Achieving the first complete sequence of a human genome via the Human Genome Project represents an incredible feat – but it’s just the tip of the iceberg when it comes to unlocking the power of genomics for drug discovery and development. As the scientific community builds on our understanding of genomics, one of the most critical questions becomes, how can we thoughtfully collect and use genetic data to better understand and support the health of all communities, especially those who have been excluded from research in the past? Co-host Maria Wilson sits down with Mark McCarthy, Principal Fellow and Executive Director of Human Genetics, to explore the interplay between genetics, research, and health equity – and the potential for more diverse genetic data to create a more equitable health landscape. This episode is the first in a two-part series dedicated to inclusivity and diversity in research.
As we celebrate 125 years of Roche transforming lives, this episode will be featured as part of the LifeTalks series, where leaders and innovators are discussing how we can overcome society’s most pressing challenges and create real impact in the lives of millions. Learn more.
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Transcript of Two Scientists Walk Into A Bar: “It's Personal: Inclusive Research and Precision Medicine” with Mark McCarthy
Maria: I’m Maria Wilson.
Danielle: I’m Danielle Mandikian.
Maria: And we are scientists. We. Love. Science.
Danielle: Yeah, we do. So, when we aren’t doing it, the next best thing is to talk about science! What’s really awesome is we’re surrounded by some of the most brilliant minds in research!
Maria: So there’s always someone interesting to talk to. But there’s never much time to just chat at work. That’s why we are so excited to be hosting this podcast. We are going to step away from the labs today to talk to other scientists about the cool stuff they are thinking about, working on and imagining…
Danielle: … as well as how some of these discoveries just might lead to new medicines. So, grab your favorite drink, get ready to unlock your science brain and join us for Two Scientists Walk into a Bar…
Maria: The show for scientists, science geeks and the people who love them!
Maria: We are going to do something a bit different for the next two episodes, tackling a very important topic: diversity in research. So, this is a relatively new concept in the world of science. And, basically it means that we recognize the need to think more expansively about the world, about people, and about data. So next time, we're going to talk with two of my colleagues specifically about clinical trials. But today, we're going to talk with Mark McCarthy about human genes. Human genes are the instructions for all of our cells. And if we can figure out how to read this manual, then we can begin to have even more insights about all sorts of diseases as we look for new treatments. And of course, our genome expresses both our diversity from other humans and our similarity to other humans. And this is an area that is of great interest to me personally and something that I'm also working on. So, it is such a delight to welcome you, Mark, to the bar.
Mark: A pleasure to be here. Thanks for the invitation.
Maria: So, one of the hopes is that by learning more about our genomes that we can get our medicine to be more precise, right? And I think one of the – for me, one of the paradoxes of genomics is it teaches us how similar we all are. But, yet, there is also variation that's associated with our ancestry which is important.
Mark: Yeah, I think we're realizing that kind of the one-size-fits-all approach to thinking about individual disease is no longer where we want to be. We're all aware that different people vary in relation to the diseases they're likely to get, the ways those diseases are likely to progress, the treatments that might be most effective in them and the treatment that might be most safe in them. And, although we may not understand, ever, all the random effects that go into those differences, I think we're increasingly being able to characterize them in terms of the genetics, in terms of the impact of gender, and all the other factors that play into the different ways that individuals respond to disease and-and respond to treatments, which obviously includes environmental factors, and healthcare access and many different factors. So, if we're really going to get the right patients, the right treatment at the right time, which is the maxim of precision or personalized medicine, we definitely need to know how each of those factors is in play and how it relates to disease risk and progression, how it relates to the treatments that we're trying to develop and deploy to treat and prevent those diseases. So, that's why I think there's a lot of emphasis now on ensuring that our research, really from the bench right through to the bedside, from the basic research right through to the clinical trials that we do and the translation that we do, captures as much of that diversity and is as inclusive as possible. Because, if we don't do that, we may well just exacerbate some of the current health disparities because so much of the data that we base our research on still emanates from particular populations. And, we and others certainly need to do a better job of including diverse groups. Ancestry is an important part of that, but it's really not the only factor. We need to include gender, age, and lifestyle and lived experience, and many different things that can play into the ways in which our treatments work.
Maria: Yes. This is so important. Before we get into this, though, can we back up and, just for our listeners, talk a little bit about the Human Genome Project? You and I lived through that, but it was, of course, a huge ten plus year effort to sequence and map all human genes. What was the impact of that work?
