Podcasts about mendelian randomization

Send us a textIn this episode, we talk to Prof. Roy Baumeister about the psychology of alcohol use and addiction. We cover topics such as why people drink, the effects of alcohol, the role of emotions such as guilt, and how self-control works. Prof Baumeister is one of the most eminent living psychologists with over 40 books and 700 scientific publications. His book "Willpower: Rediscovering the Greatest Human Strength" (with John Tierney) was a New York Times bestseller.Show notesShould alcohol research say more about the pleasure of alcohol use? A recent article published in the journal of Addiction prompted a range of responses and further comment. In discussing whether harmful effects of alcohol use are over-estimated, I mention studies using Mendelian Randomization. One of the studies in East Asian populations where genetic 'flushing' effects have been used to test whether risks of alcohol consumption providing evidence for there being no protective effects of moderate drinking.A recent meta-analysis looked at how different quality studies appear to account for different findings around low level alcohol consumption, thus explaining the supposed 'health benefits' of low consumption.  Support the showIf you are interested in one-to-one support for your drinking with Dr James Morris, contact him at DrJamesMorris.com For more episodes visit https://alcoholpodcast.buzzsprout.com/Follow us at @alcoholpodcast on X and Instagram

A new study claims that vitamin D can treat multiple sclerosis. But is this trial groundbreaking or an outlier? Dr. Chris Labos finds that the answer is far more complicated than a simple “yes” or “no”. Become a supporter of our show today either on Patreon or through PayPal! Thank you! http://www.patreon.com/thebodyofevidence/ https://www.paypal.com/donate?hosted_button_id=9QZET78JZWCZE   Email us your questions at thebodyofevidence@gmail.com.   Editor:    Robyn Flynn Theme music: “Fall of the Ocean Queen“ by Joseph Hackl Rod of Asclepius designed by Kamil J. Przybos Chris' book, Does Coffee Cause Cancer?: https://ecwpress.com/products/does-coffee-cause-cancer   Obviously, Chris is not your doctor (probably). This podcast is not medical advice for you; it is what we call information. References: The D-Lay MS randomized trial:   https://jamanetwork.com/journals/jama/article-abstract/2831270 The Mendelian Randomization study of vitamin D https://www.neurology.org/doi/10.1212/NXG.0000000000000097 The early 2012 study: https://pubmed.ncbi.nlm.nih.gov/22354743/  The SOLAR study:  https://pubmed.ncbi.nlm.nih.gov/31594857/  The CHOLINE  study: https://pubmed.ncbi.nlm.nih.gov/31454777/  The VIDAMS study:  https://pubmed.ncbi.nlm.nih.gov/37125397/  The 2024 study on clinically isolated syndrome:  https://pubmed.ncbi.nlm.nih.gov/38085047/   

In this episode, Dr. Valentin Fuster discusses a groundbreaking study that explores the role of remnant cholesterol in peripheral arterial disease, revealing it as a stronger and more direct cause of the disease than LDL cholesterol. The findings challenge traditional views on lipid management, emphasizing the need for targeted therapies to address remnant cholesterol and reduce cardiovascular risks.

Commentary by Dr. Candice Silversides

Commentary by Dr Shin Huei Liu

Commentary by Dr. Candice Silversides

With Ernesto Schiffrin, Jewish General Hospital, McGill University, Montreal - Canada & James Engert, McGill University, Montreal - Canada Link to editorial Link to paper

Show Notes for PiP Ep 02 “Empower Genomics with Proteomics”For more information about the UK Biobank, an Olink to Science blog post called “Genetic Regulation of the Human Plasma Proteome in the UK Biobank” is available here. The preprint publication itself is available here on bioRxiv. If you'd like to see a great 15 minute presentation on what the goals are for the UK Biobank Pharma Proteomics Project, Dr. Chris Whelan (Biogen) presented this YouTube video at one of the UK Biobank's scientific meetings that is worth watching.A paper discussed by Folkersen et al., “Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals” was published in Nature Metabolism in late 2020, and is available here.If you would like to contact Dale, Cindy or Sarantis feel free to email us at info@olink.com and if you would like to learn more about our backgrounds, Cindy's LinkedIn is here, Sarantis' is here, and Dale's is here.In case you were wondering, Proteomics in Proximity refers to the principle underlying Olink Proteomics assay technology called the Proximity Extension Assay (PEA), and more information about the assay and how it works can be found here.

