 My name is Charles Routime and I'm the director for the Center for Genomics and Global Health. I'm also the branch chief for the cardiovascular metabolic and inflammatory disease branch within the NHGRI intramural program. So I direct a team of multidisciplinary investigators who are interested in understanding you know disease distribution in different populations. I was born in Benin City in Nigeria where I did most of my growing up and I went to my high school and initial training as a biochemist at the University of Benin in Nigeria before migrating to the United States for further studies. After graduating in biochemistry from the University of Benin, then you're required to do one year of national service which is basically a combination of military training and also serving the nation. So after completing that one year, I applied to universities in the UK and also in the US. And interestingly I was accepted into University of Manchester in the UK to study petrochemical engineering and I was also accepted to the University of Mississippi in Ole Miss in Oxford, Mississippi to again continue my biochemistry in terms of food nutrition. So it was a very interesting thing. I presented both to my parents. I'm from a very humble home. So I wasn't at all sure that my parents we have the resources to send me to abroad for further studies. So I was presently surprised when my mother who's a trader said she could afford to pay for one year. It turns out that the US university fees was slightly lower than that of the UK. So that's actually what made the decision for me to come to the United States instead of going to the UK for petrochemical engineering. And so one year school fees was paid for me and I was basically on my own after that in the US. So my initial socialization to the American culture was actually at the University of Mississippi where I got my initial master's degree in health care administration before going to Birmingham, Alabama to study epidemiology. By those two thesis really was actually quite interesting. It's quite removed from what I'm doing now. It was actually looking at the impact of working in a foundry and engine plant for motor company, foundry and engine plant in Cleveland, Ohio in relation to lung cancer and stomach cancer. So the union was then was a little bit worried that there are workers we experienced in higher rate of stomach and lung cancer. So my supervisors then at University of Alabama were giving the contract to study to determine if this is really true and if this is actually due to occupational exposure or something that people are doing at home or some combination of this. So that was what my PhD thesis was on. It was basically using epidemiological methods to look at records and determine what kind of exposure. First of all, the time it was there increased risk and if there was why. And I say it was a very fascinating study. My story is one of those that really highlighted that it's really no one path to where you're going to become in the future. Right after graduating from University of Alabama, I got a postdoctoral position at Loma Linda University in California. So then I had a little Honda Civic that I drove across the country because I couldn't afford, you know, the other most transportation to start my postdoctoral work again with my family. And that was actually very fascinating. I was looking, I was working with a PI there who was studying Alzheimer's disease and that's what we're working on. One of the things I love about training epidemiology is once you understand the fundamental methods, you can actually apply to any disease. You know, so it was quite comfortable for me to be able to do my postdoctoral work within that Loma Linda University was a very, very wonderful university. But I think probably the most interesting part of that study is really why I was there. I saw this ad one evening in the paper by Richard Cooper. You know, that says that they're looking for somebody who would be interested in studying hypertension, you know, in the African diaspora in different African populations, and that he was interested in an epidemiologist, you know. So the way the ad was written, it was absolutely very fascinating. So I called Richard and I said, Richard, you must have written this ad with me in mind. I said, because this is exactly what I want to do. You know, so he, you know, invited me for an interview and accepted the job. So I moved from, I didn't complete my postdoctoral work, so I moved from Loma Linda to Loyola Medical Center in the suburb of Chicago. And that's when I started really, you know, studying and understanding her disparity and how to use the migration and design of the African diaspora experience to share light on why disease vary as you go from Rura Nigeria or Rura West African countries, you know, to the Caribbean and to the U.S. When we started, we really wanted to understand, first of all, what is the prevalence of hypertension as you go across these different populations who share very recent ancestry. You know, so we wanted to understand that. And then once we understand what the prevalence was, we wanted to see if we can share light on some of the factors that are driving these, you know, different prevalence as you go across these different African populations. You know, so we put together, again, Richard wrote the initial grant, you know, to do this and it was to enroll over 10,000 individuals. And we were quite happy to have been able to do that in a very, very rigorous way. We standardize the procedure because blood pressure is one of those things that are very quite a bit. So we needed to make sure that you are using the same procedure across the different sites. Otherwise, you won't be able to compare results. You know, so there was a lot of standardization that went through the process and we were able to come up with data for over 10,000 individuals from Nigeria. We have urban and rural sites in Nigeria. And in Cameroon, we also had urban and rural sites. Then in the Caribbean, we had St. Lucia, Barbados, and Jamaica. And of course, African Americans in the Chicago area, Maywood, Illinois. So the finding, you know, from this study really shed, you know, fundamental insight into the impact of the environment that people find themselves on