high for this disease. So this is what the cardiovascular expert panel of ClinGen has done, so they ... This is, ClinGen, as you might know, Clinical Genome, is a one-on-one team of curators that know the framework of ClinGen and then there is disease experts that are very well accustomed with the disease and the genes associated with it. So they provide teams and these teams work together, and the cardiovascular expert group has recently published a modified, or customized, ACMG guidelines on how to deal with the intricacies of the cardiomyopathies and for example, NYH-7 which is the first genotype deposed in ClinGen or in ClinVar finally.                                            So they modify that cutoff, the minor allele frequency of five percent, which is the BA-1 ACMG guideline cutoff, they changed that to 0.1% and I think that's exactly what you were saying, that is important to keep in mind, some of the cardiomyopathies are way more prevalent so you should not consider that if you see it in a population database that you think that it's, then it's normal, it's not necessarily the case because this is a prevalent disease. Andrew :                           Yeah, and particularly when commercial genetic testing companies all can't agree that a variant is bad, and we all can't agree that a healthy variant may or may not be good, there is definitely a lot of genetic uncertainty there. Anneline:                          Exactly, exactly. Andrew :                           Now, whole-exome sequencing certainly has its role clinically, even with that genetic uncertainty that we spoke about, but it has a clear role in genetic discovery as well. Anneline:                          Sure. Andrew :                           And you were part of a very recent paper, and you led a very long list of authors, speaking more about your collaborative approach to genetics research that evaluated a novel substrate for ARVC, is that correct? Anneline:                          Yes. So this is something I'm actually pretty proud of. As you said, it's a collaborative effort, so it literally take a village to do these kind of studies and we're lucky enough to collaborate with a lot of people who are interested in the same topic. So what we did ... and I metnioned to you in the beginning, I come from the ARVC field ... So what we did is we had one ARVC patient that was discovered by whole-exome sequencing to carry an SCN5A variant and we, in and of itself, found that that was very interesting, because SCN5A, as you know, has been associated with Brugada syndrome predominantly but many other cardiomyopathies as well, so DCM, even ACM. There's been a lot of controversy about SCN5A in that matter.                                            So the computational data, the population data, it all pointed to the fact that this variant may be pathogenic, but we weren't really able to connect those dots just yet. So we then collaborated with the group in NYU with Mario Delmar, who did, first of all, functional studies on the sodium channel, but what was nice is that he was able to use his novel method of super-resolution microscopy which is a way in which we can look at the nano-scale structure of the cardiomyocytes, or really the small, small levels of molecules that you see in these cells. And what we did is we found that not only NAV1.5 which is the gene product of SCN5A but also [inaudible 00:13:53] which is an adherence structure molecule, which links the cells together was actually less present in our ARVC patient compared to the control. And this was in the IPS so cardiomyocyte molecule, which we corrected using CRISPR-Cas9 technology so I think at least in current practice, on of the best pieces of evidence that we can get.                                            So I think this shows that our SCN5A variant, I mean, in this case, probably really was pathogenic, but also in a pathophysiological standpoint, explains to us how SCN5A mutations, which are typically thought to be only affecting the sodium channel, can also lead to cardiomyopathy phenotype which has implications beyond the ARVC world, but also in DCM I think this is a nice finding of collaboration that I think ... I hope more people will look into this. Andrew :                           Absolutely I think the trouble with SCN5A is exactly like you were saying, it's been implicated in Long QT, Brugada Syndrome, SIDS, [inaudible 00:14:57], now ARVC, and even nodal disease, like sinus syndrome and things like that. So the ability to show sort of mechanistically, that while you have a change in your sodium channel gating that you also have a change in the way that the cells can connect with each other and form contractile force is, I guess, key to your study. Anneline:                          Yeah, yeah. I think this really, I mean, I'm hoping at least, it was also finally published in a journal that looks more into functional studies, so not necessarily only genetics, and I think we need to work closely not only on the genetic side, but look closely at the pathophysiological standpoint for gene discovery purposes because this will really explain to us why one gene is implicated in one disease, and also it points to possible directions to perhaps stop the disease process and treat these patients, which I think is vital in our clinical practice. Andrew :                           So are SCN5A mutations in ARVC a common finding or are they rare? Anneline:                          So they are pretty rare. I mean, we do find them every now and then and maybe they're modifiers. So what we did to follow up on that one individual, we check 281 ARVD patients who were screened just by regular screening, not by whole-exome but we did a targeted screening of SCN5A and we found five variants in these 281 patients, so that's two percent. I mean, it's still rare, but it is as rare as any other minor gene causing ARVC, but it is a rare feature, so I mean, I think it could be a player. And interestingly, the phenotype didn't change much. It wasn't really different from the ARVC patients without an SCN5A mutation which is reassuring.                                            What we also saw is that the prevalence of mutations in those with desmosomal mutations. So ARVC is, as you know, typically associated with diseases or mutations in the desmosome. It was more often seen in those without a desmosomal mutation. That was almost double as frequent as in those with a desmosomal mutation. So it does give us some direction to the fact that this may be a player out there. I mean of course it's not Plakophilin-2 which is the major player, I think, in ARVC, but I think it may cause a, at least a certain form of cardiomyopathy of arrhythmogenic cardiomyopathy that we need to be aware of. Andrew :                           And how do you think your new discovery of SCN5A being associated with ARVC, how do you think that plays into the bigger discussion we were having about expansive genetic testing and what that may mean for a patient as far as diagnostic utility but also limitations of variant interpretation? Anneline:                          That's a great question. So I think we should be cautious of saying this gene causes only this disease, and I think this is a common feature not only in ARVC but in a lot of cardiomyopathies and even in channelopathies. I think the concept of one gene causes one disease is outdated. We know that multiple genes have multiple effects and this SCN5A, of course the gene product is NAV1.5 which is the major alpha subunit of the sodium channels so it is really not the canonical function of SCN5A or NAV1.5 that causes cardiomyopathy here but it's a non-canonical function so I think we should be aware of the fact that gene products have different functions and that there can be overlap of the cardiomyopathies. So of course I think we should be screwing SCN5A in our ARVC patients and I'm hoping a lot of labs and a lot of physicians are already doing that, but it's really not the only thing that is associated with ARVC. So that's important to keep in mind. Andrew :                           What do you think the next steps are for sort of broadening the implication of your finding? Anneline:                          So what we are doing currently, and is a little bit of a sneak peek, because this data is not really out there yet, but we have, in this cohort, we found these five variants in 281 individuals, and we're currently working on one of these individuals to get another IPSO cardiomyocyte cell line and look into the functional components to that. And interestingly, this variant, that exact variant in that ARVC patient was also found in a Brugada Syndrome patient. So wouldn't it be nice to actually set them side by side and see what the differences are?                                            Of course this is a little bit of a future music, if you know what I'm saying, like this is something that we don't have just yet, but I think what we need to figure out is how epigenetic or environmental factors play into this field and to explain how one gene or one variant, even, can cause opposite functional effects in different phenotypes. Andrew :                           What do you think is needed to help clarify some of the genetic uncertainty you see clinically? Anneline:                          I think a lot of collaboration, a lot of money, quite frankly. I think we need to ... I mean, the functional data is really helping us not only for understanding that single variant, but also for gene discovery, and as I said, for treatment down the line, that is necessary, and I think the variant of uncertain significance, I mean, if we all live on our little islands and only do our little practices, then we're not going to go a lot further. So we need to work together to understand what your patient has in this variant, my patient had in that variant, and this is our phenotype, so we need to connect those dots to be able to make certain conclusions. Andrew :                           Well, I'm all for collaboration, as well as additional money, that's good. Anneline:                          Good. Andrew :                           Well, thank you so much for spending time with us. Anneline:                          Sure. Andrew :                           And again, congratulations on a wonderful presentation. Anneline:                          Thank you very much. Andrew :                           I'm joined by Dr. Ernesto Fernandez from the Baylor College of Medicine to talk about his research project. Ernesto, I'm wondering if we can just start by introducing yourself and what your project is. Ernesto:                            I am a second-year pediatric resident, I'm applying to a cardiology fellowship right now and I'm interested in, obviously, all aspects of pediatric cardiology. We're trying to figure out whether testing for Long QT genes or Long QT syndrome is actually warranted in otherwise healthy individuals. We're trying to see what the yield is on these testings, specifically whole-exome sequencing. Andrew :                           And I think this project really hits on an important point, whereby, because we've been able to interrogate the genome more comprehensively with clinical testing, that we've run into more incidentally identified variants. And these variants can pop up in genes, like the genes responsible for Long QT syndrome. Talk a little bit more about these variants, what the implication is of finding these variants incidentally, and what your project hoped to target as far as the diagnostic value of these variants. Ernesto:                            Yeah. So I guess the answer to your first question is that we are coming up with these marvelous new techniques of analyzing the genome and now we're using whole-exome sequence testing to look up is someone has any exome that's abnormal and this has caused a huge problem whereby we're now finding all these variants that we don't really know what they mean. We call them variants of undetermined significance.                                            