 Well, good afternoon, everyone, on this glorious day, beautiful spring day in Washington, D.C. Thank you all for coming in and protecting yourself from the sun for a little while. And you can go back out again. But I'm here at Green Director of the National Human Genome Research Institute, and I want to welcome you to the third lecture in our Genomics and Health Disparities lecture series. For those who are joining us for the first time, I want to tell you the seminar series that we've put together brings speakers who are recognized not only for their contributions in genomics, but also have extended their focus to address questions of how the field can address the limitations in genomic study, population diversity, and also workforce diversity, and also increase health equity and access to genomic and precision medicine. The speakers that you've been hearing from, and we'll hear from today, are really demonstrating different approaches to the problem from their own unique perspective and their own unique involvement in genomics research, and we've really tried to span the full landscape of genomics research from basic science, population genomics, translational, and clinical research. And also you will note that the lecture topics have crossed disciplines, and as you will see from today's talk, actually crosses borders. In addition to NHGRI, we're fortunate to have four cosponsors for this lecture series that includes the National Institute of Diabetes and Digestive and Kidney Diseases, the National Heart, Lung, and Blood Institute, the National Institute on Minority Health and Health Disparities, and also the Office of Minority Health at the Food and Drug Administration. So we thank all those groups for partnering with us to put this series together. Well, we're particularly fortunate today to welcome our speaker, a good friend of mine, Dr. Geraldo Jimenez-Sanchez. Now, Geraldo received his MD from the National Autonomous University of Mexico and his PhD in Human Genetics and Molecular Biology from Johns Hopkins School of Medicine. He's trained as a pediatrician, also as a geneticist, he's an author, and he's also a businessman. He's perhaps best known, at least in genomic circles, for becoming the founding director of the National Institute of Genomic Medicine in Mexico. But his credits certainly go beyond that. I'll tell you at a personal level, I've gotten to know him quite well. He's hosted me a couple of times in Mexico, including at one of the opening events for the Institute, the National Institute of Genomic Medicine, and then I returned there recently to see how what a vibrant research organization that has become even beyond the time that he has departed from that institute. Well, he led the Genomic Diversity Project in Mexico, where his group published the first catalog of genomic variation in the native and admix populations, and in this and in subsequent work, he sought to connect the advances in genomics research with innovative and accessible medical therapies. Prado has also held numerous positions in international organizations, including the United Nations Scientific Advisory Committee, the World Health Organization, the Organization for Economic Cooperation and Development, and is a past council member and current chairman of the Committee on Genomics and Bioeconomy for the Human Genome Organization, or HUGO. He currently wears a couple of hats. He teaches and serves as a program director of Genomic Medicine and Bioeconomy at the Harvard School of Public Health, but is also the executive president of a company called the Global Biotech Consulting Group in Mexico. Well, we're delighted that he can join us here today to share a unique and international perspective on genomics research, diversity, development of genomics research in Mexico and Latin America, and also health equity. So please join me in welcoming Dr. Prado. Jimenez Sanchez will be giving a talk entitled Genomics in Mexico, Implications for Healthcare and the Bioeconomy. Thank you. Thank you so much, Eric. Good afternoon, everyone. I'm delighted to be here with my colleagues and friends. I want to thank Eric and the GRI and the hosting organizations. Very happy to see colleagues, former colleagues from Hopkins and colleagues that I've interacted with throughout the years. And today, I'd like to share with you some of the lessons or probably the principles that we have learned from working in an emerging economy such as Mexico. So when we talk about health and economic disparities, it is sometimes hard to see, we need to dig in and look into the different communities to find these kind of disparities. But in emerging economies like Mexico, where they are evolving and they come in and develop the country, there are some who can go along this challenge. And there are some that can't go at that speed, that can't make it and turn it into a developed individuals from a developed economy. And thus, you get to see a more dramatic disparity in terms of health access economy. And so what I will be showing and sharing with you today is that our genomics program, or what we've developed, both genomics, population genomics, genomics medicine, and more recently, genomics into the bioeconomy can help, can help meeting with those challenges related to genomics, to health access, and the bioeconomy, that is economic growth. So first of all, that is the reason why I thought that showing sort of the overview of what we have done within the last decade in Mexico could be helpful for this purpose. So you know, so you remember Mexico as the country just