blood pressure and hypertension distribution. So it was very clear that it varied from about 7% in rural West Africa to about 16% in the urban centers like Ibadon, Lagos, and then to about 26% among the Black nations of the Caribbean and over, you know, 34% among African Americans in the Chicago area. So you had this very, almost a monotonous increase in blood pressure and hypertension prevalence as you go from rural Africa, you know, to urban, you know, Chicago via the Caribbean. So it was very, very fascinating for us to find that. And then we shared lights also on that that gradient that we were able to establish was due, we were able to explain close to 60% of that variant by looking at things like salt intake, you know, how heavy you are, you know, and also level of physical activity, you know. So, you know, so you see the impact of the environment, you know, was really, really well demonstrated in that study. That does not mean that there are no genetic susceptibility. But if you are actually comparing and trying to understand head disparity, then I think you would get a much bigger bang for your money by looking at the environmental factors that drive these conditions. Yes. That's a very important question because one of the major challenges in doing even genetic study is the ability to characterize the environment. I will say that at some point in all of this stuff, even in genomic studies, the rate limiting step is really going to be the ability to characterize the environment, because that is what is always changing, you know, and you have to be able to do that very well. So for us, one of the things that we were able to do was to measure things like height and weight. You would think that measuring height would be a very straightforward phenomenon. It is absolutely not. For example, during one of our studies, we lost a lot of data points just because the interviewer who was measuring individuals did not tell people to straighten up against the wall, so we can use the studyometer to measure their height. So a lot of people were very short for their weight, so their body mass index was really high. And then you have some women, because in Nigeria, women wear headgear, and the headgear can be sometimes up to six inches. So some of the interviewers did not tell the women to take off their headgear before they were measured. So they were much taller than what they were supposed to be at their true height is. So all of those caused major problems in terms of your ability to measure. So things as simple as height can get very complicated if you don't standardize the procedure and you don't train very well. And the other thing also, there are some environmental factors that are really difficult, things like diet. For example, when we wanted to characterize salt, we felt that our best measure, because we were not dietitians, our best measure would be to look at it from a biochemical parameter point of view. So we collected urine samples. And from the urine samples, we were able to measure sodium potassium, and that gave us idea as to consumption. So we were using excretion to approximate intake. So you have to be clever about how you do some of these stuff. And then we collected things like education, income, occupation, which again are very, very important. In terms of the consent aspect, one of the things that we did very well is that we engaged the community and also the community leaders. And we made it clear what our intentions were and why we are doing what we are doing. And to let people know that for the very first time, we are contributing to people's understanding of why some people get high blood pressure and others don't. And how do we study this at the population level so that we can make recommendations in terms of preventive strategy. And also for people to just be aware of the fact that you don't feel anything doesn't mean your blood pressure is not high. So by doing that, I think we brought the community along. And another plus in doing studies like places like Nigeria, Cameroon, and in Caribbean is that people are still very receptive to research. And so it's not too complicated to get people excited about doing this kind of study. So that helped in our ability to be able to recruit people. And then another major factor is we employed people who are trying to live in those communities. So the participants in the study know the people who are working in the clinics and the people who are knocking on their doors in an accident to come up and participate in the study. So that was also very, very useful in doing to have familiar faces out there, you know, who people can relate to. Yes, there have been some very bad history in terms of biomedical research in the African American experience. And therefore people are skeptical, which is I think rightfully so that people should be skeptical. But one of the things that we do, I do in my study is basically to train my community liaison research, you know, assistants who go into the community to be aware of this history, not to run away from it, understand it and explain it to people. And the more importantly, let people know what are the things that we have put in place to make sure that the likelihood of something like Tuskegee occurring is much more reduced now. You know, so it's, I think for me it's important to acknowledge it, but to also let people know that that should not be a reason not to participate in biomedical research. Because the community will be the loser in the long run. But what you need to do is when you're participating in research, have your eyes wide open. The fact that I have a dark skin like you doesn't, it doesn't mean that you should trust me. You should still ask questions just like you ask anybody and make sure that the right things have been put in place. First of all, you make sure that my study has been approved by the appropriate authority and that you have somebody to call if you feel that anything is going wrong with the study that you don't like. And that person should be independent of the study itself, of the study personnel. So we explain all of those things and I will let people know why they have to, why they need to participate in study like this, especially in genetic studies. You know, and when you feel uncomfortable at any