Mark: Well, just 20 years ago, but it's absolutely pivotal because nothing that we've been able to do since really would have been possible. I started in the field before we really had that map, and it really was, in retrospect [laughs] trying to walk through a dark house with a not very powerful torch to be honest, because you could only illuminate one particular spot at a time. And, certainly, what the Human Genome Project first of all, gave was at least a map of where the genes were and an understanding of what might be in the neighborhood. But then, as people became able to do slightly more sequencing beyond the first sequence and started doing projects like HapMap where they started sequencing a hundred people from Europe, and a hundred people from Africa, and a hundred people from-from Asia in the first instance, we started to see where these variable positions in the genome were. And that's really what then led on to the ability to design: "Well, let's design an array that we can use to test all those sites of common variation," and that's really what led on to the Genome-Wide Association Studies and subsequently, on to the ability to generate sequence. So, absolutely pivotal. I mean, it's interesting that we've only just managed to finish the genome sequence now, working through all the un-sequenced bits. And just going back to what we were talking about – diversity – now, people have done complete genome sequences, not just in Europeans, but in African-descent individuals and other populations. We're starting to find there are some pieces of the genome that are only present in African-descent individuals and have been…
Maria: These are the non-coding parts or…?
Mark: Yeah, just little sections that are revealed when you take a broader range of – because the initial sequence was done on an amalgam of European individuals, and that didn't capture all – not just the-the point-by-point diversity, but the bits of the genome that can be inserted and…
Mark: …and so on. So we…
Maria: I didn't realize it had only just been completed.
Mark: Yeah, just in…
Maria: I thought it would have been done a while ago, okay.
Mark: Yeah, we were finished with genomes on various different points. But now, as technology has advanced and particularly, these – the repeat regions are pretty hard to sequence through. And as some of that's been solved, I think we're pretty close now to a complete genome sequence, not just for one individual, but one which really captures all the pieces of the genome that exist in most people somewhere on the globe.
Stephanie: Hi, Maria.
Maria: Hi, Stephanie.
Wellington: Hi, Maria.
Maria: Hi, Wellington.
Wellington: So, we had the same question. I thought we had completed the Human Genome Project back in 2003?
Maria: Right. So, we did. But it is a bit confusing. So, it turns out that most of the human genome was sequenced by 2003. Like, most of their ATCG base pairs, more than 90% had been sequenced in 2003. But there were some portions that were inaccessible to the technology at the time – these very repetitive sequences that weren't accurately sequenced. So, as Mark mentioned, the technology has gotten so much better that there have been groups working to complete sequencing every little last bit of the genome so that we do have an absolute complete reference.
Maria: So, what types of things have we found that have maybe changed how we think about diseases?
Mark: From a diversity and inclusion perspective, I think we-we see that many of these common diseases are broadly similar in terms of the genetic variation that underlies them and the processes that are involved. But, of course, the specific genetic variants may be different between populations down to historical differences in the frequency of particular variant alleles and the complex history of folks' migration around the globe.
Maria: So, the same gene may have become involved…
Maria: …independently in different populations. Different mutations.
Mark: Yes, different mutations, different variants have arisen. And that has one big implication. One of the ways in which we think we're going to be able to use complex trait genetics – so, the genetic understanding of Alzheimer's, or diabetes, or coronary artery disease, or any of these diseases – is to develop these so-called polygenic risk scores.
Mark: When you take a disease like Type 2 diabetes with six or seven hundred common variants influencing disease risk, it's easy to imagine that individually those impacts are quite small maybe…tweaking your risk of diabetes up 5% here, 2% there. If you bring them together, then you start to have something that is quite powerful. And, for example, can quite easily pull apart people whose risk of diabetes is about ten-fold different. People with lots of diabetes risk alleles will have maybe as close to a 50% lifetime risk of diabetes. Those with very few diabetes risk alleles just by chance will be down in the, you know, much lower few percent risk. But that does really depend on generating those polygenic risk scores and then, applying them in the same population because of the factors that we just talked about, that the processes might be similar, but the actual genetic variants may be different. If you take a polygenic score that you’ve developed in a European population and try and apply it in an African-descent population, it won’t work very well at all. And that’s another reason why we need to be generating data in many different ancestries so that we can develop these polygenic scores that are equally effective at stratifying risk in people in different populations or otherwise, again, we’ll be just exacerbating…
Maria: Exactly, yeah.