Our study is basically premised by the fact that if you have no underlying suspicion for any arrhythmic disease, there's really no need or no indication to be referred for whole-exome sequencing testing, given that the most likely result is a variant that we don't really know what it means. And it's probably going to be benign. Andrew :                           So on the one hand, you have a well-established gene panel that's being used for diagnostic purposes with you index of suspicion being high for Long QT syndrome versus something like a whole-exome gene screen where somebody may not be thinking about Long QT syndrome as a diagnosis and have low pre-test suspicion but then comes back with a variant found in these genes sort of incidentally. Is that sort of the dichotomy you're drawing? Ernesto:                            Yeah. I think the best way of explaining it is through Bay's Theorem whereby if you have someone with a high index of suspicion when you start off to have sudden cardiac death, a family history of an arrhythmic disease, and you get a test for it, such as a gene panel for Long QT syndrome, and they come up with a positive test result, then you're going to say, "Oh. I should probably evaluate this further," whereas if you have someone who has some dysmorphism, they have delay, they might have seizures, but there's no family history of sudden cardiac death, no personal history of syncope, then there's really no need to send off this big gun, the whole-exome sequence, because you're likely to either get a normal variant or you're likely to get a variant that we don't know what to make of. Andrew :                           So I think, Ernesto, that nicely summarizes the clinical question that you had in mind. What was your hypothesis going into the study, and how did you seek to approach that hypothesis, sort of experimentally? Ernesto:                            So we came up with the hypothesis that if you have an incidentally identified variant within the whole-exome sequencing tests without any other clinical suspicion, it's likely to represent a benign finding. We went about by analyzing the data from the Baylor Miraca labs on the whole-exome sequencing data that they achieved, and we looked specifically at individuals who had gotten these tests and found to have a variant of undetermined significance, or had a pathologic variant for either one or all 17 of the genes for Long QT syndrome. We compared them to individuals who had known Long QT syndrome that had undergone genotype testing, and we [inaudible 00:25:21] these individuals from the literature. And we wanted to compare the whole-exome sequencing cohort to individuals who were otherwise healthy and had obtained a whole-exome sequence. So these are patients or individuals from the well-established ExAC database that are believed to be ostensibly healthy individuals. Andrew :                           So if I understand you correctly, you're comparing this unknown cohort, that being the rare variants found in whole-exome sequencing, against a positive control cohort of pathologic cases versus a negative control cohort of healthy individuals derived from the ExAC database to look for whether those west variants are more similar to the cases or the controls. With regards to the west cohort, what was the prevalence of individuals with these incidentally identified variants, how many did you find? Ernesto:                            So we actually found just about 49% of individuals had some variant in Long QT syndrome gene, and noted that about 12% of them had a mutation in the major causes of Long QT syndrome, and just over a third, or 36% had a mutation in the more rare causes of long QT syndrome. Andrew :                           That's a pretty surprising finding. So you're saying that one in two individuals who get whole-exome sequencing sent for whatever reason, have a variant in a Long QT-associated gene? Ernesto:                            That's what the data suggests. Andrew :                           And where did you go from here? Ernesto:                            So from there, we went onto compare the variant frequency between the case's cohort, those individuals with known Long QT syndrome, those individuals in our west cohort from the Baylor Miraca labs, and those individuals from the ExAC database who are otherwise healthy. So we noted that in our west cohort, there was about 13% of individuals who had a positive variant in the Long QT syndrome one through three genes, the major causes of Long QT syndrome. When we compare that to the ostensibly healthy individuals from the ExAC database, it was 12% in that study that had some variant in Long QT syndrome genes that are major causes of Long QT syndrome itself.                                            This was statistically similar, it was indistinguishable. And then when we compared it to the pathologic cases, it was actually about 50% of those cases who had a positive variant in a Long QT syndrome gene one through three. Andrew :                           So there was a relatively low frequency of individuals who had variants in one of the big three Long QT genes in both controls and the west cohort, and was obviously much higher among individuals with a diagnosis of Long QT syndrome. Ernesto:                            Yep. That's exactly what we found. Andrew :                           And where did you go from here? Ernesto:                            And then from there, we had a good idea that there was probably a big difference between cases and west, but we wanted to make sure, gene by gene, that there was no difference between our west cases and the ExAC database, the control cases. So we mapped each variant frequency by gene for the major causes of Long QT syndrome. There was no statistically significant difference between the west and the controls. Andrew :                           So the gene frequencies between the controls and the west were indistinguishable and very much different, both of them, it would seem, to the pathologic cases. Ernesto:                            Correct. Andrew :                           And you then looked at the position of these variants, the actual amino acid residues, correct? Ernesto:                            Yeah. So we looked at, for KCNQ1, KCNH2, and SCM5A, the three major causes of Long QT syndrome, one, two, three respectively, and we mapped out the amino acid positions where there was actually a mutation for each individuals. So the cases, controls, and pathologic cohorts. We determined the percent overlap between the west cohort and the controls and the percent overlap between the west cohort and the cases and noticed that for all three, there is a huge preference for west and control versus west and cases. Andrew :                           So if you're a west variant you're more likely to reside in the residue also occupied by a healthy individual variant as opposed to a pathologic variant? Ernesto:                            Yeah. Exactly. Andrew :                           And so what did you do next? You retrospectively looked at some of the charts of the patients who were seen at Texas Children's Hospital, correct? Ernesto:                            Mm-hmm (affirmative). So then we had 223 total individuals that had an incidentally identified variant within one of the major three genes, the Long QT syndrome genes. We looked at the reasons for their referrals and noticed that the vast majority of individuals were referred for some developmental delay, for some dysmorphism, for a non-cardiac cause, and then it was only about 23% of these individuals that actually had a reason for referral that was cardiac in nature. And less than on percent of individuals were referred for a solely cardiovascular reason. And we concluded that it's unlikely that these individuals were referred for a cardiac reason, as the data suggests, and that as a result, the index of suspicion for an arrhythmia is likely lower in these individuals. Andrew :                           And what did you find when you looked at the charts of those individuals? Ernesto:                            We had EKG data for a good number of them, and we excluded individuals who obviously had no EKG data, and we excluded individuals who had some congenital abnormality and then anyone with any other arrhythmia that would make the QTC interpretation more difficult, such as interventricular conduction defects.                                            We ended up with 62 individuals and 61 of them had a normal QTC, so there was no evidence of QT prolongation at all. There was one individual who was left who had borderline elevated QTC of 460, which was our cutoff for borderline elevation and this individual had actually been seen by pediatric cardiology at Texas Children's Hospital and found to have ... a history of syncope and it was found to be non-cardiogenic in nature. Andrew :                           So matching the variant data which suggested that you had likely found background variation in the west, you found no evidence of Long QT syndrome in these individuals who had variants in Long QT genes. Ernesto:                            That's correct. So, the overall percent was very similar between the healthy individuals and the west individuals. The variant frequencies were almost indistinguishable, and then the variant co-mapping for all, for both the west and the controls, was preferential to the western cases. So that kind of matched what we found in our study, that there was no clinical suspicion or clinical diagnosis of Long QT syndrome in these individuals who had been found incidentally. Andrew :                           Well that sounds to me to be a pretty big finding. Ernesto:                            Yeah. I think it's pretty important to get this information out there. Andrew :                           So what do you think the take home message for your study is? Ernesto:                            I think the take home message is if you don't have a suspicion of Long QT syndrome or of an arrhythmia, there's low likelihood that such a big gun test as the whole-exome sequence is likely going to change your mind. Andrew :                           So Ernesto, what would you advise a cardiologist who maybe gets a patient in clinic with a chief complaint of a VUS in a Long QT associated gene picked up on west, what would you advise based on your study findings? Ernesto:                            They're going to have to determine their own pre-test suspicion. They're going to have to get a good history and physical, probably get a baseline EKG to determine what the QTC intervals are, and if there's really no other clinical suspicion for Long QT syndrome, they're likely to be able to provide reassurance at that point in time. Andrew :                           Ernesto, what do you think the next steps are for this project, and what do you think still needs to be done in the field to reinforce your conclusions? Ernesto:                            I think my study is one of the early studies of this field, so getting more studies like this and other channelopathies, getting not just looking at Long QT one through three but looking at all of them, and in patients who've been evaluated at Texas Children's or any other institution would be helpful. And then moving forward to give more credence to the idea that if you have history that's reassuring and physical exam that's reassuring, then you probably don't need to have further testing. Andrew :                           What do you recommend if your index of suspicion is high for Long QT syndrome, so maybe a QTC in the low 480s, maybe a family history of syncope or seizures, do you think whole-exome sequencing is the way to go? Ernesto:                            Right now, that's probably not the best test, given all these incidental findings that we don't really know what to do with. There's other tests that are more high-tailored for those specific diseases, like Long QT syndrome panel among others, that are probably more likely to give you a positive post-test probability. Andrew :                           So testing for the disease you're suspicious for as opposed to testing indiscriminately? Ernesto:                            Yeah. Andrew :                           So Ernesto, thank you so much for taking the time our of your day to speak with us. Ernesto:                            Thank you, Andrew. Andrew :                           I'm here with David Tester, senior research technologist working with Mike Ackerman at Mayo Clinic, and he just gave a wonderful talk on whole-exome sequencing and next-generation sequencing as an unbiased look to determine underlying causes of Sudden Infant Death Syndrome, or SIDS. So David, I'm wondering if you can introduce yourself and talk a little bit about your project. Dave:                                 Sure. I'm Dave Tester and I'm at the Mayo Clinic, again with Mike Ackerman. Dr. Ackerman and I have been together for about 18 years now, with a real focus on genetics of sudden cardiac death disorders. So this latest study was looking at whole-exome sequencing in a population of SIDS cases in collaboration with Dr. Elijah Behr at St. George's University in London.                                            And really the approach, what we were aiming for is really kind of two-fold. First we were looking to determine what is the yield of ultra-rare variance within genes that have been implicated in cardiovascular disorders? These would be the cardiac channelopathies and some of the cardiomyopathies such as ACM or ARVC, for example.                                            And the second thing that we were wanting to look at was can we use this to search for sort of novel candidate genes for Sudden Infant Death Syndrome susceptibility? And so we took that aim and really the main result was to show that about 14% of our SIDS cases had what we term potentially informative variants. And those are going to be variants that were within sort of the major channelopathy genes that are implicated in Long QT syndrome or CPVT as well as loss of function variants within the 90 ICC genes that we had examined.                                            Using the ACMG guidelines for determining the pathogenicity of variants, about 4.3% of our SIDS cases hosted an ACMG guideline predicated likely pathogenic to pathogenic variant. And most of those variants represent either a frame shift or splice site error variance really in minor cardiomyopathy genes and channelopathy genes. So there's still a lot of work that needs to be done in terms of looking at specifically missense variance within channel genes and that sort of thing, and really kind of functionally characterizing those to determine whether or not they truly are pathogenic or if they should remain variants of uncertain significance. Andrew :                           And so you took a very complex disease like SIDS with probably a number of differens ideologies and found a pretty good percentage have suspicious variants, that 14% or so, and then 4% had variants that were so suspicious they would meet American College of Medical Genetics guidelines for being a possible or likely pathologic variant. Where do you think this study lies in sort of the continuum of identifying the genetic ideology of SIDS, and what do you think these findings sort of add to that overall picture? Dave:                                 Well I think these findings in general really just kind of show the complexity of SIDS. Whether or not SIDS is really truly genetic or not, or perhaps it just, if it's not monogenic, perhaps it's polygenic, and so those are some things that we should be considering and looking at. Now some of those questions might be able to be answer through our whole-exome sequencing data set that we have, and I think those are really going to be kind of the next phases.                                            