point in the process, stop, you know. But if you see that the study is going on in the way that you like, you should participate because that is the only way we can understand what is going on in your community. One of the things that I also observe, you know, with my mentor Richard Cooper, one of the studies is that when you see somebody who is diabetic or hypertensive, they tend to have the other condition. So if it's somebody who is diabetic, they tend to also be hypertensive, they tend to also be obese, you know. And so for me, it made absolute sense to try to study this condition. So I started looking at my whole research, not in the context of these very specific diseases, but looking at it as a metabolic disorders, you know. So bringing them together, you know, so I looked at hypertension, obesity and diabetes as a triangle, you know, and they feed into each other. And their ability to understand how these things are related, I think will shed tremendous light on how we prevent it, how we treat it, and how we even communicate to people about their condition. You know, so to me that was absolutely the way to go in terms of my own research, you know, in research activities. So I started with Richard Cooper with the Tense of the Appetention and we saw the impact of obesity. And then during that process, we started talking from this whole discussion about diabetes, started actually with Francis Collins and Georgia Dunstan. So Georgia Dunstan, who's a professor at Howard University, you know, was doing a sabbatical in the Francis Collins lab at the NIH. But before then Francis has done a missionary work in Nigeria where he treated quite a few diabetic patients. And so I think that stopped with him for a long time. So when the opportunity came about in terms of discussing how can we study diabetes in West Africa, with Georgia in his lab. So there was a discussion about how do we move this forward. And so I was brought into that picture as an investigator who was doing work in Africa to see how we can put together a study that will shed light on, first of all, what is the risk of diabetes and what are the factors that are contributing to diabetes from the environmental point of view and also from genetic point of view. So that's how the diabetes component, you know, started in a sense. At that time I was already very, very interested on the genetic contributions, you know, to these various diseases. Because we studied from the epi point of view and we saw that there were still some pains that we noticed. For example, why is it that you tend to see hypertension run in certain families and diabetes run in some families and not in others. So there was aspect of what we were studying that was not completely explained by environmental factors. So we wanted again to bring genetics to bear on that. So I think to call it a long story short, I accepted to come to Howard University to lead the genetic epidemiology unit. And I tend to be the director of the genetic epidemiology. So the genome center there had four major areas. It had genetic epidemiology, it had statistical genetics that was led by George Bunny. And it had molecular genetics, which again was led by Georgia Dunstein and Ray Kiddles. And it had ethics that was led by Charmaine Roy. And it was a very, very successful center then, you know, attracted a lot of research fundings. And we did quite a bit of what I consider very good work in shedding light on genetic and environmental determinants of different diseases, cancer, diabetes, hypertension, and across the African diaspora. There are several two kids and I think depending on your approach and how you want to go. And also the state of technology tends to be very, very critical because when I started out, you know, in terms of trying to look at the genetic, you know, contributions to these diseases, at some point we were able to look at one gene at a time and, you know, do some, you know, basic, you know, you know, SNPs, you know, in these genes. And, you know, we call that the candidate gene approach, you know, then. And then, of course, with more markers, you know, coming in as a result of things like the HAPMAP, and we're able to do linkage studies, you know, which, you know, basically, you know, helps you to determine regions in the chromosome that is tracking the disease, you know, in families, you know, basically. And again, it's based on the principle that things that are related together that are close together in the chromosome, you know, stick together during meiosis, you know, in the sense they are not broken up. So you can use a marker to track, you know, that chromosome region. And if you see a signal, then you can do more work within that region to see if you can actually localize the specific gene. So there's, there was a linkage study, and it is your association study also, which basically is, you know, you're looking at people who have the disease and people who don't have the disease, and you are trying to see, do I see an increased risk when I look at these people and all decreased risk, so it can be susceptibility or resistance. And then you basically, that is based on having a large number of people who are cases and large number of people who are controls. And you do your association study and use statistics to see what's the relationship and see if that relationship is significant. And then you make in your, you make your inference. But all of these, you know, association study became actually practical as a result of the sequencing of the human genome and the subsequent spin-offs like the HAAP map and the Tauzon genome, because we couldn't do it, you know, cost effectively enough before, before all of these things, you know, came about. So those are some of the tools that we have, you know, to map, you know, these various diseases. The complex diseases, what we call complex diseases, or diseases that are not Mendelian, which are basically as a result of multiple genes and multiple environmental factors, are pretty difficult to track, you know, because you're not just, it's not just one gene and it's not just one environment. It's the constellation of different things coming together to increase risk or decrease risk, you know, so it's quite challenging, you know, a process. The challenge, you know, is