Mark: …health disparities. We’ll be generating a tool which works quite well in European-descent individuals and it doesn’t work as well in other populations.
Maria: And especially if the tools get rolled out, and it’s not understood that they have these liabilities, that’s, I think, a big danger, right?
Maria: But it should technically be possible with the right data inputs to generate a sort of universal polygenic risk score, you think?
Mark: Yes, and there’s been some progress towards doing that, but still, it’s compromised by the paucity of data from many of these ancestries.
Stephanie: Hey, Maria, let’s back up for a second. I was not trained as a geneticist. So, let’s try to understand polygenic risk scores a little bit more. What do they represent, and how do we use them?
Maria: So, that’s a great question. The way to think about polygenic risk score is, it’s another way of determining sort of what’s your statistical chance of having any particular disease. So, for example, think about something like having a heart attack. What’s yours or my statistical chance of having a heart attack in the next five years or the next ten years? So, without any information, you can look at the whole of the population of the world and say, on average, maybe you’ve got in your lifetime a risk of having a heart attack that is quite high, it’s like one in four across your whole lifetime. And without knowing anything about you, you could say something like that. But then if I have more information about your gender, your lifestyle, your plasma lipids, I could give you a more accurate prediction of how likely you are to have a heart attack. And then if we look at you genetically, that gives you so much more information. And what the polygenic risk score is, it’s using variants in multiple different genes, all of which contribute to cardiovascular disease. We’re often a lot more familiar with something like a monogenic disease where, yes, there’s a defect in that one gene. And if you have that defect, there’s a 100% chance you’re going to have that disease. But most diseases are not monogenic, but they have a genetic contribution. So, for something like cardiovascular disease or diabetes or Alzheimer’s, what we’re doing is we’re looking at, well, you have variant X in this gene, maybe to do with lipids; you have another variant in a gene to do with inflammation. And we use statistical analysis of big datasets. So, you've got what looks like a fingerprint. Like if you have, you know, 20 or 30 of these genes, all of which have this particular variant, that signature says you are actually at a much higher risk for cardiovascular disease than someone else who doesn’t. And now, that doesn’t mean you’re going to have a heart attack in five years or even in ten years. But it does tell you and you and your medical professionals that you’re perhaps at a higher risk than somebody else. And you wouldn’t know that unless you looked at your genes.
Maria: So, when you're studying the genetics of a disease, what do we understand at the moment about the role of variants in different populations? For example, in something like diabetes, why is it that we see the same genes and variants involved in some populations, but maybe different ones in others?
Mark: So, in terms of what the genetic studies have highlighted about differences between major parts of the world in terms of diabetes risk, because most of the work has been done using these genome-wide association studies that mostly focuses on common variation, we've been really looking through one end of the telescope. And, it's true that most common variation has risen to being common because it's been around for a long time, and it is often widely shared between major population groups. So, maybe no big surprise that we see quite a lot of the genetic variant that's associated with diabetes in Europe, may often be also associated in Africa and in Asia. But that's not entirely true. Sometimes, we see slightly different variants in the same gene have popped up. And as we get down to looking at rarer and rarer signals, which is what is increasingly becoming possible through access to sequence-level data, rare variants tend to be rather more recent. And they therefore tend to have a rather more circumscribed location. So, we're certainly starting to understand what some of the differences in disease risk and prevention might be as they relate to some of these rare variants that maybe have only been present in one population or, by chance, risen to a high rate in just one population. So, one example that is quite striking is that one of the biggest effects of diabetes risk in people of East Asian origin is a variant in a gene called PAX4 that's a transcription factor that's known to be involved in beta cell development. It has about a 10% frequency, that variant that gives you the risk – increased risk of diabetes in individuals of East Asian origin. But it's almost completely absent in other populations around the world as far as we can tell. So that is an example of a variant that we would have known nothing about had we not done studies in those other populations. And it's an illustration of the ways in which, by doing that, we can pick up signals we wouldn't otherwise have seen.
Maria: That is a great point. And it really underscores why it's so critical to include diverse populations in our studies, because we just don't know what we don't know if we don't look, right? So, what are going to be those challenges that we need to think about in trying to make sure everyone is represented in our genetic data?
