Podcast appearances and mentions of erik ingelsson

Happy New Year! In our first episode of 2025, Patrick is joined by Erik Ingelsson, Chief Scientific Officer at Wave Life Sciences. Erik is also the formerr Senior Vice President of Target Discovery at GlaxoSmithKline and a former Professor at Stanford and Uppsala universities. Patrick and Erik discuss Wave's world-first discovery in RNA editing therapies for Alpha-1 Antitrypsin Deficiency (AATD), Erik's far-reaching career across academia, big pharma and biotech, and how to be a present parent in the thick of a thriving career.

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

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

I Digitalsamtal #048 är Erik Ingelsson, professor i medicin vid Stanford University, gäst. Han forskar om kopplingar mellan hjärtkärlsjukdomar och vårt DNA. Ett verktyg som blivit väldigt viktigt för hans forskargrupp. I avsnittet berättar han om vad big data innebär i hans forskning, men framför allt hur teknikutvecklingen påverkar jakten på nya mediciner. Istället för […] The post #048 – Så förändrar big data medicinsk forskning appeared first on Podcasten Digitalsamtal.

Om kopplingen mellan med gener och fetma med Erik Ingelsson, professor vid Institutionen för medicinska vetenskaper och Science for Life Laboratory, Uppsala universitet. Programledare är Madeleine Nilsson.

Om du vill veta din risk att dö inom den närmaste framtiden finns det nu ett verktyg för det. Det är ett datorprogram som, baserat på några få frågor, kan beräkna din statistiska risk att dö inom fem år. – Vi har en dator-algoritm som räknar ut hur du har svarat på alla dina frågor. Utifrån det får man sedan en absolut risk att dö inom fem år, säger Erik Ingelsson, professor i molekylär epidemiologi, och ansvarig för en stor studie som publiceras i veckans the Lancet.  I studien, som ligger till grund för riskkalkylatorn, har forskarna gjort jämförelser och undersökt samband mellan mer än 650 olika socioekonomiska, biologiska och psykologiska faktorer i ett av världens största studiematerial, den brittiska biobanken. Datat består av uppgifter och levnadsöden från nära en halv miljon britter, som forskarna noga följt i fem års tid, och på så sätt har man kunnat få fram vilka av de 650 faktorerna som bäst förutspår risken att dö. – Det visade sig att det var saker som "hur uppskattar du din egen hälsa" eller "hur snabbt går du" som var starkast, den typen av frågor, som var starkast. Vilket var intressant och faktiskt lite förvånande, säger Erik Ingelsson. Den stora analysen visade överraskande nog att de frågor man själv kunde svara på faktiskt bättre kunde förutspå risken att dö, än till exempel blodtryck eller fettsammansättning. En stark faktor är vilken hastighet du normalt går. Men det handlar inte om att du riskerar att dö bara för att du går långsamt, berättar Erik Ingelsson, utan om de bakomliggande orsakerna till att du faktiskt går långsamt. –  Man kan tänka sig att personer som går långsamt är sjuka, överviktiga eller röker. Det finns många olika saker som de faktiskt är, som i sin tur ökar risken för att de ska dö. Forskarna lät en dator-algoritm ta fram den kombination av riskfaktorer som på bästa sätt förutspår en persons risk att dö inom fem år. Resultatet blev riskkalkylatorn, som med 11 enkla frågor för kvinnor och 13 för män, ger dig en procentuell risk. Verktyget finns tillgängligt online och forskarna tror att det kommer vara till stor användning både för privatpersoner och olika aktörer inom samhället. Vetenskapsradions reporter Katarina Sundberg knappar in sina 11 svar i verktyget Ubble: sin ålder (40), sitt kön, hur många barn hon har fött, och om hon går fortare eller långsammare än genomsnittet. – Jag knappar in det sista i datorprogrammet och lite nervöst är det allt, när jag ber Ubble uppskatta min risk att dö och beräkna min så kallade ubble-ålder. – Ok, då ska vi se... Åh! Jag är bara 22 år! konstaterar Katarina Sundberg. Skulle du själv vilja testa att beräkna din risk att dö inom 5 år, hittar du en länk till Ubble här.