We can also take and do some pathway analyses of the exome sequencing data, for example, and see our variance kind of lining up on certain pathways that may contribute to certain pathologies that could contribute to SIDS. Andrew :                           And in your study, you had a few genes where the number of variants that were found in SIDS cases were higher than in your controls. Can you speak some more about what those genes may tell you in the context of pathway analysis for SIDS? Dave:                                 Yes. So there was ... There were not genes that came out with sort of a genome-wide significance level. But there were at least 400 genes that had a p-value of 0.05 over representation in SIDS versus our ethnic match controls and 17 of those genes have a p-value of 0.005 and we're really kind of focused on some of those that have a little bit higher p-value for us to assess. A few of those genes may represent biologically plausible candidate genes for SIDS and we were kind of actually going through and considering which ones we'd like to follow up on in terms of function. Some of these genes do play a role in, say, cardiorespiratory system and function of the heart as well as in the brain. Andrew :                           So then given all these findings, and the fact that you may have some candidate genes and candidate pathways that might be interesting to look at further, what are the next steps that you think would help this project move forward, and what do you think the field of Sudden Infant Death Syndrome and Sudden Unexplained Death Syndrome needs to kind of move forward? Dave:                                 Well I think from a genetic standpoint, the study that we just complete was really on a large set of unrelated infants that had died suddenly. We did not have access to parental DNA and so moving forward in terms of the genetics, I think incorporating sort of a trio analysis I think would get at the question of sort of [inaudible 00:42:01] variance for example. The other things, in terms of genetic standpoint is perhaps looking at different genetic mechanisms. Whether these are copy number variance that may be missed by exome sequencing, perhaps some of the SIDS could be due to epigenetic abnormalities or even small chromosomal abnormalities that perhaps may not be detected on certain arrays on there being used. So I think going forward, kind of taking those approaches to look for sort of unique genetic variation. Andrew :                           Well Dave, thank you so much for taking the time to speak with me and congratulations on a great project. Dave:                                 All right, great, thank you. Jane Ferguson:  Thanks to Andrew for highlighting the interesting precision medicine research presented at HRS and thanks to you all for listening. We'll be back with more next month.

Jane:                   Hi. Welcome to episode three of Getting Personal, Omics of the Heart. I'm Jane Ferguson and this podcast is brought to you by The Functional Genomics and Translational Biology Council of The American Heart Association. In this episode, I talk to Jonathan Mosley about an interesting genetic method he has developed to look at shared genetic contributors that influence risk phenotypes, as well as disease risk, which can be used to integrate data from prospective studies with large scale electronic health record data. We also highlight a recent AHA scientific statement on genetic literacy and Nevine and I discuss the latest in precision medicine.                              Our large hurdle in implementing precision medicine will be to increase understanding of genetics and genomics amongst healthcare providers. Sima Mittal, Karen [inaudible 00:01:11] and colleagues tackled this issue as part of a recent AHA scientific statement published on behalf of The Council on Functional Genomics and Translational Biology, The Council on Cardiovascular Disease in the Young, The Council on Cardiovascular and Stroke Nursing, The Stroke Council, The Council on Lifestyle and Cardiometabolic Health and The Council on Quality of Care and Outcomes Research.                              As the promise of genetics guided treatments is becoming a reality, cardiovascular healthcare providers often struggle to stay up to date on this large and rapidly advancing field. Although there is a need for more dedicated genetics professionals, such as genetic counselors, it is also important that all cardiovascular practitioners maintain core competencies in cardiovascular genetics. This statement entitled, "Enhancing Literacy in Cardiovascular Genetics" was published in the October 2016 issue of Circulation Cardiovascular Genetics and outlines useful information for the cardiovascular practitioner when considering genetic and pharmacogenetics testing and includes pointers to resources for enhancing knowledge and genetics and genomics. As always, this and the other papers mentioned in this episode are linked on the podcast website at fgtbcouncil.workpress.com.                              Hi, Jonathan. Thank you for joining. Jonathan:           Thank you, Jane. I'm happy to be here. Jane:                   Maybe first I'll give you a chance to introduce yourself. Jonathan:           Sure. My name is Jonathan Mosley. I'm up here at Vanderbilt University. I'm an instructor in the department of medicine and do both a little bit of clinical work related to hypertension but spend most of my time doing research, and in particular genetic research, using Vanderbilt's integrated electronic health record and biobank data system we call Bioview. Jane:                   Which is a really fantastic resource and you've been doing some really interesting things with it. Today, I wanted to talk about sort of an interesting method and the application of that method that you've developed. For our listeners, there's a link to these papers on the website but you can find them. We're going to be talking about two different papers. One of them called Defining a Contemporary Ischemic Heart Disease Genetic Risk Profile Using Historical Data and was published in the December 2016 issue of Circ CV Genetics and then a second sort of related paper entitled Investigating the Genetic Architecture of the PR Interval Using Clinical Phenotypes is in the current issue of Circ CV Genetics, which is the April 2017 issue.                              Jonathan, maybe to start you could tell us a little bit about sort of your thinking behind the development of this new method. Jonathan:           Yeah. I can kind of give you a rationale. I'll focus on that first paper initially. The content area really relates to ischemic heart disease or coronary heart disease, so disease due to blockages of your coronary arteries. This disease in particular it's really I think an epidemiologic success story. Really over the last several decades there's been a pretty marked decline in death due to coronary heart disease. A good portion of that can be attributed to the fact that we've really started to understand this disease and understand risk factors. I think the framing in studies is always brought up at the prototype but study these prospective studies like Framingham. Help us identify risk factors that modulate risk of coronary heart disease. By changing behaviors like smoking and treating cholesterol levels, we've been able to now really change the epidemiology of this disease.                              There's also been some other changes in society I should add. We've become a much heavier society and so this also modulates our risk of coronary heart disease. If it were cheap and easy to long perspective studies, you could imagine that it would be desirable to start a new Framingham today kind of under the notion that the epidemiology of this disease has changed. Let's create a new cohort that reflects today's epidemiology so we can really target the prevalent risk factors that we're seeing in our population right now and really try to target those risk factors.                              It's not really a feasible approach. Starting a perspective study is constrained for quite a few reasons. Jane:                   Right. None of us have the funding for that. Jonathan:           Yeah. In terms of cost and then there's always an inherent lag also in these perspective studies in terms of you have to wait for outcomes to occur too. Kind of a rationale underlying this first paper is is there a quicker way which we could try to get the outcomes that we might otherwise expect from a perspective study? At Vanderbilt, we have this data resource that I mentioned earlier called Bioview. A large amount of the health information and data related to clinical encounters are captured in electronic form and this is available in a de-identified form for research. We have a dataset of over two million clinical records or records on two million individuals that we can use for research. About 235,000 of these individuals also have DNA available. In particular, we can do research looking at genetic modulators of disease.                              In this dataset that I have, I can quickly identify thousands of cases of various outcomes like myocardial infarctions or ischemic heart disease or diabetes. We can identify far more of those outcomes than you'd except really in most prospective studies. They're not large enough to observe that many outcomes but really a limitation of these is that we don't collect standardized baseline measurements within individuals who are captured in electronic health records. In other words, they're just encountering the health systems for different reasons but there's no protocol that describes specific baseline data that should be captured. It's really not feasible to do a perspective study or even a retrospective study despite having all these great outcomes.                              The rationale for the current study was really can we take all these outcomes that we're observing in institutions like Vanderbilt and others and perhaps get the baseline risk factors from another source. In this kind of imagined study design, you take risk factors that you're able to measure under epidemiologic conditions and then see whether they're associated then with these outcomes that we're observing in our EHR systems. That's really the study design. That's really the hypothesis that I was exploring. Could you implement this particular study? Jane:                   That's really interesting. I think it's a great way to sort of maximize the availability of data from different sources to sort of take what you need from different studies and combine it. For this kind of study, if you're combining data from two different studies, how similar do the demographics have to be? Do these have to be from people in the same country, the same racial group? How important are those sort of factors? Jonathan:           Using standard epidemiologic methods, it's not possible obviously to link kind of risk measured in one population and outcomes measured in another population. Really, emerging genetic methods allow us to do this. What we're doing is we're capturing or using genetics really to measure or to link these risk factors in one population into another population. Issues like race are important. We certainly know that there are racial differences that if you don't account for these they can really lead to a lot of confounding and unexpected findings. It's important that you can get populations that are genetically similar for the method that we used. Jane:                   Yeah. Maybe we can go into the results of what you found in the first study looking at ischemic heart disease genetic risk. Jonathan:           Yeah. Let me just kind of tell them a little bit about the genetic method that we used to associate these. What we did is we used a method measure the effects of large numbers of snips on a phenotype. This method is fairly similar to or has a lot of things in common with kind of very old genetic methods where you'd estimate heritability. Often, you'd do this by you'd collect related individuals and you wouldn't directly measure their genetics or how genetically similar they were, but you could infer how much genetics they shared by knowing their relationship. For instance, a mother and a child share on average half their genetics. Then what you can do by kind of inferring how genetically similar individuals are, you can measure a phenotype and then estimate the contribution of genetics to that phenotype based on the shared.                              With kind of current genotyping methods and with appropriate sample sizes, now you can do similar types of analysis, but instead of inferring how genetically similar people are, you can actually measure how similar they are based on common snips. There's basically computations you can do across large numbers of snips to estimate how genetically similar they are. Once you have the similarities, then you can quantitate really how much of the variability in the phenotype can be modulated by genetics. That quantity is often called the chip heritability or it's really the heritability that can be due to large numbers of snips. You can estimate the heritability of two phenotypes but you can also measure the extent or quantitate the extent to which those genetics are shared between two phenotypes. This quantity is called the genetic correlation.                              What we did in this particular study is that we took baseline risk factors that were measured in the Eric population. There's about 8,000 unrelated individuals of European ancestry. We took baseline risk factors that had been measured in their first visit and then we took two different outcomes that we curated from our electronic health record dataset. This was the dataset that actually came from the emerged population, which is a collection of institutions like Vanderbilt that have come together to pull their resources in order to create larger datasets for study.                              What I did was measure then the genetic correlation between these 37 baseline factors measured in the Eric study and these two phenotypes. The first one I looked at was type II diabetes. I kind of think of that as a positive control. It's fairly easy to clinically diagnose or create a definition of diabetes. Basically, if you have a glucose above a certain threshold then you have diabetes. It's fairly easy to standardize. I expected that this phenotype would work well. What I did was I measured the genetic correlation between each of those 37 risk factors and type II diabetes and then I also did a longitudinal analysis within the Eric study where I measured hazard ratios, so a standard epidemiologic measure of risk, basically for each of these 37 risk factors to find out kind of the epidemiologic association between these risk factors.                              