really how do you begin to tease all of this apart in a way that you actually are able to quantify your risk, my risk, if we carry these markers, and maybe in the end design drug targets, you know, for these various, you know, genetic markers in a way that you can actually treat people or come up with better medications. It's a very, very difficult thing, you know, we all know, even at our own personal level, we all know, sometimes you know the reality, but you just are not able to implement some of these things, changes to take advantage of the information you're getting. But I think the basic thing is letting people understand the concept of risk and the concept of probability. Okay, and that is a chance. You can come from the various activities that people engage in on a daily basis, you know, that if you have a deck of cards and you have more of the cards that are similar, you see, the likelihood of picking that card that are more than one in the deck is higher. Okay, so if you start with that concept, so basically what you're saying is when we find people who have 10 types of this type of genes, their risk of diabetes is much higher. Okay, and what that means, it doesn't mean that they are going to get diabetes tomorrow, what that means is if they put in place, you know, things like reduce your weight, do more physical activity, watch what you eat, that they might indeed be able to overcome that genetic load that they carry. But it's something that you have to do for the rest of your life. It's not just do it for one month and you know, you give it up, but you have to maintain an ideal weight and, you know, eat properly and do your physical activity. But we also have to be realistic when we are communicating that and tell some people that if you have a very strong family history, that you may be able to postpone when this disease will occur, but you may not be able to prevent it completely. So we have to go through all of these dynamics and all of these ways of explaining risk and probability to people in a way that people can understand and implement it in their daily life. So I think it is an issue really of education and communication, so it's not just the scientists at this point, you have to bring in people who actually have the skills to be able to communicate, you know, no risk to individuals. So it's, again, a very daunting process. The genome-wide association study, what we call GWAS, is really, I think, one of those fundamental tools that have really changed biomedical research in a way of looking for disease gene association. It's really revolutionized in what we can do. And that came about because of our efforts in the HAPMAP project, the international haplotype mapping project, because before then you couldn't really interrogate the genome. It was too expensive, you know, to do. It wasn't practical. And we really didn't even have an idea of all the markers that you could use to do that kind of study. You know, so investing in the HAPMAP project at the national and international level really led to the genome-wide association revolution, in a sense. And it also led to the ability of the managed biotech companies to be able to develop this genotype in arrays in a way that they could be used from one study to another in a more cost-friendly manner. I was involved in the discussion right from the beginning. And we recognized that, again, given the evolutionary history of humans, that we all started somewhere. Our ancestors started somewhere in Africa. And before we migrated to different parts of the world. And because of that, you just have more variation, you know, in African people. So it made absolute sense that if you were going to characterize human genetic variation that you have, that the African population is represented in that effort. So there was right from the very beginning that there was a discussion to at least represent some of the continental diversity that we currently have, given limited resources. We all knew that that wasn't the complete answer to all of this. But we needed to start somewhere. And those four populations, you know, the Europeans, Japanese and Chinese, and also European, were initially put together in terms of the HAPMAP project. So I was involved in that discussion of which population to sample from Africa. So I was the PI of engaging in the African community for the HAPMAP project. And I did that with my colleagues. And we were able to engage the Yoruba community in the Badon area in Nigeria to participate in this effort. That was my very first full community engagement activity to really tell people why they need to participate in this study. And to hear from them why they may want to or why they may not want to participate in that effort. So again, right from the very beginning, I think we were very cognizant of these various aspects of the project that needed to be brought to bear. One of the distinctions between the HAPMAP project and the diversity project is really the fact that we wanted the HAPMAP to be biomedical research based. It was health. We are designing a tool to help us understand human health and disease. Not necessarily the what I'll call the other gains came later in terms of population genetics and all that. But the main focus was how do we characterize the human genome in a way that will become a tool for us to be able to understand health and disease. That was really the driving force behind. And that set HAPMAP aside from all of that diversity project or variation project that was going on at that point in time. And that also helped us when we were doing the community engagement to be absolutely clear to people why we are asking them to participate in this study and what their resources will be useful. Again, the process was really identifying key investigators who work with me on this, engaging the African community. In the Badon area, for example, engaging the Euruba, the local PI for that project was Clement Adebamo, who was a professor of surgery at the university very close to the community when we were doing this work. So that was a key person. And then I had Shamay Royal and also Patricia Marshall who were ethicists and social scientists who knew how to engage communities. So it was bringing these forces together that we approached the community. First