Mark: One of the things that I think we need to be careful about when we start thinking about precision medicine is not to settle for just dividing people up into rigid categories. There's a real tendency to want to do that, and medicine is built around categorizing diseases and so on. And we also see that with ancestry. We have this exquisite data we can generate from millions of data points that capture lots of information about an individual's makeup and their ancestry. But then, we collapse it down to these crude categories of White and Black or the different Census categories that are used in the U.S. in ways that really fail to capture the nuance of that. And, okay, it can be useful to have those broad categories, but they also reinforce the notion, both in terms of, you know, disease subtypes and ancestry, that these really are discreet groups that are somehow intrinsically different when clearly, it's a continuum between, folks of European descent and African descent because many people have a mix of those. It's a continuum between Type 1 and Type 2 diabetes because there's nothing that says if you have Type 1 or risk of Type 1 you can't get Type 2 as well. And some people have a combination of those.
Maria: Why do you think we do that?
Mark: I think part of the motivation there comes from the simple fact that in medicine you do often have to make a binary decision. "Am I going to treat X or Y? Am I going to operate? Am I going to do this test? Am I going to give treatment A or treatment B?" So, you understand how at some point you have to…
Maria: You have to decide which door in the hospital you’re walking through, GI or cardiovascular.
Mark: Exactly. But, what often happens is that when a patient first presents, before any of those decisions have to be made, there's an effort to pigeonhole them. To categorize. And to reduce all this rich genetic data, and wearable data, and clinical data we may have on that individual and say, "Oh, they're at high risk for coronary artery disease," or "They're at low risk," or "They have type A of disease," or “type B”. And that neglects the fact that that those are very crude classifications, and there's a continuum, and neglects the fact that actually people will evolve, and five years later somebody who was in type A might look a bit more like type B, and if you have pigeonholed them and have given them a label then, you may end up treating them five years hence for the disease that they no longer really have. So, I think although it's great, and it's a move forward to think, "Well, how does our treatment that we might be developing work in people of South Asian descent?" "And how does it work in people of African descent?" We should not stop there.
Mark: We should be moving beyond that. Since we can actually capture all that genetic data let's actually understand that much more detailed level rather than trying to collapse all of that rich data down to just one dimension. You know, just to give an example, there's a historical example of that, a treatment for HIV. There's a risk of hypersensitivity reactions, and that's associated with particular HLA genotype. The frequency is about 0.5% in Europeans, and it was thought historically to be actually much rarer in people of African descent, so “maybe we don't need to do the genetic testing in people of African descent.” It turns out that was just based on a very small bit of African diversity, and there are African populations, for example, the Maasai in Kenya, where the prevalence is 10%. So, just reducing it to White versus Black is a very, very crude and inadequate measure of what we need to be doing. So, I do think it's important as we develop this thinking about personalized medicine and that we don't simply replace “one-size-fits-all” with “two-sizes-fits-all” because actually, that's not much of a step forward.
Maria: Too much generalization…
Maria: …isn't helpful. A simple analogy I like to use when we talk about this is that it is a true statement to say that human men are taller than human women, but it's not that hard…
Mark: Yeah. [Laughs]
Maria: …to find a human woman who is taller than another human man, right?
Mark: Yes, that's right.
Maria: It's useful to be able to broadly say, "Oh, this group of people broadly has this phenotype," but you can't just rely on that.
Mark: Yeah, and if we really want to get to precision medicine by what people mean by it, we should be capturing all of that rich diversity of information that we can potentially, provided we do it ethically, and with privacy, and…
Mark: …all of the appropriate safeguards. You know, that is a rich resource that we can use to understand how each of us travels from some state of health to some state of disease. And, being able to do that not just a one-time point, but to do that longitudinally I think is also really instructive because we all start in different places, so if we're comparing ourselves against the population we might not really have a full sense of where we have traveled historically. And it's a bit like the cyclists in the Tour de France when they're checking them for doping. They don't necessarily wait until their testosterone levels are going outside the normal range.
Mark: They may look back at well, two years ago your testosterone level was 12. And now, it’s 18, and that ain’t normal. It’s still in the “normal” range.
Maria: It’s normal for you, yes.
Mark: But that’s not normal for you. So, that ability to compare against the historical self, to pick up subtle changes that may be premonition of, an incipient move towards disease.