When I compared them, what I found was that genetic correlations or measures of genetic risk across populations very closely corresponded to the hazard ratios or a standard epidemiologic measure of association. Suggesting for type II diabetes, really the epidemiologic risk seems to be measuring the genetic risk of the disease. Jane:                   Yeah, which is interesting. I guess for diabetes, we know a lot of the risk factors but then can your method say how many sort of the risk factors are still missing for example? Would you conclude from this that we know most of the things that cause diabetes or could this method be used for finding novel biomarkers for example? Instead of looking at only 37 risk factors, if you looked at more do you think you would add a lot of additional information or do you think we already know sort of the bulk of what's increasing risk of diabetes? Jonathan:           I think that's really kind of ... You hit on the important point. This analysis was done using well-known risk factors but really I think the ultimate goal would be discovery. I think there is a lot more discovery to be had, and in particular, whether a risk factor may or may not account for a lot of risk, you might identify new biomarkers that may help you make an earlier diagnosis that can allow you to then perhaps modify the course of the disease. Jane:                   I know in this paper, as well as looking at diabetes, you were looking at ischemic heart disease and finding a different pattern to what you saw for diabetes. Jonathan:           Yeah. The conclusion from the diabetes was that diabetes that we're diagnosing clinically, that seems to be very similar to the diabetes phenotype that was identified in the Eric population. That didn't seem to be true of the ischemic heart disease where we actually saw that the risk factors, again, these 37 factors that we looked at, really the genetic measure didn't follow the epidemiologic measure nearly so well. We do see some important risk factors where they did follow each other well. For instance, high systolic blood pressure, high triglycerides increase your risk. Low HDL increasing your risk. Even smoking, which is interesting, how you smoke is actually genetically modulated, so we can measure smoking as a risk factor and see that smoking increases risk.                              There are some differences that caught us by surprise. For instance, LDL cholesterol, which is a strong known risk factor for ischemic heart disease, showed a genetic correlation of close to zero, suggesting that there is not a lot of shared genetics going on between modulators of LDL levels and ischemic heart disease in our population. That really caught us by surprise. Jane:                   Yeah, it's interesting. Of course, LDL is one of the risk factors that's most treated and that we're most aware of and we have a lot of different therapeutic options for treating LDL cholesterol. Do you think have we sort of reached the end of how much additional effect we can get from treating LDL? Are we already so good at treating LDL that any sort of additional therapeutic options down that avenue may not give us any additional gains in preventing disease or do you think there's some other explanation for why LDL did not appear to have a strong correlation? Jonathan:           I'd certainly like to think that we're great at treating LDL here at Vanderbilt and it certainly could be one contributor is that we've perhaps attenuated the genetic effect of LDL. Unfortunately, kind of with the nature of the dataset that I used, I didn't have information on use of statins and other drugs that could give a sense of whether that was an important effect modifier. What the patterns of associations also shows in our dataset, again, this association with low HDL, high systolic blood pressure, high triglycerides seem to be a big driver, is that we really might have a population where the metabolic, this syndrome really driven being overweight and obesity, is really an important driver of risk in our population. It's possible that we might represent this changing epidemiology of the disease.                              Our other thought was that maybe just LDL doesn't work as a phenotype but we actually looked at another outcome, peripheral artery disease, and actually found a pretty strong association with peripheral artery disease. I don't think that there's an inherent problem with the phenotype. I think it's an excellent question and I think it's something that we're still trying to figure out the answer to. Jane:                   Maybe for ischemic heart disease, maybe treating the sort of obesity, metabolic aspect may be more important for helping these individuals. Jonathan:           Yeah and I don't think our data supports stopping treating LDL but maybe it's possible that we can say we're doing a good job, at least in this particular population that we studied. Jane:                   Right, right. You've used this method and you sort of showed it really nicely with the ischemic heart disease and then the type II diabetes, and then in your more recent publication, you've shown how this can be applied to other phenotypes sort of in a more directed way. I'd love to chat about that a little bit. Jonathan:           Yeah. In this paper, it's really running that same experiment backwards. Again, because we're measuring risk across populations and we're doing it based on underlying genetics, which your genetic risk is determined at birth, so we can either start with risk factors or diseases and there's kind of no temporality in this particular study design. In the second paper, what we did is somewhat run the experiment backwards where we took a risk factor or a biomarker really, in this case it was the PR interval derived from the cardiac electrocardiogram, and we asked the question for what diseases does the genetic risk driving that disease did those same genetics also modulate the length of your PR interval?                              What we found is one, with atrial fibrillation, and one was with measures really related to adiposity. Genetic factors which tend to make you heavier prolong your PR interval. The surprising finding here was that actually genetic factors which tend to shorten your PR interval increase your risk of atrial fibrillation. This seemed to at least conflict with a little bit of what's been published in the epidemiologic literature where the association has gone in the other direction. That was an interesting observation. Kind of our bottom line that we came to is actually if you go through the literature as a whole, there's really a U shaped relationship between PR interval duration and atrial fibrillation risk. We think that the genetics might be contributing to that lower end of the U or the inverse relationship. Jane:                   Yeah, which is really interesting. You have to be in that sweet spot right in the middle of not too long and not too short. Jonathan:           Lifestyle factors. Again, obesity is always such a big driver of these things and metabolic phenotypes just tend to modulate a lot of these biomarkers. Jane:                   Right, right. Really interesting. For your next studies, maybe you'll be looking at other obesity related phenotypes I guess. Jonathan:           Yeah but also, as you alluded to earlier, really the next step is to now start exploring more novel biomarkers. These studies allowed us to use pretty well described phenotypes and biomarkers to give us a sense of expectation of the results that we might see but now really we hope to move on to more discovery and novel discovery. Jane:                   I think it's a really exciting method. It has a lot of promise and it'll be really interesting to see where you go next with this. Jonathan:           Yeah. Well, thank you for the opportunity to talk about it. Jane:                   Yeah, thank you so much.                              Hi, Navine. How are you doing? Navine:               I'm doing well, Jane. A couple of exciting papers have been published in both Circulation and Circulation Cardiovascular Genetics I see.  Jane:                   Absolutely. There's two that were published in Circulation in this month, so April 4th issue of Circulation that I thought were pretty interesting. They were sort of related to each other thematically. The first one is called Genetic Risk Prediction of Atrial Fibrillation and this was published by Steven Lubitz, Xiaoyan Yin, Emilia Benjamin and colleagues on behalf of the AFGen Consortium. What they did was they looked at variance associated with atrial fibrillation and they generated this AF genetic risk scores and then they looked at the association between this genetic risk score and incidence of atrial fibrillation in five prospective studies. That was almost 19,000 individuals. As well as looking at the relationship between this risk score and AF, they also looked at the relationship between the risk for stroke in a separate study of 500 stroke cases.                              What was sort of interesting is they were able to find that this genetic risk score was associated with the incidence of atrial fibrillation and it did add a little bit of additional discriminative power beyond just looking at clinical risk factors for AF but it was moderate. The addition of this genetic risk score didn't add a huge amount of additional predictive capacity.                              They did also find that this genetic risk score was associated with stroke. It's sort of an interesting example of using a genetic risk score to look at prediction of incidence disease and also sort of related diseases such as stroke. Navine:               Sure. I think genetic risk scores are useful when the effects of each individual variant are very low. Compiling comprehensive genetic risk score may add incremental value, and of course being able to predict the onset of atrial fibrillation is very valuable for many patients, as it's a common cardiovascular condition. Hopefully, this will be refined as we move forward. Jane:                   Absolutely. Actually, the second paper was sort of a similar application. This paper is called Common Genetic Variant Risk Scores Associated with Drug Induced QT Prolongation and Torsade de Pointes Risk. The authors here were David Strauss and Christopher Newton-Cheh and colleagues. They generated a genetic risk score variants that have been associated with QT intervals through sort of various genome [inaudible 00:27:54] association studies and then they did genetic analysis in a relatively small number of subjects, 22 subjects, but they looked at the association between this genetic risk score and the drug induced QT prolongation. They found that it actually explained quite a significant proportion of the variability in drug induced QT prolongation and it was a significant predictor of drug induced Torsade De Pointes.                              In this case, compared to the first paper where it had a relatively modest effect, they actually saw quite a good effect here where the addition of the genetic risk score was able to predict the reaction to the drugs. Navine:               Yeah, Jane. I think as genetic information for each individual patient is going to be increasingly available, as whole genome sequencing or whole [inaudible 00:28:52] sequencing or even doing [inaudible 00:28:54], the costs seem to be going down. If patients have a digenetic profile available, then compiling such genetic risk scores and then being able to apply them for individual situations would make sense. Then I think it could be more widely clinically applicable because people should realize getting genetic testing is just not getting genetic testing but it's a lifetime of information that's available as we are able to use this genetic information in various clinical conditions as we are seeing in these two papers. Jane:                   Absolutely and it actually relates back to the AHA statement that we highlighted in this episode where genetic literacy is so important. It emphasizes the fact that practitioners and healthcare providers need to be aware of genetic testing an be aware of the potential that it has so that when genetic data is available for patients it can be used throughout the whole lifetime of that patient. Navine:               That's great, Jane. I found an interesting paper that was published in this month, April 2017 Circulation Cardiovascular Genetics. It's volume 10 and it's titled Prevalence and Clinical Implications of Double Mutations in Hypertrophic Cardiomyopathy. The first author is Dana Fourey and the senior author is Arnon Adle. Essentially, what this study did was looked at all the hypertrophic cardiomyopathy in gene panel results that were available in 1,411 patients over a 12 year period and try to discern how many of these patients were genotype positive. It turns out that 19% of these patients, or 272 of the 1411, had pathogenic or likely pathogenic variance.                              The purpose of this paper was to see how many of these patients have so-called double mutations because having two pathogenic or likely pathogenic mutations in earlier studies have shown an earlier disease onset, more severe left ventricle hypertrophy, higher prevalence of advanced heart failure and increased risk of sudden cardiac death. They wanted to see if this was true in this large cohort of patients. It turns out that actually just 1.8% of that total population, or 25 patients, had such double mutations; meaning, two likely pathogenic or pathogenic mutations. Fairly small number of the total population.                              