neighbor down south border. And it is the 11th and most populated country in the world. And it's the second in America. And the one, the first among those countries where people speak Spanish. And so we were just recently, this earlier today talking, Eric and I, how the language barrier can be a major challenge in important science and technology into a community that does not speak the language in which that those discoveries and innovation were produced. And so Mexico has 65 different ethnic groups. And when I say ethnic groups, I'm referring to groups that would have their own language. That is, in many of them, they don't even speak Spanish. Some of them are bilingual, but many of them are not. And so there is a target for, or an area where disparities are extremely evident. And where we are proposing that, genomics could make a difference. So if you go from one place to another, geographically distant parts in Mexico, they have different ethnic background. And in terms of genetics, as you know, this is tremendously important. And Mexico, as I mentioned, is going through a demographic and epidemiological transformation, a transition. So you get to see the diseases of the poor and the diseases of the industrialized countries. That is to say, you see infectious diseases at its most. And you get to see more and more obesity, diabetes, and cardiovascular disease. So that's a major challenge and a major opportunity for genomics to influence how we conduct healthcare down there. So it was early in the century when we started planning what genomics could do for Mexico and whether it was worth joining the genomics era for a country like Mexico. And when I say a country like Mexico, not only do I refer to what I just mentioned before, but I'm talking about a country that was not part of the human genome project. So we were way behind down there in terms of the genomics era. And the considerations were, is it worth jumping in at this point? So we started planning stages and trying to see how genomics could help, as I said, health equity and development in Mexico. And so we thought, you know, genomics medicine will benefit the diseases, as I said, of the developing industrialized countries. It will contribute developing a more prevention-oriented public health policies and promote a more reason-expansed in healthcare. Those were part of the reasons why we decided to move on into genomic medicine, or Mexico decided to. Genomic medicine can contribute to better health and social well-being. And if we do it timely, and timely, that's the key word, if we do it timely, it will contribute to reduce social gaps. Whereas if we do it late, it will only increase those gaps because then we will be able, we will be at the mercy of those countries, say, who have developed genomic medicine. And then those who didn't participate in innovation will just have to pay the price of using their knowledge and products of innovation. So we thought that, you know, genomic medicine cannot be imported, and this is the admixture in the background reason why we couldn't just cut and paste from what it's developed in the European and North American countries into a country like Mexico. And it will certainly stimulate scientific research and local health problems, building infrastructure, and a country's competitiveness in the context of a knowledge-based economy. Finally, we thought it would be a good driver for knowledge, as I said, for innovation, knowledge-based economy. So we worked intensively with the Mexican Government Academy and the Society to put together an institution that would coordinate all the efforts in genomic medicine in Mexico. So we did that. So this was in 2002 and 2003, and it was in 2004. And we see President Fox at that time signing the law created our National Institute of Genomic Medicine, the 11th NIH for Mexico, our NIH-GRI equivalent down there. And that was 2004. And later, so we started working in temporary, what did I do? OK. So we started working in initial facilities. And then the following year, I remember, we started building what would be our NIH-GRI or in genomic medicine. And here you see September 2005. And to the right, we see former Secretary of Health, Julio Frank, and Professor Sobrano and myself, and Francis Collins, who was gracious enough to come and support this initiative for this is the groundbreaking ceremony, what turned out to be later this building, which is in the campus of the NIH down south Mexico City. Then so we started working in different projects. One of them, one of the starting projects, was the Mexican Genomic Diversity Project. And this was, as Eric mentioned before, to produce a catalog of genetic variants that could help in taking into account the atmosphere for different genome-wide association studies and other projects that we had in mind. So one of the initial motivations for this project was that the phase one of the HAPMAP project at that time did not include any Hispanic or Latino population. It was focused on the ancestral Asian, European, and African population, as you remember. And so we decided to conduct this research program and very much in communication with the Lander group and the whole HAPMAP group so that we could work along similar lines. And we ended up genotyping about 1.5 million steps in copinomic variants in the Mexican populations, both in Mestizo, that is the most common mixture of indigenous and Spanish group, European groups, and indigenous populations in Mexico. And we did the whole analysis as to characterizing the makeup of the genomic populations. And