Joe Cohen, founder of SelfDecode, SelfHacked and LabTestAnalyzer is a thriving speaker and entrepreneur that has been independently researching health and science for over 15 years. Growing up with a myriad of health issues that conventional medicine failed to solve pushed Joe to study his own genes to get to the root cause of his problems.After fixing his chronic health issues with a personalized gene-based approach to health, he knew that he needed to share this information with the masses. Founding SelfHacked, LabTestAnalyzer and SelfDecode was Joe's way of helping people take their health into their own hands and to provide people with access to cutting-edge tools and information designed to optimize health.Join us as we explore:How Joe's powerful journey from pain to purpose has culminated in his incredible health app SelfDecode.Joe's lived experience and perspective of the nature vs nurture puzzle.Why Joe is passionate about personalization and rejects one-size-fits all solutions.Joe's belief that genetics should be the foundation of health care but also why there are so many misconceptions about what genetics can predict.SelfDecode's next level genetic analysis, mapping and prediction models and how that is changing people's lives around the world!What your snips can tell you about where to prioritize your time and energy during your health journey, and what it means to “niche down”.Why the potential of Mendelian Randomization made me go WOW!Contact:Website - https://selfdecode.comInstagram - @mrbiohackerMentions Podcast – High Uric Acid Is Another Cause of Chronic Disease, https://youtu.be/4i3QOkqiQgASchedule a FREE 15 min discovery call with Steve and let's get started on your journey to thriving: https://bit.ly/3BcTsFwSUPPORT THE SHOW ON PATREON:As much as we love doing it, there are costs involved and any contribution will allow us to keep going and keep finding the best guests in the world to share their health expertise with you. I'd be grateful and feel so blessed by your support: https://www.patreon.com/MadeToThriveShowCONTACT Steve Stavs and join our community:https://www.facebook.com/MadeToThriveZA/     https://www.facebook.com/SteveStavsZA/https://www.instagram.com/stevestavsza/  Send me a WhatsApp to +27 64 871 0308. 

Commentary by Dr. Valentin Fuster

For more information, contact us at 859-721-1414 or myhealth@prevmedheartrisk.com. Also, check out the following resources:  ·Jubilee website·PrevMed's website·PrevMed's YouTube channel·PrevMed's Facebook page·PrevMed's Instagram·PrevMed's LinkedIn·PrevMed's Twitter ·PrevMed's Pinterest