Mark: But I think that's going to be particularly powerful. And that won't be captured by just trying to reduce everything down to high risk and low risk.
Maria: So, I know something that Wellington will want to know.
Maria: What's the difference between genetics and genomics?
Mark: Yeah, it's one of those terms, they are both used a little imprecisely. When we talk about genetics, I think we are specifically talking about genetic variation, and how it moves through families, and how it's related to the clinical phenotypes that we care about. Genomics is a broader concept and sort of meta-level study of genetics, but encompassing, “How do all the genetic parts fit together? How do they relate to the way in which the DNA codes for RNA and the way in which the RNA is converted into protein?” So, it's a more systematic and holistic perspective of how DNA and DNA variation relate to all the impact that it has on the body.
Wellington: Maria. You know me too well. I totally wanted to know that answer. But I did have a second question. Where are we going to see this genetic and genomic data in my experience of medicine?
Maria: Yeah, I think it's going to be so exciting in the future how this can potentially impact our health. But, at the moment, we're in this brave new world where some people do get access to this information. There are various companies that you can get genetic information about disease risk from, but it's not become standard of care as of yet. But I can imagine a future where it will be; where there will be FDA-approved genetic testing, where people will know their polygenic risk score, where your insurance companies will reimburse you to get these tests done. And then the question is, what do you and your health care provider do with that information? And some of it will be, well, if I know that I am at a particularly high risk for cardiovascular disease or diabetes, you may know this already because you will have family members who suffer from it, but you'd know it more concretely with the polygenic risk score. You can take your own actions to modify. Lifestyle is one is one way you could use that information in an empowered way yourself. But then also I do think we'll start to understand how medicines impact long-term disease progression related to polygenic risk scores. So potentially your primary care provider might be able to say to you one day, “Look, you have a higher risk than average for this disease or that disease, and there’s some clinical data that shows that if I give you this medicine, it will reduce the incidence and the risk of you having that disease.” And you'll have the choice whether or not to take that potentially.
Maria: So, what drove you to become a physician and then, a scientist? Could you tell us a little bit of your story?
Mark: I struggle to recollect how I quite ended up as a physician, but I, like many people who did science at school, I ended up going to medical school. And then ended up, as again, many people do, careering from one specialty to another as you do, as you rotate as a junior doctor and ended up in endocrinology. And I thought, "That's pretty cool." So, I ended up choosing to be a physician with a specialty in endocrinology, and rose through the ranks, as you do, of what you'd call residency here, and so on. Didn't do much science. I had no desire really, to do research whatsoever, but the situation in the UK at the time, and still true to some extent, was that, if you really didn't have a research degree as well it was difficult to get a good consultant job. So, I was dragged kicking and screaming into…
Mark: …the lab to do research for two or three years, which I really hated…
Mark: …at the outset because, you know, you train as a medic for eight, 10 years, and you're pretty good at it by the time you reach that point. So, it felt like being a pretty good 100-meter athlete who was suddenly told, "Well, don't bother doing that anymore, start throwing the hammer."
Maria: I will say that as a PhD science post-doc when the medical people came into the lab it was, "Oh, god who is going to look after him?" [Laughs]
Mark: So, I was that person.
Mark: I was that person for someone. And just towards the end, I got a few papers published, gave a couple of talks, got a bit of confidence, because I had terrible imposter syndrome, and then got a chance to go to the States to work at the Whitehead for a year, and that was really transformative. It was a great environment, and it sort of gave me the confidence that I could hack it with the best of them. So, when I went back to the UK after my time in the States was up I fell back into a position that was half medical and half research, started building up my own research lab, started doing less and less medicine, started feeling less and less comfortable doing medicine because…
Mark: …I was spending less and less time doing it and yeah. I ended up moving to doing more and more science. I'd love to tell you it was some fantastic career scheme…
Mark: …that led me inexorably from my 16-year-old self to this position but it was far from that. It was – as, you know, maybe happens to other people – just a series of events that guided me one way and the other.
Maria: So, I bet there are some 16-year-old potential scientists listening to this podcast. And I know that when I was 16, like you, I didn't really have a career plan in mind, but I was fascinated with genetics and with DNA. So, if you were trying to decide what to focus on as a young researcher, why pick genetics?