of all, we identified the elders of that community. We identified the leadership structure of the community. And then we approached them with the help of Clement Adebamo. And we told them about what the study was all about and what we want to put in place to make sure that individuals who participate in the study are protected and for them to actually understand what they are doing. And the informed consent that they are going to sign, how the resources are going to be used, how they are going to be broadly shared. And also the fact that no identifying information will go with the samples, you know, that even if consent forms that are signed will not come to the U.S. It will stay in Nigeria. And so those samples will never be linked, you know, to an individual in a sense. So we went through all of that community engagement, letting people know what the study is about. To our own fascination was when we started talking to people, we were quite surprised as to the level. Scientists tend to want to underestimate the level of understanding of community members. And I think that's a big mistake. You know, the fact that somebody cannot say haplotype doesn't mean that you don't understand what you're doing. You know, so we were very, very surprised as to how people were able to relate back to us what do they understand the HAPMAP was going to do. I remember very clearly, one of the comments made by one of the ladies who were participating in this community engagement was the fact that she said this will enable us, you know, understand how people who left Africa are related to us now. You know, so that was a fundamental way of really articulating what we're going to learn from the HAPMAP. To me, I was completely blown away when somebody we thought, you know, didn't understand what we're saying can actually put in such simple language. So we started using that expression ourselves, you know, to explain the HAPMAP, you know, to people. So it was absolutely quite fascinating. The whole community engagement was quite interesting. And the other thing I would say is don't engage the community if you don't want to hear what they have to say. You know, because community can tell you things you don't want to know. That you didn't think about why you were designing your study and some of the concerns that they may have about the study. So when you are doing community engagement, you have to have an open mind and be prepared, you know, to deal with issues that you did not think about as a scientist. She was really the overall coordinator from the NIH point of view and, you know, Jean and McQueen and also Lisa, you know. So Jean especially went with us, you know, multiple times to Nigeria. She participated in the community engagement aspects. And so she brought expertise from the ELSI, you know, the ELSI and all this point of view. And she had a wonderful understanding of the informed consent process and what are the things that needs to be put into that document to make sure that people really understand and appreciate how their resources were going to be used. So Jean was there again when we were engaging the community right from the beginning to the end. And she was absolutely invaluable to the whole process, you know, in terms of how do you do this, you know. How do we put this in place in a way that we can actually call this community engagement. And I think HAPMAP set an example that I think is really difficult to beat. Because up to now, the community advisory group still received documents from Korea, you know, about a repository in New Jersey about how those resources are used. That is incredible head of environmental research. So the standard that HAPMAP sets is extremely high. And they have lived up to it so far. I regard Francis as a mentor, you know, basically for my own career development. And we, I have always, you know, seek advice from him and talk to him about things. And we have been engaged, you know, again with the Diabetes Study and also, of course, with HAPMAP and the 1000 Genome. And when I was coming to the NIH, it was basically a discussion between myself and Francis in terms of setting up the center that I lead now at NIH. And then we brought in Eric Green, then, who was the scientific director at that point in time. So I really do see Francis as a mentor in my own career development. And of course, he's the one of the brains behind the sequencing of human genome and also the HAPMAP project. I think he had a vision about the fact that we can actually characterize imaginative variation and use it to understand human health. And that, I think, was a driving force, you know, for Francis. Again, I think Francis has, you know, pretty broad base, you know, knowledge, broad knowledge base. And he appreciated the fact that you have Mendelian diseases and you have, you know, complex traits. And his own interest in diabetes, really, again, is illuminating in that sense that these are, you know, diseases that you need genetic epidemiologists, you know, in the sense of bringing in the environment and bringing in the genetics and try to understand how these things influence each other, you know, to increase risk of disease. So we really talk from that point of view of, you know, complex traits and also from a point of view of making sure that genomics is a global exercise, not just exercise of rich societies. You know, so Francis is absolutely passionate about that and that is my own complete passion in the sense that we, whatever gains we are going to get from genomics that we make sure is global, that it supplies to all human populations. And so when, for example, what we're doing is starting to do, like the Hartman project, you know, discussions with Francis and other, we could have sampled Europe by people in America, okay? It would have been much cheaper, probably very much faster. But I was absolutely opposed to that. And Francis appreciated that completely, that you cannot do something like the Hartman project if you don't go to where the people you are sampling actually live, you know? And how can you actually engage that community and say you've done a community engagement when you are doing their engagement in America, but their home is thousands of miles away. So he was pretty clear during that discussion and Francis