Episode 67: Covid, Food, and HIV.  Medical students discuss the relationship between high cholesterol and COVID-19, the effect of food order in postprandial glucose and insulin, and HIV history. Moderated by Hector Arreaza, MD.  During this episode you will listen to three medical students discussing some topics that they found interesting during their family medicine rotation. All the credit goes to them because they read these topics and provided a very good summary. I hope you enjoy it.____________________High Cholesterol and COVID-19By Milan Hinesman, MS3, Ross University School of MedicineGiven the current state of the world, there's been a lot more attention to COVID-19 presentation, risks, and treatment. One study conducted by Dr. Kun Zhang and collaborators shows that there may be a relationship between higher total cholesterol levels and ApoB levels to increased risk of COVID-19 infection[1]. Dr. Zhang used a mendelian randomization from the UK Biobank data to test for lipid effects on COVID susceptibility and severity. The study performed analysis of data from the host genetics initiative consisting of more than 14,000 cases and more than one million controls showing a potential positive causal effect between high total cholesterol and ApoB and COVID susceptibility. A mendelian randomization is a process of taking genes which functions are already known and measuring their response to exposure to a disease in observational studies[2]. In short, high cholesterol and high ApoB are linked to COVID-19 infection.This is Rio Bravo qWeek, your weekly dose of knowledge brought to you by the Rio Bravo Family Medicine Residency Program from Bakersfield, California. Our program is affiliated with UCLA, and it's sponsored by Clinica Sierra Vista, Let Us Be Your Healthcare Home. __________________________Impact of food order on glucose after meals.   By Yvette Singh, MS3, American University of the CaribbeanIn the management of diabetes, health care providers usually assess glycemic control with fasting plasma glucose and pre-prandial glucose measurements, as well as by measuring Hemoglobin A1c. Therapeutic goals for Hemoglobin A1c and pre-prandial glucose levels have been established based on the results of controlled clinical trials. Unfortunately, many patients with diabetes fail to achieve their glycemic goals. Elevated glucose after eating may be the cause of poor glycemic control leading to vascular complications. Postprandial hyperglycemia is one of the earliest abnormalities of glucose homeostasis associated with type 2 diabetes. This is one of the important therapeutic targets for glycemic control. Current studies show that the amount and timing of carbs in the diet primarily influence blood glucose levels. Other studies also show that eating whey protein before meals, as well as changing the macronutrients in meals, reduces postprandial glucose levels; however, these studies did not have patients with type 2 diabetes. The main author of this study was Alpana P. Shukla and many other collaborators. The title is Food Order Has a Significant Impact on Postprandial Glucose and Insulin Levels, published by the American Diabetes Association on Diabetes Care in July 2015.This study was performed to analyze the order of food consumption with vegetables, protein and carbohydrates and its effects on postprandial glucose in overweight/obese patients with type 2 diabetes being treated with metformin. Subjects were studied for 1 week. They were given a meal with the same number of calories, after fasting for 12 hours: 55g protein, 68g carbs, and 16g fat. They were asked to eat carbs first, then to eat vegetables and protein fifteen minutes later. This order was reversed during the second week. Their postprandial glucose and insulin levels were measured at 30/60/120 mins after meals. The statistical studies showed an average post prandial glucose decrease by more than 25% when protein was consumed first. As well as the average post prandial insulin levels decreased by more than 40%. These results demonstrated that the timing of carbs during a meal has a significant impact on glucose and insulin levels comparable to some pharmacological agents. Reduced insulin excretion with this meal pattern may also improve insulin sensitivity. This may help patients with type 2 diabetes control their HbA1c, and possibly help reverse early diabetes. Educating patients about this approach is not controlling how much they are eating or restricting their diet so patients will likely comply with this recommendation. Eat your protein first!The potential problems of this study are that it was a small sample size (11 patients), limited food types, and insulin was measured only up to 120 minutes after meals. Further studies are needed to demonstrate the full effectiveness of this recommendation.___________________HIV Series Part I: HIV HistoryBy Robert Dunn, MS3, Ross University School of Medicine This is an HIV series for the Rio Bravo qWeek Podcast. The following episodes will include some of the history of HIV, transmissibility, the PARTNER-1 and PARTNER-2 studies, and will finalize with a full episode on HIV prevention. Today we are starting with HIV history.Prejudice against those with HIV stems from the history surrounding the virus. Between 1981-1983, cases of rare infections like Pneumocystis carinii pneumonia (PCP) and aggressive cancers like Kaposi Sarcoma were appearing predominantly amongst gay men and injection drug users.  