then we made sure that we published that for everyone, academic-based and private-based in Mexico and internationally so that we could profit all from that. But probably the major point I want to make about this part of the project is that we were very focused in engaging the underserved population, the underserved communities, mainly the different indigenous groups and those groups that were contributing with the blood sample that we wanted to make sure that we were really knowing what they were doing, what the importance of this was. And so we started, we did a top-bottom approach and then bottom-top. And here is just to, you don't need to look at details, but this is just to show the states where we sampled at-mixed and indigenous populations. And in the small figures, pictures around the map, there is the governor of every state supporting the initiative. So this provided a lot of credibility to the project. It is today, when I see back this, is 10 states with the governor being the first ones to donate a blood sample. That was quite something, as I said, an example for the community and making sure that the community will trust what we were doing. And so we invested a lot of time in informing the community. And one of the things we did with the consent form, we did a very explicit consent form. And so we did lectures all around the country. And before sampling those individuals, we would publicly share the consent form. So we will leave those consent forms in the form of a poster and leave it for a week. And markets, main plazas, universities, libraries, so that people will freely look at it and decide whether they want it to join or not. To my surprise, the day of sample collection, there were long lines before we opened at seven in the morning, people who wanted to contribute to this project. And not only that, but we also did it in the indigenous, in the ethnic populations in a very similar way. It wasn't easy to get to those populations either because of their own social structure or because of the remoteness of where they're located. But just to share with you and this figure and this photograph, we see the same consent form. And for those of you who can read Spanish, the first three lines are in Spanish, but the rest of it is in the indigenous language and the ethnic language. Pretty complex to translate genomic diversity in those concepts that were hard to explain. But the whole, again, the whole six-page consent form was translated and used in the form of a poster. And we see here Alejandra from my group and other people from my group going through the process of consent publicly in the open so that people, whether they will consent, they will take a copy of their consent form and take it home so that it could always have it and see who to contact and how to go about that. And we sample different indigenous communities around the country. And as I said, 65 different ethnic groups, different languages that are not even similar. It's not like English and French. It's just completely different languages. And so it was pretty complex, but it turned out to be pretty useful in terms of having consent and engagement from the community. So time passed and the year later, we had the results of our HAPMAP or HAPM type catalog of our population and this is just the principle components plot. But what I want to show here, these are the three ancestral populations from the HAPMAP, the original HAPMAP. And so question was, where would Mexicans plot? Will there be a concise group just like these three ancestral populations? And it turns out when you plot them all together, it turns out that they are, first of all, their smear of distribution in this plot. They're not really overlapping any of the ancestral populations. And to our surprise, we thought, on the one end, you have the European component, which makes sense. And then you have the rest of the mestizo Mexicans, some of them with the more towards a more African ancestry. And this makes sense. These are the two states in Mexico where you get to see African ancestry. But then we were expecting that the Asian would be on the other end here, since they came all along to America and funded these areas. Well, it turns out they weren't, they were far from here. And when we analyzed the very indigenous population, so this is the Mex population, and the indigenous populations turned out to show up there. That's the sapotex from Oaxaca in Mexico, which clearly showed the other end of the spectrum. And so it really showed that analyzed in an ethnic population, it's not as straightforward as analyzing just a ancestral non-mixed population. So when we ask what are the proportion of ancestral contribution to the Mexican populations by region, we see here in the four columns, this is from the half map, the European, Japanese and Chinese, Yoruba and from Africa, and here in blue is the Amerindian. And you see the six states here, I'm showing six states and average. You see some of them have a more European contribution, whereas some others like Guerrero and Veracruz, they have a lot more ancestral indigenous contribution and as I showed before, some of them have some African contribution. And that led to the first catalog of genetic variation in the Mexican population, which we made available just as the half map did, we just even actually even divided by every SNP, by allele frequencies by state, you see here, Guerrero, Guanajuato, Sonora, Veracruz at the bottom here. So we'll show minor allele frequencies and well allele frequencies for every SNP by the states analyzed. And