appreciated that very well when I indicated that we have to go back to Africa to make sure that we actually do the community engagement and let people know what those resources are going to be and engage Africans on the African continent, not in other parts of the world. My take on understanding human generation, variation period is we have to sample as many human populations as possible, whether they are small or big. I think we have to cover the breadth and scope for us to actually fully appreciate what is the complete picture in terms of human genetic variation. So the more population we sample, the more population with genotype or sequence, the more we are going to know about variation. But we also know because the human history is very, very recent history in relation to other organisms, at least in relation to the planet itself, that we are very, very recent. And because of that recent history, we share a lot of our variation. So very few human populations can adequately represent the variation up to 99.5% of the time. We can capture that. But that's 0.5% or so that is left. It tends to harbor quite a bit of some of the things that we are interested in in terms of health. So it makes sense to sample people, not from the point of social descriptive like race or ethnicity, but from the point of view of history, of human history and migrations, where people have lived, where are the sources of variation likely to have occurred from? Diet, climate, you know, those are the things that shaped the genome. And sometimes those things coincidentally overlap with our social descriptive of ourselves, but that's really not the purpose. That's not the evolutionary purpose of those, you know. So for example, if you're interested in kidney disease and the APOL1 gene, for example, you will only see it in the part of Africa that was endemic to trapanosomalysis, the so-called African sleeping sickness. You don't see it in other part of Africa and you clearly don't see it in Europe or Asia, you know. So it's not even a question of doing Africans. It's a question of doing that part of Africa where they needed to survive that kind of very dangerous disease. So for me, it's very, very important for us to appreciate what we do, what we do, and not to overlap it with our social notions that may distort our understanding of human genetic variation. The whole concept of, say, the rare variant, for example, I think if you push that and push it and push it to its limit, it becomes an individual project. You have variants that you may carry that your parents may not carry, you know. So it's where do you stop and where do you slice this, you know, I think becomes, you know, some of the things that you have to consider. Clearly, the more populations with sequins, the more diverse populations with sequins, the more we're going to pick up on rare and rare variants. There's absolutely no doubt about that. But we're not doing that from the notion of race or ethnicity or, no, we're just doing it. Why do we have rare variants? It started from a place. It has no hard time to spread. And if that population mates with another population, you know, you may carry that variant, it may start to spread. So something is rare to the point of, it's a time thing. You know, the more time it passes, the more likelihood that that thing will spread to other parts if there is no gene flow restriction. For the phase three, we wanted to make sure that we expand beyond just the continental thinking for the phase one, you know, that led us to identify a little over a million, you know, markers, you know, in a sense. So we wanted to expand it to other ancestral populations globally. And some of it was convenience. You know, some of it was scientifically strategic, in a sense, but really it was trying to say, how do we capture more populations from Europe? You know, how do we capture more populations from Asia? How do we capture more populations from Africa? For example, the African part, you know, that I was engaged in, we wanted to make sure we capture individuals that are from different parts of the language group, the major language groups, you know, for example, the Bantu expansions, you know, the Nilo-Saharan, you know, we wanted to capture, so the Maasai, you know, the Luya, the Yoruba, we wanted to expand that in a way that we know that some of the evolutionary history of those various populations may have introduced differences that needed to be captured, you know, in a project like the Hapma. You know, so it was basically trying to involve more global populations in a way that we can capture more of the variations. I think, for me, one of the major things that happened to us is indeed that we are indeed very, very similar as human beings, that we share a lot of things, and that there are some, you know, things that we don't share. And when we quantify that, it turns out to be about 10 million, you know, single-nucleotide polymorphisms that tend to differ, you know, between two individuals or when you compare individuals, you know, so and that those differences tend to be important when you're talking about health and disease, you know, in a sense. So Hapma helps us to understand, you know, in a very clear way that a lot of things that we share, a lot of things, the variation in the human genome is shared, you know, what we call the common variants. They are common, you know, because we share them, you know, and it's also based on the fact that the human history is indeed very recent. So we still share a lot of the variation that we see. So Hapma was very instrumental in communicating that message. He also enabled us to now have a map of where these things are located in the genome and in a way that we can genotype them in a cost-efficient manner. And for the very first time, scientists were able to interrogate the whole genome instead of specific genes in relation to human disease. That really was indeed fundamental in biomedical research. It is the beginning and the major thing that made GWAS happen. It wouldn't have happened without a Hapmap or a similar project to generate that kind of database that the Hapmap generated. And Hapmap also gave us understanding of, you know, signals of natural selections where you could actually look in the genome and you see where the