Even children were presenting with AIDS creating misconceptions of how the disease was transmitted by touch. By 1982, this syndrome was referred to as the Gay-Related Immunodeficiency (GRID), which we now know as AIDS. Some History of HIVThe start of the Human Immunodeficiency Virus (HIV) was thought to have started in the Democratic Republic of Congo in 1920 when the virus crossed species to humans and gave its ability to infect humans[4]. In 1981, five young gay men in Los Angeles, California, presented with a rare lung infection called Pneumocystis carinii pneumonia (PCP). Two other groups of men also presented with a rare and aggressive cancer called Kaposi Sarcoma, in New York and California. By December of the same year, the first case of PCP was found in an injection drug user. And by the end of the year, there were 270 reported cases of this severe immunodeficiency and about 121 of them had already died from it, almost 50%. In 1982, due to the prevalence of these rare diseases being present among gay men, the syndrome was called the Gay-Related Immune Deficiency (GRID). The CDC later officially called the disease the Acquired Immune Deficiency Syndrome (AIDS). The term “gay cancer” was used in Venezuela before AIDS was known.In 1983, the disease was found in both women and children. In May 1983, in a joint conference between the Pasteur Institute in France and the National Cancer Institute, they announced that LAV and HTLV-III were the same virus and the cause of AIDS.In 1985, Ryan White, a teenager with hemophilia was banned from school when he was diagnosed with HIV after he received contaminated blood products. Ryan later died at 18 years old due to AIDS-related illnesses. At the same time, the FDA licensed the first commercial blood test to detect HIV. A foundation was later created to provide primary care and medications for low-income HIV patients.In 1987, the first antiretroviral drug, Zidovudine (AZT) was approved by the FDA to treat for HIV. In 1991, the famous basketball player Magic Johnson announced he tested positive for HIV and retired immediately. After his retirement he planned to educate young people about the virus which helped dispel stereotypes. Also in 1991, the famous singer of Queen announced he had AIDS and died the next day.In 1993, the movie Philadelphia with Tom Hanks promoted further discussion about HIV and AIDS. In June 1995, the first protease inhibitor was approved by the Food and Drug Administration (FDA), which started the era for Highly Active Antiretroviral Therapy (HAART). This brought down the rate of AIDS-related deaths and hospitalizations by 60-80%. Of special note, in 1986, the FDA passed the policy to ban all men who had sex with men (MSM) from 1977 onward, from donating blood or plasma to avoid the risk of transmitting HIV or Hepatitis A. This policy was amended in December 2015, when the revised policy said any MSM within the last 12 months, would need to wait at least 1 year before donating blood. In light of the COVID-19 pandemic, the FDA amended it its policy once more to decrease the wait time to 3 months form the last time the man had sex with another man.____________________________Conclusion: Now we conclude our episode number 67 “Covid, Food, and HIV.” Kudos to Milan, Yvette and Robert, they presented relevant information for our practice of medicine. They taught us that high cholesterol is a risk for COVID-19 infection; Also, when you eat proteins first, your glucose and insulin after meals are lower than when you eat carbs first; and you will be hearing from Robert for a couple episodes regarding HIV. Today he gave us a little piece of HIV history. Even without trying, every night you go to bed being a little wiser.Thanks for listening to Rio Bravo qWeek. If you have any feedback about this podcast, contact us by email RBresidency@clinicasierravista.org, or visit our website riobravofmrp.org/qweek. This podcast was created with educational purposes only. Visit your primary care physician for additional medical advice. This week we thank Hector Arreaza, Milan Hinesman, Yvette Singh, and Robert Dunn. Audio edition: Suraj Amrutia. See you next week! _____________________References:Zhang, K. Dong, S. Guo, et. al., Causal Associations Between Blood Lipids and COVID-19 Risk: A Two-Sample Mendelian Randomization Study. Arteriosclerosis, Thrombosis, and Vascular Biology, originally published on September 9, 2021. https://doi.org/10.1161/ATVBAHA.121.316324. What is Mendelian Randomization and How Can it be Used as a Tool for Medicine and Public Health? Opportunities and Challenges, Webinar announcement given by Professor George Davey Smith on November 27, 2018. Centers for Disease Control and Prevention, https://www.cdc.gov/genomics/events/precision_med_pop.htm Alpana P. Shukla, Radu G. Iliescu, Catherine E. Thomas and Louis J. Aronne, Food Order Has a Significant Impact on Postprandial Glucose and Insulin Levels, Diabetes Care 2015 Jul; 38(7): e98-e99. https://doi.org/10.2337/dc15-0429. History of HIV and AIDS Overview. Avert, October 10, 2019. https://www.avert.org/professionals/history-hiv-aids/overview. Accessed on September 21, 2021. Shaw, Maggie. FDA's Revised Blood Donation Guidance for Gay Men Still Courts Controversy. AJMC, April 3, 2020. https://ajmc.com/view/fdas-revised-blood-donation-guidance-for-gay-men-still-courts-controvery. Accessed on September 21, 2021. BAYER, R. (2015), Science, Politics, and the End of the Lifelong Gay Blood Donor Ban. Milbank Quarterly, 93: 230-233. https://doi.org/10.1111/1468-0009.12114. Ways HIV can be Transmitted. Centers for Disease Control and Prevention, April 21, 2021. https://www.cdc.gov/hiv/basics/hiv-transmission/ways-people-get-hiv.html. Accessed on September 21, 2021.