this has helped to understand genetic variation which actually had been pretty useful when we talk about implementing pharmacogenomics and those other applications that had to do with a certain number of variants. Then the initial question was, what the half map cover for the Mexican population? And here we show how the percentage of common haplotypes share in the Mexican population. And we observe that the Gerugian cover for 64% of the Mexican haplotypes, whereas the Asian 74 and the Europeans 80. So if you want to capture 93% of the diversity in Mexico, you will need to add haplotypes for these three populations, whereas if you wanna capture 96%, you will have to put all the haplotypes together and you will only capture 96%. From these, we derive all the series of studies that help us say normalize our population correct for ancestry and some other studies so that would increase our power to significance to detect variants that influence common disease. So we ended up, of course, this project, the following administration when the Institute was created and it was my responsibility as a founder director to bring the results to the president and he announced that the wrap up of this project was announced and not only we published results, but there were comic books and other means to share with, again, with the underserved populations and those who gave a sample. So what we did once we announced, we tore again the whole country, bringing back the results to the communities so that people will learn, not the PNAS paper, but in addition to that, the different comics and different ways to express how important their participation in the project was. And so from that, we have followed with several projects, several projects that entail fine mapping of the Mexican populations, genomic analysis of ancestry in different ways, not only in Mexico, we have joined efforts with colleagues like Carlos Bustamante and others so that we've analyzed populations from Alaska to Patagonia altogether in trying to learn about migration and ancestry. And then we jumped into GWASs for H-related macular degeneration type two diabetes, cardiovascular and some other disorders, including cancer, infections diseases and others that had to do with pharmacogenomics and cancer. And so we started analyzing and trying to find if there were variants that influence common disease in Mexico as opposed to others discovered in other populations. And this is just an example from work from Eros Balán in my lab where he analyzed variants in the AGT gene and its influence in risk for hypertension. And so he found and he actually found some a haplotype that turned out to have two influential variants that composed a single haplotype that was pretty common in the indigenous and the ethnic population that would increase risk to hypertension. And later down the road he found that there were some of these risk variants turned out to be together in certain individuals. And so he found super alleles here, what we call super alleles at the bottom, for example, we see the AGT-3 that combines four high risk alleles and increases ORs to 3.3, really indicating that there were major components of the ancestral populations of Mexico influencing hypertension. In this case, he actually follow up on these haplotypes and turned out that he discovered that was an ancestral haplotype that came from the very indigenous populations in Mexico. So here we were moving from just describing what the population was to finding some initial polymorphisms related to common disease of interest in Mexicans. So we began to see that it actually made sense to jump in timely into the genomic serena and trying to move into common disease. So later down the road, there was the ambition to join into other major programs that would help again, help disparities in Mexico so that not only would the solving help disparities but would prevent from making those gaps wider, as I said before. So we joined in for a non-president synergy between our institute and the Broad Institute where Eric Lander and I had submitted a grant to the Carlos Slim Foundation. I'm sure you have heard of Carlos Slim. He has a lot of money and he was willing to support genomics. So we ended up submitting a grant that turned out to be funded. And so that was to sort of characterize the different cancers in Mexico and common disorders such as diabetes, type two diabetes and cardiovascular disease. So you know, these studies, what we're trying to do as I said is to find variants in our population that would be rare to other populations. And this is worked from these alliance between our institute and other NIHs in Mexico with the Broad Institute funded by Carlos Slim under the Sigma Initiative for type two diabetes. And this turns out that type two diabetes has a prevalence probably twice as you as non-Hispanic white. So it's very prevalent in Mexico. And so the approach was to analyze about 8,000 Mexicans and other Latin Americans, half or 3.8K with type two diabetes and the rest of non-diabetic controls with about 9.2 snips. And trying to identify 9.2 million snips and trying to identify those regions that would be a risk to Mexico, to the Mexican population. And it turns out that the studies showed or part of the results showed identify a region and this salute carrier that increased risk to diabetes in the Mexican population of about 20%. And this association interestingly, those who had these haplotype are younger and leaner individuals at risk for diabetes or with diabetes as opposed to other variants identified so