environmental contests have shaped the genome in a way that is specific to that part of the world. Again, we get very good knowledge from the Hapmap. For example, about something like Lysar fever or even, you know, skin color or hair color. Those things were adequately characterized by the Hapmap project here. What it has given us is we were able to sample populations that were not part of the Hapmap or the 1000 genome. Okay? So the very first contribution from the African genome variation project is really expanding the level of diversity that we now have in terms of samples and data and understanding of variation across the African continent. So we, again, one of the fundamental things that we need to appreciate and appreciate fully and we're just beginning to appreciate it very well is the huge diversity on the African continent and we needed to sample more and more from the African African continent to capture this diversity. Okay? Because of language and barriers, you can travel, you know, 200 kilometers and be in a completely different environment where you don't understand where this person is talking in a sense of language and nothing restrict genetics or gene flow better than language in a sense. So there's a lot of characterization and of course humans have lived the longest on that continent. So I've had opportunity to have more variation in a sense. So by sequencing and genotyping more African population and putting that in the public domain we are contributing to the efforts of the HAAP map and the Towson genome in a way that will serve understanding of disease genetic basis of disease on the African continent. So that is what the variation project is really about was to expand the African populations that are now available in publicly available database and then to also we're able to show the science of natural selections we're able to that resource we also contribute to genotype in the GWAS array that we are developing for the H3 Africa or doing research in Africa. One of the things that has been very troubling to people like me and people who study African populations is that the initial generation of GWAS chips were not very efficient for African for interrogating African genomes. Again just because most of the variation and most of the characterization came from European and Asian ancestry populations. So they were not very efficient for African populations. So by characterizing more Africans populations and then making those data available and then the biotech companies are grabbing these variants and they put it on the chip we are now having more efficient African chips. Some of them are newer generations what I'm using in my lab as we speak to look at diabetes and other diseases. So we are making these tools more efficient by genotyping more African populations. The genotyping for the African variation program was actually done at Sanger in the UK and with our colleagues there at the World Control Sanger Institute in the UK. But the H3 Africa project that the human heritory and health in Africa project is actually putting in place infrastructure of genotyping now in Africa. You are beginning to see evidence of that. So not just doing the finding disease gene but also creating opportunity for people to understand the technology and be able to use it locally created incentive for jobs and also for training of young men and women in this technology. Again the fundamental vision principle behind the H3 Africa is really to empower African investigators to do research about conditions that are important to the continent and to do that research locally and then to be able to analyze it and write the papers and then increase their ability to compete for subsequent funding so that biomedical research can go to a much higher level on the continent. So that is really the driving principle behind the H3 Africa. So you now, before H3 Africa what you tend to see was African investigators, they don't talk to each other. They talk to people in Europe, people in America, people in Asia because they were basically following the money. Now the H3 Africa is funding investigators and universities in Africa directly. So although they collaborate with whoever they want but the money goes to the African institution and the African investigator so therefore empowering them to drive the fundamental questions that they want to address that they think is important to their people. The common fund is really, it comes out of the director's office from Francis Collins office or whoever becomes the director of the NIH and it's basically the director is designed to help bring novelty fund projects that we otherwise not get funded through the mechanism but to catalyze biomedical research in a way that is not easily done through the other mechanism. So it's a very, very wonderful idea and so that's how H3 Africa is funded because it's really putting aside some of money allowing African investigators to come up with their own specific questions and apply for that resource. So the common fund has been instrumental of course along with our colleagues from the Wecom Trust in terms of their contribution to this effort. So between the Wecom Trust and the common funds the H3 Africa has indeed really revolutionized genomic studies on the African continents in a way that would have been otherwise very, very difficult to do. That whole discussion with me and my leadership with the African Society of Human Genetics our colleagues there worried about the fact that African investigators were not participating fully in genomic research and we wanted to change that. It is high risk high reward project it is because we really don't know how all of this would play out with time but we are hoping that the initial success stories seems to be pointing in the direction that this is going to be more of reward than risk in a sense but it was a leap of faith to put in this kind of resources and say let's go for it in terms of the African continent and I'm very extremely proud that the African investigators have stepped up and really made us very proud made this a success story. The H3 Africa is funding multiple types of research. It's funding diabetes research which is again almost in 11 countries now. It's funding microbiome project it's also looking at cervical