Interview with Gregory M. Marcus, MD, MAS, author of Coffee Consumption and Incident Tachyarrhythmias—Reported Behavior, Mendelian Randomization, and Their Interactions, and Zachary D. Goldberger, MD, MS, author of Another Cup of Coffee Without an Arrhythmia, Please. Related Content: Coffee Consumption and Incident Tachyarrhythmias: Reported Behavior, Mendelian Randomization, and Their Interactions Another Cup of Coffee Without an Arrhythmia, Please Mendelian Randomization: How the Natural Assortment of Genes Can Mimic Randomized Clinical Trials Mendelian Randomization

Interview with Gregory M. Marcus, MD, MAS, author of Coffee Consumption and Incident Tachyarrhythmias—Reported Behavior, Mendelian Randomization, and Their Interactions, and Zachary D. Goldberger, MD, MS, author of Another Cup of Coffee Without an Arrhythmia, Please. Related Content: Coffee Consumption and Incident Tachyarrhythmias: Reported Behavior, Mendelian Randomization, and Their Interactions Another Cup of Coffee Without an Arrhythmia, Please Mendelian Randomization: How the Natural Assortment of Genes Can Mimic Randomized Clinical Trials Mendelian Randomization

Commentary by Dr. Valentin Fuster

Commentary by Dr. Valentin Fuster

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.11.09.374298v1?rss=1 Authors: Jiang, L., Yi, G., Li, X., Xue, C., Li, J., Huang, H., Li, M. Abstract: Isolating causal genes from enormous genome-wide association signals of complex phenotypes remains an open and challenging question. SMR (Summary-based Mendelian Randomization) is a widely used Mendelian randomization (MR) method for inferring causal genes by using a single expression quantitative trait locus (eQTL). In the present study, we explored more powerful MR methods based on multiple eQTLs. Among six representative multiple instrumental variable (IVs) based MR methods, original used in the epidemiological field, not all MR methods worked for the causal gene estimation. But we found the maximum-likelihood based MR method and weighted median-based MR method were preferable to the other four MR methods in terms of valid type 1 errors, acceptable statistical powers and robustness to linkage disequilibrium (LD) in eQTLs. Both of the MR methods were also much more powerful than the SMR. We recalibrated key parameters of the two MR methods in practices and developed a multiple IVs based MR analysis framework for causal gene estimation, named MACG and available at http://pmglab.top/kggsee. In the applications, MACG not only rediscovered many known causal genes of the schizophrenia and bipolar disorder, but also reported plenty of promising candidate causal genes. In conclusion, this study provided a powerful tool and encouraging exemplars of mining potential causal genes from huge amounts of GWAS signals with eQTLs. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.10.16.342675v1?rss=1 Authors: Cheval, B., Darrous, L., Choi, K., Klimentidis, Y., Raichlen, D., Alexander, G., Cullati, S., Kutalik, Z., Boisgontier, M. P. Abstract: Physical activity and cognitive functioning are strongly intertwined. However, the causal relationships underlying this association are still unclear. Physical activity can enhance brain functions, but healthy cognition may also promote engagement in physical activity. Here, we used Latent Heritable Confounder Mendelian Randomization (LHC-MR) to assess the bidirectional relations between physical activity and general cognitive functioning. Association data were drawn from two large-scale genome-wide association studies (UK Biobank and COGENT) on accelerometer-based physical activity (N = 91,084) and cognitive functioning (N = 257,841). We observed a significant MR association, suggesting that increased duration of physical activity improves cognitive functioning (b = 0.61, CI95% = [0.36,0.86], P = 1.16e-06). In contrast, we found no evidence for a causal effect of cognitive functioning on physical activity. Follow-up analyses revealed that the favorable association from physical activity to cognitive functioning was driven by moderate physical activity (b = 1.33, CI95% = [0.72,1.94], P = 2.01e-05) with no contribution from vigorous physical activity. These findings provide new evidence supporting a beneficial causal effect of moderate physical activity on cognitive functioning. Therefore, interventions that promote moderate rather than vigorous physical activity may be best suited to improve or recover cognitive skills. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.07.24.208975v1?rss=1 Authors: Storm, C. S., Kia, D. A., Almramhi, M., Bandres-Ciga, S., Finan, C., Hingorani, A. D., International Parkinson's Disease Genomics Consortium (IPDGC),, Wood, N. W. Abstract: Parkinson's disease (PD) is a neurodegenerative movement disorder that currently has no disease-modifying treatment, partly owing to inefficiencies in drug target identification and validation using human evidence. Here, we use Mendelian randomization to investigate more than 3000 genes that encode druggable proteins, seeking to predict their efficacy as drug targets for PD. We use expression and protein quantitative trait loci for druggable genes to mimic exposure to medications, and we examine the causal effect on PD risk (in two large case-control cohorts), PD age at onset and progression. We propose 23 potential drug targeting mechanisms for PD, of which four are repurposing opportunities of already-licensed or clinical-phase drugs. We identify two drugs which may increase PD risk. Importantly, there is remarkably little overlap between our MR-supported drug targeting mechanisms to prevent PD and those that reduce PD progression, suggesting that molecular mechanisms driving disease risk and progression differ. Drugs with genetic support are considerably more likely to be successful in clinical trials, and we provide compelling genetic evidence and an analysis pipeline that can be used to prioritise drug development efforts for PD. Copy rights belong to original authors. Visit the link for more info

Mendelian randomization is a powerful technique that enables investigators to mimic randomized clinical trials by characterizing genetic differences between groups of people and studying their clinical outcomes. Brian A. Ference, MD, MPhil, from the University of Cambridge in England, is a leading expert on this topic and spoke with us about how mendelian randomization has facilitated a better understanding of lipid biology and how it relates to cardiovascular risk.