What was interesting was as they applied the latest American College of Medical Genetics and Genomics criteria, and remember this was done over a 12 year period, and what they decided to do was look at all these 25 variants and apply the latest criteria and see after this stringent overview whether these likely pathogenic or pathogenic variants held up. It turns out that of the 25, only one genotype actually held up to these criteria. Only one patient had these double mutations that were pathogenic or likely pathogenic, so 0.07% of the total population. The bottom line is, though it sounds interesting that if people have two likely pathogenic or pathogenic mutations they should theoretically have a worse prognosis and more severe disease, the reality is that this is unusual, and furthermore, when they compared these patients who had double mutations with those who had single mutations, they found no difference in these high risk features or premature death.                              It turns out that our knowledge is still evolving regarding having double mutations in hypertrophic cardiomyopathy. There's a nice accompanying editorial in Circulation Genetics April issue of this year if people want to read further into this article. Jane:                   It sounds really interesting. I'm thinking that that is a little related to the other paper that I wanted to talk about, which is not specifically cardiovascular related but I think they used an interesting approach that could be applied to cardiovascular conditions. The first Author is [inaudible 00:33:56] Cummings, last author Daniel MacArthur and this was published this month in Science Translational Medicine the 19th of April. The title is Improving Genetic Diagnosis in Mendelian Disease With Transcriptome Sequencing.                              What they did was they took muscle samples that had been collected from patients with a variety of rare mendelian conditions such as muscular dystrophies and myopathies. They decided to do RNA sequencing in these samples. These samples were from subjects and families that had previously been very difficult to diagnose. A lot of these subjects had already been subjected to whole exome sequencing or whole genome sequencing, and in many cases, they had been unable to find the [inaudible 00:34:43] mutation.                              They sequenced the RNA from the muscle and they compared it to control muscle RNA from [inaudible 00:34:53]. [inaudible 00:34:54] contains RNA data from hundreds of different individuals with healthy controls and they were able to filter the data from [inaudible 00:35:01] to get high quality RNA samples matched to the patient's age and BMI to some degree. They then compared the sequences to see if they could find aberrant splice variation or aberrant expression in the RNA samples from the patient samples compared with the controlled. They actually were able to find a lot of additional interesting causal mutations that were able to explain the diagnosis in subjects who were previously undiagnosed.                              Actually, in this sample of very challenging rare cases, they had an overall diagnosis rate of 35%. By identifying sort of aberrant splice patterns in these patient samples, they identified multiple causal variants that were not able to be identified through the usual means through whole genome sequencing or whole exome sequencing. They actually found 17 families where they were able to make a diagnosis where there had previously been none.                              Although this is in different conditions, not cardiovascular, but I think it highlights how sometimes using a different approach, for example doing transcriptome instead of genomic profiling in the disease relevant sample can give really interesting insight that you wouldn't get just from looking at the DNA sequence. Navine:               That's a fascinating approach. This is more like a genotype transcriptome correlative study after the transcriptome has been further refined based on so-called normal transcriptome and this way they were able to identify the functional significance of certain genetic variants based on what the transcriptome looks like in disease states. Jane:                   Yeah, absolutely. They were able to sort of actually link variants of unknown significance with the actual transcriptomic pattern and then highlight the variants that actually did have a causal effect on gene transcription. Navine:               Thanks for pointing that out, Jane. It could be easily applicable to cardiovascular disease. It'll be interesting to see if papers come out based on this study design. Jane:                   Yeah. I think so. Navine:               All right. We look forward to reviewing some more exciting papers next month. We'll be well into summer soon. Jane:                   We will. Great. Well, thank you, Navine. Navine:               Thank you. Jane:     That's all for this month. Thanks to Rick Andreasen at the Mayo Clinic Media Support Services for production assistance. Thanks, everyone, for listening. We look forward to bringing you another episode of Getting Personal Omics of the Heart next month.

Jane Ferguson:                Hello, and welcome to episode two of "Getting Personal: Omics of the Heart". I'm Jane Ferguson, an Assistant Professor of Medicine at Vanderbilt University Medical Center. This Podcast is brought to you by the Functional Genomics and Translational Biology Council of the American Heart Association.                                            If you're a current or prospective member of the American Heart Association but not yet affiliated with our council, I do encourage you to join us. FGTB is a vibrant council with a diverse membership spanning disciplines from basic research to clinical practice, with shared interests in genomics, precision medicine and translational research.                                            You can find out more by going to the AHA professional website at professional.heart.org and selecting FGTB from the list of scientific councils. If you're listening to this, you've obviously already figured out a way to access this Podcast. We do have several convenient options to make sure you never miss a new episode. You can stream each episode and find additional information on links to articles on the Podcast website fgtbcouncil.wordpress.com. You can also subscribe to the Podcast on iTunes or if you are an Android user, you can subscribe via Google Play. Just search for "Getting Personal: Omics of the Heart" and click, Subscribe.                                            In this episode, Kiran Musunuru talks to Erik Ingelsson about research from his group on epigenetic patterns in blood and how these relate to coronary heart disease, which was published in the February 2017 issue of "Circulation: Cardiovascular Genetics".                                            We highlight a recent AHA Science Advisory on merging electronic health record data and genomics, and Naveen Pereira and I discuss precision medicine and whether it can live up to the hype. Kiran Musunuru:             Hello. This is Kiran Musunuru. I'm on the faculty at University of Pennsylvania and it's my pleasure to represent the Functional Genomics and Translational Biology Council of the American Heart Association. Today I have the privilege of interviewing Dr. Erik Ingelsson who is Professor of Medicine in the Division of Cardiology at Stanford University School of Medicine. We're going to be discussing a very nice paper on which he is senior author that was published last month in "Circulation: Cardiovascular Genetics" titled "Epigenetic Patterns in Blood Associated With Lipid Traits Predict Incident Coronary Heart Disease Events and Are Enriched for Results From Genome-Wide Association Studies". It's all right there in the title. Erik, welcome. Erik Ingelsson:                 Thanks. Kiran Musunuru:             It's a pleasure to have you. Maybe you can say a word or two to introduce yourself and your research interests. Erik Ingelsson:                 Yeah, it's a pleasure to be on. Yes, as you said I'm a professor of Medicine at Stanford, an MD, PhD trained really in epidemiology but started to do genetics about 10 years ago. I've been most of my career in Sweden but moved to Stanford now about one and a half year ago. I'm doing broadly studies within omics and molecular epidemiology but also have a translational part where I do [inaudible 00:03:29] and model systems. Kiran Musunuru:             That's great. To the subject at hand, so I think we all appreciate how, with the completion of the Human Genome Project about 15 years ago now, genetics has really taken off. What's interesting is, over the last few years, there's been a bit of a shift in focus from genetics to the layer of regulation that lies right above genetics and that's epigenetics, so modifications of DNA and the proteins that are bound to DNA and how this interacts with genetic expression and then has consequences in terms of clinical traits and diseases.                                            What caught my eye about your study is that you're actually looking at epigenetic regulation of gene expression but not in a very traditional, one locus at a time or one gene at a time fashion, but really in a genome-wide fashion. Whereas, starting in 2005, we started to see genome-wide association studies. Now we're starting to see, just over the last few years, epigenome-wide association studies.                                            Personally speaking, one of my research interests is lipid traits. I thought it was very nice how you were able to apply an epigenome-wide association study to lipid traits and actually find some very interesting things. Why don't I start by asking you simply describe the main goals of your study. Erik Ingelsson:                 As you've already referred to, we wanted to look at variation in DNA methylation, which is one of the ways to look at epigenetics. I think either it's the most common way to look at epigenetics, at least if you want to do it genome-wide. We looked at variation in DNA methylation in relation to circulating lipid levels, and we did this through this epigenome-wide study and in whole blood derived DNA.                                            We did it with about 2,300 individuals from the Framingham Heart Study and from the PIVUS cohort, and then we had an independent external replication in about 2,000 additional individuals.                                            In addition to looking at these DNA methylation associations with lipids, we also wanted to look at these DNA methylation patterns in relation to incident coronary heart disease. We also wanted to integrate all of this with genetic variation, gene expression and also actually with metabolites through metabolomics. The whole idea here is trying to understand genomic regulatory mechanisms that link lipid measures to coronary heart disease risk. Kiran Musunuru:             That's one thing I really liked about this paper, how you really took it all on. It wasn't just one particular type of omics analysis. It started with epigenomics but then you really went the extra mile, I thought, to connect it to genetic variation, and then to disease, and to metabolomics and so it was very comprehensive that way. Why don't we discuss the actual findings. You actually found quite a bit in your analysis, didn't you? Erik Ingelsson:                 Yeah. I think some of it were already actually in the title. We did, as I said, several different layers of things. The first thing was really to look at methylation patterns. We looked at CpG sites across the whole genome, and we identified almost 200 such sites that were different lipid levels in the discovery but then going to the replication stage, we had a little bit more than 30 of them being replicated and 25 of them had never been reported in relation to lipids before. That's one layer, so it is new associations. A lot of the genes that were then enriched they were involved in lipids and amino acid metabolism so it makes a lot of sense biologically.                                            There is the one example of an interesting finding there with ABCG1 that we perhaps can discuss a little bit later. Other larger things that we found was that there was a lot of cis-methylation quantitative triglycerides so that means that there were a lot of genetic variants that were associated with these methylation levels. In fact, actually, 64% of all of the CpG sites that we found, they also had genetic variance determining the level of the methylation. So quite large fraction being genetically determined. We also- Kiran Musunuru:             That's actually quite interesting because typically when you hear it in the lay press or what not about epigenetics, they tend to equate epigenetics with more environmental influences. It's a simple dichotomy or simplistic dichotomy of your genes are what you're born with but then epigenetics is the way that environment actually modifies your genetics in ways. But what you're suggesting from your findings is that it's actually genetic variation itself that could be directly responsible for epigenomic variation, which then would have effects on gene expression. Erik Ingelsson:                 I agree. I think we're seeing a shift a little bit in this field. Again, my background is not really within that genetics field so I'm a little bit on the side here but what I see is that it's come more from an approach or focus really on inherited epigenetic changes so studies in animals, primarily, I guess a lot, but also in some human studies so more on that level to something that had been, as you mentioned, a lot of focus on environment causing methylation changes and now almost more into a focus of gene regulation and then gene expression and that focus.                                            