cancer on the continent and it's looking at cardiometabolic disorders across multiple countries and there are other conditions like trapanosomalysis and also pharmacogenomics in terms of how do we better understand how people respond to drugs so it is a really wide research areas that H3 Africa is funding and we are experiencing some very very initial good success stories coming out of this it's very important that as we use genetics, genomics to define drugs and design drugs it is very important that we have to understand the fact that we need to bring all global populations to bear on this because we know that for reasons that we've talked about earlier in terms of natural selections and just being in one location versus another for a long time structures are genome somewhat differently and that we can respond to drugs differently and therefore if we don't understand the global implications of imaginative variation in relation to that drug then we may not fully appreciate the side effects or even the efficacy of such drugs as we go from one community to the other the way I sort of phrase it is with tomorrow's medicine work for all I think that's really sort of the bottom line and we have to make sure that it does make sure that tomorrow medicine will work for all human populations and that when it comes to pharmacogenomics really I look at it as going to the tailor if you want your clothes to fit you have to present yourself to the tailor to be measured if you rely on my measurements guess what your clothes is not going to fit you properly that is really the bottom line with pharmacogenomics and with the whole concept of precision medicine that we have to characterize human population as deeply as we can and at some point it has to be at the individual level I think the thousand genome basically built on the heart map the heart map we use technology to genotype you know basically we are putting you know identifying things across the genome whereas the thousand genome use a combination of these factors including sequencing and basically that allowed us to identify things that we couldn't pick up with the heart map and so we have more detailed you know fine-grained characterization of imaginative variation instead of 3.1 million we are talking about over 80 million variants being identified with the last publication of the sequencing of over 2500 individuals with the thousand genome so again all of this one of the things I have appreciated about the genome effort starting from the sequencing of the human genome is really the appreciation of the fact that we need to start somewhere and then build on that and build on that and continue to build on this sometimes some people see that as a criticism but I think I see that as a realistic way of approaching a major major biomedical initiative and I think the success so far seems to have supported our strategy of continuing to build so we said thousand genome but we the final paper actually sequenced over 2500 individuals because again we know the more we sequence the more we are going to be able to capture these variations especially the rare ones and they have a much broader and deeper final map of the human genetic variation so again one of the things that the thousand genome is doing for us even in the context of GWAS is we now have a much more comprehensive reference panel for imputation what do I mean by imputation that is basically when we do GWAS and we use this genotyping arrays there are gaps ok so the typical one may give you like 2 million you know snips but if you do imputation and you fill in those gaps using that reference panel you can get up to 20 million and that just increases your power to find genes you know in relation to disease and health the bioinformatics I think without that would not be would be no way really because you can sequence all you want if you don't have the tools and you know how to interrogate that sequence and characterize them in a way that people can interact with it you really all you have is just the mess of data on your computer so the bioinformatics tools that were developed from the half map the thousand genome has really really enabled us to have a much better appreciation of human genetic variation and how to relate that you know to disease so that is fundamental delivery when it comes to the thousand genome or when the half map you know project that bioinformatics strategy and you know real strong computational biology understanding and very clever people who are able to interrogate and design software that help us to interrogate this kind of huge data that you really cannot it's not your typical excel spreadsheet you know that you put this kind of data on so you really need people who understand bioinformatics and also you know computational work and how to design software it's really you cannot over emphasize you know the role of bioinformatic it's just it's central to you know to all of this otherwise you just have a blob of data and you can make head or tail out of it it's the annotation of that data making sense of that data and putting it in a way that people can use it you know otherwise you know if the half map data was in friendly nobody would go there nobody would use it so to be able to structure in a way that somebody in Africa or in China can log on and be able to download that data and be able to use it with clear documentation of what has been done it's just it's absolutely fantastic you know for me I think it's important for us to appreciate you know projects like half map 1000 genome and African genome variation in you know history Africa because of what they do they tend to revolutionize our ability they give us tools that we otherwise don't have we don't have those kind of international effort people coming together from different parts of the world to see we need to develop a human good you know that everybody can use to understand biomedical research absolutely I think it will be tragic if we go through all of this and we exacerbate disparity instead of making disparity go away you know so I think by representing different human populations and being careful about the way we look at the results I think we would indeed share light on human history how we're related and human health in a way that we couldn't do before