Commentary by Dr. Valentin Fuster

Mendelian randomization is a genetic epidemiological approach that is made substantial inroads into our understanding of the causes and consequences of disease, but can that same technique be run in reverse? In the March 2019 issue of Clinical Chemistry, a paper investigated potential blood markers of early chronic kidney disease which are caused by loss of kidney function, using an innovative reverse Mendelian randomization approach. That same issue included an editorial that was authored by Dr. Michael Holmes from the University of Oxford and by Dr. George Davey Smith from the University of Bristol, both from the United Kingdom and both are our guests in this podcast.

Identifying markers of chronic kidney disease that occur early in the disease process and are specific to loss of kidney function may allow timely more accurate identification of patients who will eventually develop the disease. In the March 2019 issue of Clinical Chemistry, a paper investigated potential blood markers of early chronic kidney disease which are caused by loss of kidney function using an innovative reverse Mendelian randomization approach.

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.  

The best evidence for proving cause-and-effect comes from randomized clinical trials. However, they are expensive and difficult to perform. The natural assortment of gene variants at birth can mimic randomization in some circumstances and yield important clinical information that can help physicians better care for their patients. Read the article: Mendelian Randomization  

Commentary by Dr. Valentin Fuster

Commentary by Dr. Valentin Fuster

Type 2 diabetes is a metabolic disorder with globally increasing prevalence. Therefore, the identification of etiological factors is of ascending relevance for the understanding, treatment, and prevention of the disease. Levels of the acute-phase serum amyloid A (A-SAA) protein have been found to be elevated in type 2 diabetic subjects, but little is known about their causal implication in the development of type 2 diabetes so far. This doctoral thesis presents an epidemiological perspective on the association between circulating levels of A-SAA and risk of type 2 diabetes and assesses a possible causality in this association using a genetic approach. Three studies were conducted. In a prospective cohort study, A-SAA levels were measured in 836 initially non-diabetic, elderly, Western European subjects without clinically overt inflammation who participated in a seven-year follow-up examination. Results of this study provided first evidence that levels of A-SAA are elevated years before the manifestation of type 2 diabetes independent of other type 2 diabetes risk factors. However, adjustment for parameters related to glucose metabolism, particularly levels of 2h-glucose, attenuated the association suggesting a potential link via post-challenge hyperglycemia in the association between elevated levels of A-SAA and type 2 diabetes or, alternatively, a possible reverse causality between levels of A-SAA and 2h-glucose. In a meta-analysis of genome-wide association studies (GWAS) on levels of A-SAA conducted in three population-based studies and one prospective case-cohort study including a total of 4,212 participants of European descent two biologically highly plausible genetic susceptibility loci for A-SAA proteins at chromosome 11p15.5-p13 and chromosome 1p31 were identified. One of these loci represented a suitable candidate for a Mendelian Randomization study. In Mendelian Randomization studies, genetic variants are used as proxies for a biomarker. These studies benefit from the fact that genotypes are randomly assorted at meiosis and are largely independent of non-genetic confounding and disease processes. Thus, they constitute a genetic approach to assess whether the association between a biomarker and a disease is causal. The associations between genetic variants of the candidate locus and type 2 diabetes were extracted from the results of a meta-analysis of eight GWAS (8,130 cases, 38,987 controls) published by DIAGRAM, a large diabetes and genetic consortium. In spite of sufficient power, the above mentioned associations were not significant suggesting that there are genetic mechanisms that raise plasma levels of A-SAA without translating into an increase in type 2 diabetes risk. In conclusion, results of this doctoral thesis indicated that levels of A-SAA are elevated years before the manifestation of type 2 diabetes but could not provide evidence that the association is truly causal using a genetic approach. Rather it seems likely that the association between levels of A-SAA and risk of type 2 diabetes is substantially influenced by post-challenge hyperglycemia. Time-series studies are warranted to elucidate the role of post-challenge hyperglycemia in this association.

Seamus Harrison discusses new research which suggests no causal link between raised HDL cholesterol and reduced risk of coronary heart disease.