Perhaps the ENCODE project and the Epigenome Roadmap and those projects have moved this field a little bit towards more focus on gene regulation and gene expression and that's kind of a part, a linking variation to gene expression. I think we're seeing a shift a little bit in that field. Kiran Musunuru:             That's very interesting. Can you give an example of a particular locus or particular gene where epigenetic regulation really seems to be playing an important role, not just with respect to lipids but even, perhaps, connecting to disease. I think you'd mentioned ABCG1 very briefly. Erik Ingelsson:                 That's actually a pretty interesting locus. It's been recorded in the past, as well, in relation to methylation but we linked it all together. Basically, we see this intronic variant here where the minor allele is associated with increased methylation at the CpG site in that 5 prime UTR region of this gene of ABCG1 and then so that minor allele leads to increased methylation. It also leads to decreased expression of ABCG1 in blood. I think that makes sense. Quite often in the past, people have recorded that increased methylation should decrease expression.                                            As we see that, we also see an effect on triglyceride levels and HDL levels as well and, interestingly, also, on the risk of coronary heart disease. In addition, also, associations with several of the metabolites, so single myelins and[karomites 00:11:40] which have also been implicated in coronary heart disease in some prior studies. It all comes together quite nicely at this locus where you have a minor allele increasing methylation, decreasing expression, increasing triglyceride levels and increasing the risk of coronary heart disease along with increases in some of the metabolites that also have been linked to coronary heart disease. Kiran Musunuru:             Wow. Fascinating. Erik Ingelsson:                 Yeah, I think it's pretty interesting, actually. We could link it all together in the study. Kiran Musunuru:             That's very nice. Another aspect of this study that caught my attention is that you really did it in a fairly rigorous way. You had your discovery cohorts in which you did the initial screen or the initial association study, but then you also had replication cohorts where you were then able to go independently test your findings and then accrue more evidence or lack of evidence for replication in the ones for which there was evidence of replication, those are, obviously, much more stronger results.                                            I expect that we have among our listeners trainees who might be interested in hearing more about how you were able to assemble so many different cohorts to be able to get this study done. Erik Ingelsson:                 I think that's an important question. I would say that it goes back a little bit to the development that we've seen in genomics in the past 10 years. People coming in from gene studies to GWAS realizing that you really need to work together both because the science is better but also just if you want to establish any robust findings that can be replicated, you need to combine the data.                                            I think we've seen that for GWAS clearly, but I think we're starting to see that also for other [inaudible 00:13:28] approaches as we move forward. Because all of these approaches are prone to false-positives so if you just do your analysis in your own data, then you're more likely to report false-positives and you need replication.                                            I think we're lagging behind a little bit for epigenomics and other omics methods, but we're truly starting to see this happening also in other omics fields. I think, in a sense, the field is prime for collaboration and then I'm talking about the broad, molecular epidemiologist field or the people having cohorts and this kind of data, they're all used to working together from the GWAS era and also realize the need for it. I think for that reason it's usually not that difficult to get people together.                                            Then how do you do it practically? It's easier if you know people, of course, since before and that's probably more common nowadays than it would have been 15, 20 years ago because you always used to work with people in the GWAS era and you can even add a junior level set up these collaborations because you might have been involved in some other collaboration before and know some postdocs in some other labs, etc.                                            That might be one way to go about but the other thing is also that you have an interest in a certain phenotype and then you reached out to people that you think have the data. You can know about either from other publications and other phenotypes or on the same phenotype or just by word of mouth you know it since you've met people at conferences, you've seen some poster on the same phenotype, etc.                                            I would say that people, in general, are very open to collaborations, and I think we've seen that change and shift of the past 10 years. I think we see it now also for other omics methods, and I definitely do think that's the way forward. To report more robust findings, in general. Kiran Musunuru:             In closing, I'd say that seeing your study and seeing the very nice results, it seemed very promising with respect to what we're going to find going forward and doing epigenetic studies. Do you see more of this happening in the near future? Maybe even what happened with GWAS where it just got increasingly larger and larger studies and finding more and more results as these studies became increasingly powered.  Erik Ingelsson:                 Yeah, I think so. I think for epigenomics, as with some other omics, I think we will see the same development that we saw with GWAS, which is the people start to publish in relatively small settings with perhaps a few discovery cohorts, a few replication cohorts, and that parts happen kind of independently of each other. Then the next stage is you're grouping together and you're starting to involve other people as well and these consorts get larger and larger.                                            I think the value of this data can be exponentially increased if you can actually combine it with other data sets. We've seen that in genomics. There's a large return on your investments by collaborating with other people. I definitely do see the same kind of development happening here, as well. Kiran Musunuru:             Well, Erik, thank you so much. That's all the time we have for today but we greatly appreciate your taking the time out of your busy schedule to discuss with us this really nice paper that you and your colleagues published very recently. I would encourage all of our listeners to go take a look at the paper themselves. As I recall, this particular paper is open access so it should be freely available to anyone who is interested. Is that correct? Erik Ingelsson:                 Yes, it's an open access. And thanks, Kiran. It was a pleasure. Kiran Musunuru:             Thank you very much. Jane Ferguson:                An AHA Science Advisory from the FGTB Council published in 2016 focused on the challenges and the potentials in merging electronic health data with genomics data to advance cardiovascular research. Jennifer Hall, John Ryan and colleagues published this on behalf of the Functional Genomics and Translational Biology Council as well as the councils on clinical cardiology, epidemiology and prevention, quality of care and outcomes research and the stroke council.                                            As electronic health records have become ubiquitous in medical practice, there is an opportunity to utilize existing stored data and add new types of data to the EHR to facilitate research through EHR-coupled biobanks and to improve patient care through the use of precision medicine approaches based on genomic and clinical data stored in a patient's record.                                            While logistical and ethical considerations remain, this is an area with great promise. You can read more in the Science Advisory published in the March 2016 issue of "Circulation: Cardiovascular Genetics", which along with all the papers mentioned in this episode, are linked on the Podcast website at fgtbcouncil.wordpress.com                                            This Podcast has the focus of precision medicine, and I saw an interesting back and forth in the JAMA comments section about the hype of precision medicine. I think even those of us who are fond of precision medicine would agree that there's probably a certain amount of hype surrounding it.                                            There was this interesting opinion published in JAMA last October addressing the question of, will precision medicines really have an impact on population health? I think there is some important points that really to improve population health, there may be other options rather than precision medicine, which may be more focused on the individual or on certain subgroups, which may not actually raise the broad population's health.                                            But then there was response to that published in JAMA in January, which was arguing against it. I thought it would be some interesting thing for us to talk about a little to see do we agree? Is this over-hyped? Or is precision medicine really something that could fundamentally change population and individual level of health in the future? Naveen Pereira:              I agree. There seems to be a tension between precision medicine that stresses on the individual and using omic technology and molecular markers to determine individualistic response or characteristics and population health in general, which looks at population trends. Both of them in principle and philosophy appear to be deferring fields. I guess the question is how do we integrate both of them to improve overall, not only individual but large population health? Jane Ferguson:                I think there's probably some disconnect maybe between what people think of as precision medicine and what sort of things it includes because I think our first thought could be that precision medicine is very much based in genetics and genetic risk scores, using genotype as a way to predict an individual's response to a drug or their risk of disease.                                            I think maybe one of the things we have to think about with precision medicine is to encompass all of these additional omic technology. So, yes, genotype alone is unlikely to really affect population health on a broad scale, but when you add in gene expression and proteomic biomarkers, metabolomics and microbiomes, I think then we do start to get to a point where it's mathematically complex but it would theoretically be possible to predict risk and implement precision medicine approaches, even on a large-population scale. Naveen Pereira:              Right. One of the things I've always wondered is should we move away from our traditional classification of disease? For example, hypertension. Is all hypertension the same? We know it's not, it's such a heterogeneous disease process. Are we still stuck in the 19th century where we think of hypertension as blood pressure? Should we move away from that? Should we integrate all this great input from omics technology and phenotype hypertension is a better disease process, which would, perhaps, improve outcomes. Jane Ferguson:                I think that's a great point. Honestly, probably a lot of the challenge in this is just us in thinking about things differently. You're right. We're very used to thinking of hypertension and we recognize it, we treat it. But it really is just ... The underlying causes of hypertension in the individual may be very different and it may need very different treatments.                                            I think a paradigm shift is probably needed in thinking about a lot of these complex diseases. Diabetes is another one where really that's the causes and then the way it progresses in different individuals is probably really distinct subtypes of disease rather than being one broad disease that we can classify as such. Naveen Pereira:              Exactly. And that would enable, perhaps, more dramatic treatment effects, too. I keep thinking of the example in cystic fibrosis where the genetic mutation in the cystic fibrosis gene actually proved that a certain therapy for cystic fibrosis in those patients who carry that gene mutation had a dramatic response. It didn't take tens of thousands of patients to demonstrate that effect but it took several hundred patients. Jane Ferguson:                That's a great point. I think if we're accurately substratifying individuals so that we really are looking at people who really do have the same underlying causes of disease, then I think we will have a lot more power to see effects in smaller numbers of people and we can move away from these huge GWAS of hundreds of thousands of people as being necessary to find effect. Naveen Pereira:              In fact, what we could do is take some of the knowledge from precision medicine and apply it at a population level and, hence, perhaps what we need to do is integrate the two disciplines better and people need to speak to each other more often. What do you think, Jane? Jane Ferguson:                Absolutely. I think that is key. We're used to thinking about our own little narrow field and focusing on that but I think integration and finding good ways for it. The humans to integrate and also to integrate the data mathematically, I think that will be key. I think that certainly caveats, I mean, these approaches may not find everything but I think there's definitely a lot of promise that has not yet been fully exploited. Naveen Pereira:              Absolutely. Jane Ferguson:                Last time we talked, we were talking about a paper that used gene expression profiling in CAD. I think you found a really interesting paper for us to talk about this month looking at gene expression profiling but in the setting of heart transplant and heart transplant rejection. Naveen Pereira:              Yes, Jane. It's interesting to see increasing number of publications now looking at gene expression arrays and profiling for various disease states. In the March 7, 2017, issue of "Circulation", there was a very interesting paper looking at gene expression profiling and complementing the diagnosis of antibody-mediated heart rejection.                                            