Mark: Genetics is fantastically exciting as a topic. I mean, the things that we can do are beyond our wildest imaginations of what we could do 20 years ago and looking forward I'm sure the same will be true looking forward in 20 years' time. It's a time where we're benefitting from fantastic technological developments. We're getting lots of novel insights into disease biology that it's hard to imagine we would have found in any other way. And I would think what we're going to see over the next 20 years is the translation of that knowledge into better ways of preventing and treating disease. And part of that will be down to using what we understand about disease biology to say, "Well, here's a pathway we should be perturbing. And if we perturb it, and we can find a molecule to do that, we might well have a safe and effective way to treat and prevent disease that we didn't have previously." But partly, it will be about embedding an individual's genetics in their medical record so that we can use it in many different ways to understand which diseases somebody might be particularly at risk for so that we tailor the screening in the way that we think about disease detection to fit with their genetic risk, to think about what treatments may be best somebody based on the knowledge of their genetics and also, the impact of genetics on side effects of particular treatments. So, I think we're going to see all of that play out, and I would be surprised if in many parts of the world in 20 years' time you don't have your entire genome sequence sitting in your clinical record and impacting on the ways in which you interact with the healthcare system during your life. Hopefully using that to prolong the healthy period of individuals, and, should they get disease, we’ll have a much better basis for treating and preventing it. But I definitely would say that we shouldn't neglect [laughs] all the other things that are not genetic that also are in that mix. So, I think, alongside the genetics, we're also going to learn much more about those environmental effects and how we can measure them and how we can mitigate them so that we can build individual profiles of disease and individual strategies for combating the risks of diseases that each of us is particularly prone to.
Maria: So yes, in, say, 50 years from now if you walk into a doctor's and with elevated glucose, and you're diagnosed with diabetes what do you think the treatment is going to be like?
Mark: Well, I'm tempted to say that we have failed if people are still walking in with elevated glucose levels…
Maria: There you go.
Mark: …because we would have hoped to be in a much better position of identifying those who are at greatest risk based on their genetics and other risk factors, and getting in there early with safe and effective strategies to reduce their risk. And we know that lifestyle and some therapeutics can be very effective at doing that. Should somebody have gotten to a point where they get disease then, I think we would have the wherewithal to understand a little bit better about what particular processes have contributed mostly to them ending up in that state. And therefore, be in a position to think about what is the right portfolio of therapeutic and non-therapeutic approaches that we should take to try and put the biology back towards normal so that we cannot just treat the diabetes, but, in some sense, cure it.
Maria: So, you might be able to say, "Well, your genetics tell me that exercise isn't going to work very well for you. You're not a very exercise-sensitive person, so I'm going to put you straight on to a medicine." Whereas with another patient, maybe I would say, "You know, you're really exercise-sensitive, and you need to eat more vegetables and less carbohydrates, and…
Maria: …that will probably fix you a bit more."
Mark: Yeah, so I mean…
Maria: Yeah. [Laughs] Being very simplistic.
Mark: But I think along those lines. I mean, of course, there are companies out there who will tell you that they can do that with your genomes at the moment. But yeah, that goes well beyond the bounds of science so at the very least… [Laughs] I hope that when those claims are made in 50 years' time they have a lot more science behind them…
Mark: …and a lot more evidence that they're actually doing what they promise.
Maria: Thank you, Mark. It's been so much fun talking to you today.
Mark: Thanks for the invitation. Great talking to you.
Wellington: Wow. Maria, what a great conversation. I started thinking about just how much data is going to be produced in the coming years.
Stephanie: Yeah, I agree. Especially when you start thinking about how to capture all of the diversity and variation across the human genome, sequencing every person's genome for example, how much data there is. And, is that information sufficient? Is that all we need to understand biology?
Maria: So that's such a great question. And I think the answer is, it's not, right? Because the genome, and your genome, and your genetics is just one piece of information. And you can see this quite obviously when you look at two identical twins. They're not the same, don't have the same diseases, don't have the same life outcomes, necessarily. And yet your genome is identical to your identical twin at the level of the fundamentals of the genome. So, it's one piece of information. It's really, really powerful. We're going to learn with these big data sets so much more about how genetics links to health and disease. But I think it's going to raise even more questions than it answers in how that intersects with all of the other things that are determinants of our health and life that are not coded in our genome. And we'll have to work on that too.
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And now for me, it's back to puzzling over data.