Just as a background, the two types of heart rejection that heart transplant recipients can have, one, is cellular rejection which we're seeing now less often due to improvements in immunosuppression; the other type of rejection is antibody-mediated rejection most often caused by anti-HLA antibodies that are directed towards the donor or what we call as donor-specific antibodies.                                            This paper, the first doctor is Alexandre Loupy and he is from INSERM Institute in Paris, France and the senior author is Philip Halloran who is from Edmonton, Canada. What they essentially did was look at 617 heart transplant patients from four French transplant centers. Out of these 617 recipients, there were 55 recipients who had antibody-mediated rejection.                                            They did a case control study, the controls being 55 recipients who did not have antibody-mediated rejection. They analyzed 240 heart biopsies in total. Unfortunately, even in this modern era, we still perform heart biopsies traditionally through the internal jugular route and endomyocardial biopsies and these biopsies are then analyzed for features of antibody-mediated rejection.                                            The International Society of Heart and Lung Transplant has standard definitions by consensus as to what is antibody-mediated rejection and their various features histopathologically and by immunostaining. We also use donor-specific antibody detection in the serum to finally make a diagnosis.                                            What this group really did was analyze these heart biopsies by performing expression microarrays and they found a very distinctive pattern in patients who had antibody-mediated rejection by traditional criteria. The gold standard was the traditional criteria, and they used the gene expression pattern to correlate it with the gold standard.                                            They found certain selective gene sets that they call antibody-mediated rejection gene sets. It involved transcripts of natural killer cells, endothelial cell activation, macrophages and interferon gamma. The area under the curve that they found using these gene expression patterns for these four gene sets was greater or equal to 0.8 which is quite good. This gene expression pattern was then validated in a separate cohort of patients from Edmonton, Canada.                                            It's an interesting manuscript, which essentially looks at using gene expression profiling in addition to traditional histopathological determination for a relatively common type of rejection in heart transplant patients to consolidate the diagnosis and give insight into pathophysiology.                                            But some of the questions that arise are we still submit patients to endomyocardial biopsies so this does not supplant the need to perform endomyocardial biopsies because this was looking at expression arrays within heart tissue. We are still struggling with the gold standard, the histological diagnosis of antibody-mediated rejection as to what it really means in patients, for example, who do not have dysfunction of the graft, or a low ejection fraction. Useful in many ways. I think it adds to the overall knowledge of this phenomena, but it may not change clinical practice significantly. Jane Ferguson:                That's really interesting. It's exciting but, you're right, we are subjecting people to heart biopsies isn't necessarily going to be a good way to monitor rejection or be able to predict in advance who is going to suffer rejection versus not.                                            I think it's definitely a very interesting study and I think, the fact that they discovered these genes that which were then validated, may give some additional insight into the underlying biology, which may help us develop new ways to start thinking about treating this unmitigating rejection. Naveen Pereira:              Right and it would be interesting to see how this corresponds to peripheral blood gene expression and whether there's an early, noninvasive way of detecting rejection. I know the Stanford group in the past has looked at circulating DNA from the donor heart, analyzed by peripheral blood, the same thing that's done in efforts to its cancer detection to see if we can pick up rejection by just a blood draw instead of doing endomyocardial biopsies. Jane Ferguson:                Yes, definitely. I wonder if this group collected any blood or is this something they may want to do in the future because I think that would be a really interesting addition to this study. Naveen Pereira:              Absolutely. Jane Ferguson:                Well, it's been great talking to you as always, Naveen, and we want to say special thank you to Rick [Andraysen 00:31:10] for the Mayo Clinic Media Support Services for helping us with this Podcast. Naveen Pereira:              Always does a great job. Jane Ferguson:                Absolutely. We'll thank everybody for listening and we'll look forward to being back with you next month with more topics related to precision medicine and getting personal with omics of the heart. Naveen Pereira:              Lot of excitement next month, Jane. Thank you.

Jane Ferguson:                Hello, and welcome to episode one of Getting Personal: Omics of the Heart, a podcast from the Functional Genomics and Translational Biology Council of the American Heart Association. I'm Jane Ferguson, the current chair of the FGTB Professional Education and Publications Committee. This monthly podcast will bring you up to date with the latest in genomics, other omics technologies, and precision medicine as they relate to cardiovascular and metabolic disease.                                            In each episode, we'll give you an overview of some of the latest research to be published, and delve deeper into topics of particular interest. Whether you're a clinician, researcher, genetic counselor, or other healthcare or science professional, we hope these podcasts will be informative, and help you stay up to date with the latest developments in this exciting field.                                            In this episode, my colleague Naveen Pereira talks to Amit Khera about his recent publication with Sek Kathiresan and colleagues in the New England Journal of Medicine entitled Genetic Risk, Adherence to a Healthy Lifestyle, and Coronary Disease, and we highlight a recent AHA scientific statement on the use of genomics. But first, Naveen and I will give you a round up of some interesting papers from the past month. Naveen Pereira:              So Jane, there was this really interesting paper in the American Journal of Medicine whether we can use gene expression signatures along with other clinical covariates to predict the presence or evaluate whether symptoms are suggestive of obstructive coronary artery disease. Jane Ferguson:                Yes. This paper was published online on the 16th of December 2016. It comes from Joseph Ladapo, Mark Monane and colleagues. They carried out this study in 566 patients from the PRESET Registry, which enrolled stable, nonacute adults presenting with typical or atypical symptoms that were suggestive of obstructive coronary disease.                                            What they did was calculate an age/sex/gene expression score, or ASGES score. They included gene expression, which they measured in a blood sample collected in a PAXgene Tube, and this score ranges from 1 to 40. They've previously validated this, and a score less than or equal to 15 indicates that a symptomatic patient is very unlikely to have obstructive coronary artery disease. The genes they measured include 23 genes that are selectively expressed in circulating neutrophils, NK cells, and B- and T-lymphocytes. Naveen Pereira:              So really, this expression reflects inflammation, and the hypothesis being perhaps these inflammatory markers are very indicative of the presence of obstructive coronary artery disease, or plaque rupture I guess, huh? Jane Ferguson:                Yes, exactly. What they actually found was that individuals with high scores were referred to cardiology or advanced cardiac testing at far greater rates, and then even of subjects with low scores who did undergo additional testing, none of them had any detectable abnormality. Then, in subjects with high scores who did undergo further testing, 14% had abnormal findings. So after a year of followup, 1.2% of patients with an ASGES score below 15 had an adverse event, compared to 4.5% of those with elevated scores. Naveen Pereira:              So a fairly high negative predictive value, huh Jane? Jane Ferguson:                Right. Right, exactly. Naveen Pereira:              Did you find any limitations, Jane, in this study? Jane Ferguson:                There were some. Well firstly, it's worth noting that the score itself, and this test, has been developed by CardioDx, and a number of authors on this manuscript are affiliated with CardioDx. In addition to that, they did not include a control group in this. That certainly is somewhat of a limitation, but the authors say that this is probably still useful, and it may have some clinical utility in guiding decision making for patients with obstructive CAD. However, whether or not this is actually true will probably require some additional testing. Naveen Pereira:              So quite a foray into using this perhaps in the emergency room or in hospital. So I guess our audience should look out for this in the American Journal of Medicine, December 2016. Jane Ferguson:                Yeah. Naveen Pereira:              So there was another paper that we kind of thought was interesting, Jane, from the European Heart Journal. Jane Ferguson:                Yes, exactly. This comes from Jozef Bartunek, and Andre Terzic, and their colleagues, and they were reporting this on behalf of the CHART Program. Naveen Pereira:              So this was published on January 15, 2017. Jane Ferguson:                Yeah. This was a prospective, randomized, double-blind, sham-controlled trial, which was the Congestive Heart Failure Cardiopoietic Regenerative Therapy, or CHART-1 trial. In this trial, they were aiming to test safety and efficacy of delivery of cardiopoietic cells. They recruited subjects who had symptomatic ischaemic heart failure, and they consented to bone marrow harvest and mesenchymal stem cell expansion. They ended up randomizing 315 subjects.                                            They received cardiopoietic cells delivered endomyocardially by a retention catheter, or either a sham procedure. The outcome that they were looking at was a hierarchical score, which is assessed 39 weeks post-procedure. That score comprised all-cause mortality, the number of worsening heart failure events, the Minnesota Living with Heart Failure Questionnaire, a difference in the six minute walk test, change in left ventricular end-systolic volume, and change in left ventricular ejection fraction. So it was interesting. They found a neutral effect on the primary end point, but they did find some evidence of benefit in subgroup analyses, which were based on baseline heart failure severity. Naveen Pereira:              But and this was not designed to show efficacy, because it was primarily a safety trial. Is that right, Jane? Jane Ferguson:                Yes, exactly. Naveen Pereira:              Right. Jane Ferguson:                Overall, they found that there were no indications for concern regarding safety, so I think they've shown that certainly this is a technique that is safe and is well-tolerated, and I think it's really quite exciting. Future studies that are adequately powered, particularly looking at subgroups of individuals, may actually identify patient populations that would derive particular benefit from cardiopoietic cell therapy. Naveen Pereira:              Fascinating, so it'll be interesting to see what the Phase III clinical trial will show. Overall, a new foray into regenerative medicine. Jane Ferguson:                Yeah, yeah. Really interesting. Naveen Pereira:              Hi everybody. My name is Naveen Pereira. I'm from the department of cardiovascular diseases at Mayo Clinic in Rochester, and on behalf of the Functional Genomics and Translational Biology Council of the American Heart Association it gives me great pleasure to interview Amit Khera. We are going to be discussing this very exciting paper that was published in The New England Journal of Medicine on November 13, 2016, titled Genetic Risk, Adherence to a Healthy Lifestyle, and Coronary Disease. Amit, welcome. We are so glad you could make it. We really appreciate you doing this for us. Amit Khera:                      Naveen, thank you so much for having me. It's a real pleasure. By way of introduction, as you said my name is Amit Khera, I recently joined as a staff cardiologist at Massachusetts General Hospital in Boston where I see both general cardiology patients and also work in the prevention center. But one of the things I've noticed is that many of us have heard a lot about precision medicine, and how we can incorporate genetics into some of our clinical decision making, or risk stratification. So I've really been working with Sek Kathiresan at both Mass General and the Broad Institute to get training in both genetics to complement some of the clinical medicine aspects in order to help us get at some of those questions. Naveen Pereira:              Fantastic. Amit, what got you interested in genomics? Amit Khera:                      Sure. Well, you know for a complex disease like coronary artery disease, and risk of a heart attack, we've really known for a long time, like since the 1960s that there is a familiar pattern, meaning that if your brother or your father had a heart attack at a young age, your risk of having one is increased by almost a factor of two. It's really been only recently that the technology has allowed us to get at those questions, and really isolate the exact genetic determinant.                                            So really in the last 10 years, we've been able to identify a large number of variants that influence an individual's risk of coronary artery disease. So it really an opportunity to be in a place where the technology was coming along, where we have discovered all these variants, clinical medicine of course has come a long way since the 1960s as well. So the idea was to really put these two bodies of work together, and see what we could come up with. Naveen Pereira:              Yeah. This is very exciting. Amit, I completely agree with you. In our clinical practice we see patients with strong familial history of coronary artery disease, so certainly inheritance has been suspected for some time, and in fact genome-wide association studies have been done to identify loci for coronary artery disease.                                            As you know, the effect size of these individual variants have been small. And so groups have got together to form genetic risk scores, where they take kind of an aggregate of the effect of these individual variants, and we think this is more helpful. And this is what you did for your paper, so can you describe to us a little bit about how you derived the genetic risk score that you applied in this great paper? Amit Khera:                      Sure. The first aspect of our paper involved proving basically that we could quantify someone's genetic risk for having a heart attack. So in order to do that, as you said, we took advantage of a number of previously published genome-wide association studies. There are about 50 genetic variants all across our genome and different chromosomes that we know are strongly linked to coronary artery disease from a statistical standpoint, but actually might only have a very modest impact on coronary disease.                                            So let's say any individual could have a maximum of 100 risk variants. Now, some people might have inherited just by chance 80 variants, and other people might have inherited only 20. So we basically genotyped, meaning measured all 50 genetic variants in a large number of people, and then we said, "Those who are in the top quintile," meaning the top 20% of the genetic risk score, we're going to say "those people are at high genetic risk." And by contrast, if you're in the lowest quintile, we said, "Okay, those people are at low genetic risk." Naveen Pereira:              Right. Amit Khera:                      Then the question became okay, well does that categorization actually predict your risk of having a heart attack? So in order to do that, we analyzed over 50,000 individuals from three different prospective cohort studies, and what we found actually was that if you compare the high genetic risk to the low genetic risk people, their risk for having a coronary event over prospective follow up was increased by about 91%, meaning almost two fold. Naveen Pereira:              Wow. Oh, that's amazing. So using the genetic risk score, you could almost predict a doubling of the risk for coronary events. That's fantastic. Can you describe these populations briefly, Amit? Who are these people that you applied the genetic risk score to? Amit Khera:                      Sure. So we took advantage of three prospective cohort studies. The first was a Atherosclerosis Risk in Communities study, and that was a community based population of about 8,000 people. The second was the Women’s Genome Health Study, over 20,000 women who were originally recruited as part of a randomized control trial, and the third was the Malmö Diet and Cancer Study, which again had more than 20,000 individuals.                                            The really nice thing about these studies was that they were asked questions in a similar way, and they were followed ... in each case, participants were followed for about 20 years. So we really had a long time to observe what happened to these folks over time. Naveen Pereira:              So these are really longitudinal cohorts, not specifically disease oriented cohorts, but just community based, Amit? Amit Khera:                      That's exactly right, and in fact none of the individuals had coronary disease at baseline. They were all disease free- Naveen Pereira:              I see. Amit Khera:                      ... and then we followed them over 20 years to see who developed the coronary artery disease and who did not. Naveen Pereira:              So this is really applicable to the general population. Amit Khera:                      I do believe that these risk estimates would for sure hold true. Naveen Pereira:              Okay, wonderful. So Amit, you know you have the genetic risk score for coronary artery disease, and you have some great longitudinally followed population based cohorts, and you were studying a specific phenotype, so can you describe to us the phenotype? Amit Khera:                      Well, the primary outcome phenotype was incident coronary events, and those were all adjudicated by different committees, but it basically involved individuals who had either a new heart attack or myocardial infarction, they had to have one of their vessels either stented or bypassed via revascularization, or in fact it was determined that they died from coronary artery disease. So that was the outcome which we were trying to predict. Naveen Pereira:              Amit, let's get straight into it. What did you find? Amit Khera:                      So as a preventive cardiologist, I often see patients in my clinic who come to me and they say, "You know, almost everyone in my family has had a heart attack." Oftentimes at a very young age, and in some cases that can lead to almost a sense of determinism, where they feel like maybe they are unable to control their fate. So our primary question was a really a pretty simple one, which is to what extent can a healthy lifestyle offset someone's genetic risk of coronary disease.                                            So as I mentioned, we had a way of quantifying someone's genetic risk, and then we next said, "Okay, we want to quantify someone's lifestyle risk." So for that we kept it pretty simple. We had four criteria of what makes up a healthy lifestyle. First, no current smoking. Second, avoiding obesity. Three, regular exercise, and fourth, adhering to a healthy diet. And we said, "Okay, if you have at least three out of those four," we gave you a pass and said "you had a favorable lifestyle." Now if you had only zero to one out of those four, you had an unfavorable lifestyle.                                            One of the interesting things was that actually the genetic risk and the lifestyle risk actually were totally independent. There was no association for example between those who had high genetic risk and what their lifestyle was. So it really reinforced longstanding views that genetics and lifestyle are really independent axes of someone's individual level of risk. Now- Naveen Pereira:              So both, Amit, both could contribute to your individual risk for coronary artery disease? Amit Khera:                      Exactly. As I mentioned, the high genetic risk versus low genetic risk, there was about a two fold difference in risk, and we saw an almost identical pattern versus a favorable lifestyle versus an unfavorable lifestyle. There was about a two fold risk [inaudible 00:17:08]. Naveen Pereira:              Interesting. Amit Khera:                      Then that got us to the next question, which is to say if we analyze only those at high genetic risk so everyone had a similarly increased degree of genetic risk, to what extent could that risk be offset by a favorable lifestyle? This really gets back to the questions and the conversations we have with our patients who have a family history all the time. What we found there I think was a nice message, was that if you are at high genetic risk, you could actually decrease your risk by about 50% if you adhered to a favorable lifestyle, as compared to those with an unfavorable lifestyle.                                            So for example, when we looked at it in absolute terms, in terms of a 10 year risk of having a coronary event, in one of the cohorts, those with a high genetic risk but an unfavorable lifestyle had about an 11% chance of having a coronary event, versus if you had the same high genetic risk but a favorable lifestyle, your risk was only about 5%. And we saw that, a very consistent pattern across all the cohorts and all the categories of genetic risk, that those who had a favorable lifestyle ... the risk was decreased by about 50% in those with a favorable lifestyle. Naveen Pereira:              So that's fascinating, Amit. When physicians see a patient who have a really strong history of coronary artery disease within the family, and come up to you and say, "Doc, am I destined to have a heart attack?" You know, now with the availability of genotyping, with direct to consumer testing, people can find out their genetic risk. So they may not necessarily be doomed. Their fate is not predetermined. What you're suggesting is that fate can be modifiable. Amit Khera:                      Right. I think certainly for coronary disease your DNA is not your destiny, at least for these common variants. I think we provide evidence that really lifestyle factors powerfully modify your risk, really regardless of your genetic risk profile. Naveen Pereira:              So Amit, can we make any recommendations based on the results of your paper? Amit Khera:                      Well, I think ... The American Heart Association has really endorsed these four lifestyle criteria as a way of improving the population's health in the population as a whole, and I think actually that our results actually support that. Which is to say that this really supports the fact that these healthy lifestyle parameters are critically important for everyone, and I think that's a good starting point.                                            Genomic medicine is actually in its early days, but really what we hope to do is first to identify individuals, a subset of the population, who are at increased risk for a disease like heart disease, and I think we've shown that we can actually do that reasonably well. Like 20% of the population has a double risk. And the second part is actually to disclose this risk to both the patients and their providers in a way that's meaningful. And third, is actually demonstrate that we can actually implement the therapy to mitigate this increased risk.                                            So I think we, in this paper, we provided evidence that a healthy lifestyle can mitigate that risk. Papers from our group, both published and some in press, have actually demonstrated that taking a statin can also powerfully modify this increased risk. And you might imagine that there may be other interventions that ... especially if an intervention has increased risk, you really may want to target it to those people who actually ... if a medicine has increased side effects, you may want to target it to those at the highest risk. I think that, you know, this polygenic risk score does provide at least one way of stratifying people into those high risk groups. Naveen Pereira:              Yeah. Amit, really impressive results, 50% relative risk reduction in a high genetic risk population. You make a compelling argument. Obviously, however, this is not a prospective randomized clinical trial. It's really hard to do these. You had the advantage of well designed cohorts to study this in a cross sectional way. We don't know how these behaviors change. So these are some of the limitations, but the results are quite compelling, and contribute to the literature. Any other comments, Amit? Anything else that we should take home here? Amit Khera:                      No, I think as you said, there are some limitations. I think our really goal was to lay the foundation for future efforts where we really think about what the optimal way is for genetic information to be integrated into routine clinical practice to help prevent disease, and that's really what our group is planning on focusing on for the future years. Naveen Pereira:              We look forward to hearing more exciting results from your laboratory, Amit. It's been a pleasure. We should end I guess with a quote from William Shakespeare, "It's not in the stars to hold our destiny but in ourselves." Correct? Amit Khera:                      Thank you very much. Sounds great. Naveen Pereira:              Thanks, Amit. Jane Ferguson:                So as we just heard from Naveen and Amit, the combination of genetic risk and modifiable lifestyle parameters are crucial in determining CAD risk. A recent AHA statement from the FGTB Council focused on this topic. The statement, entitled Nutrigenomics, the Microbiome, and Gene-Environment Interactions: New Directions in Cardiovascular Disease Research, Prevention, and Treatment, focused on how dietary and genetic contributors to disease have been studied in the past, and how emerging omics technologies can be used to rapidly advance these fields.                                            Genomics, transcriptomics, metabolomics, proteomics, lipidomics, epigenetic profiling, and metagenomic characterization of the microbiome can all be used alone or in combination to better understand mechanisms underlying gene-environment contributions to disease. While the ultimate goal would be the development of improved therapeutic options, including personalized and precision approaches, a considerable amount of research remains to be done before this goal can be clinically implemented. You can read this statement in the June 2016 issue of Circulation: Cardiovascular Genetics. Naveen Pereira:              So, Jane, this has been an exciting first podcast. I really look forward to doing more with you. Jane Ferguson:                Yeah. I think this is great, so thank you everyone for listening, and happy heart month. We will look forward to bringing you a podcast again next month. Naveen Pereira:              Thank you.