far. And so this haplotype turned out to increase about 20% the risk for type two diabetes in the Mexican population. And what was interesting is that this is almost a private haplotype for the Mexican population. And that's pretty cool. I mean, if you see, if you're trying to do something for your population, analyzing your background and trying to hit into one of the major health problems in Mexico, let me show you how this haplotype that carries for amino acid substitution in this protein is common. So there is a reference sequence and there is the one with the four medicines and the sign limitation, the five SNP haplotype. And then there is less frequent this other intermediate haplotype when we ask in the 1000 genome project how frequent this haplotype was. It turned out here, we see that the reference is frequent in the African and the European and Asian and even in Mexico. But the rare haplotype that confers risk at zero in Africans, two in Europeans, 12 in Asians and almost one third of the Mexican population. So they reanalyzed the population, those individuals, those thousands of individuals that were part of the study. And what was found was that this is the ones participated on the sickness study. And given that we have ancestry markers for these individuals, we could separate of all the ones that participated and those individuals that were more of the Native American, Native Amerindian individuals. And you can see here that this becomes very common in the Mexican population and in the Amerindian population. So clearly the fact that we can approach those communities and then tackle some of the risk for type two diabetes becomes to make sense in terms of how genomics can help those that are under these underserved populations and starting for Mexico as a whole, but then having as a part of the question, the indigenous populations. So these are examples as to how genomic medicine is moving down in Mexico, at least on the research level. But later I was interested in my work at the OECD chairing the biotech area. Let me to learn how developed countries use science to create wealth. And this is for me at that time that was revealing how those countries use it for a cleaner environment for better mining and mineralization. Mining and mineral extraction for health, for better food, for better milk and how genomics was more and more becoming a key player into the bioeconomy. So we started defining a new strategy, say a second component or a new component of strategy that was what about using genomics for other means in Mexico. And so here is some of the, some of here is just represented some of the ideas of using genomics to tackle or to meet global challenges, not only in human health, but animal medicine, agriculture, food, aquaculture, environment and energy amongst others. And certainly it was time. And there were four or five elements that indicated it was time to migrate from just human health to innovation in different areas. And one of the elements you're familiar with because this is data from here, it's the cost of sequencing that has dropped so dramatically that it was possible to sequence thousands of cows or thousands of rice plants and so on. So that along with the fact that there were thousands of species that were becoming sequenced and their sequence was publicly available along the line. So those were two key elements. Then as genomic medicine proved the principle that we could identify genes for common disease. And again, this is data from you guys, how the human genome has been, how genes related to common disease have been common rare diseases have been identified throughout the genome. That was another indicator that studying genomes could be useful and results could be obtained in the form of associated polymorphisms to specific traits. Why not high economic value traits such as protein in the milk, such as better protein in rice, so submergence tolerance and so on. So that and then later, I'm sure you know that the Battelle report came with the economic result of analysis of the human genome project with the return of investment of $141 per dollar that later turned out to be $170 per dollar invested in the human genome. And boy, when you think about the importance of investing on time in science and innovation, well, it was clearly a call for attention in a sense to say we can translate those principles and use it in a way that could be helpful not only for health, but for other global challenges in the world. So clearly we clearly saw that there was this opportunity for genomics innovation connecting those ideas for people across sectors, not only those of us working in a biomedical lab, but from other sectors and they're into talk to them and they're into identify challenges. And then of course, having a way to get sustained investment in large science and technology innovation, again, sustained as a keyword that it's a keyword for the rest of the world, probably not so much in the US where investment in science and technology is pretty much sustained although directors have to struggle for the budget the early year. But this is pretty much a policy whereas in other countries, the sustained investment is key so that knowledge can be translated into products and services that can be useful for the economy, for a human well-being. And there are many examples that we've studied so far. One that called my attention recently in nation and then I learned that we do that in Mexico as well is the palm oil. The palm oil that is the major source of food, biomass, the climate change mitigation. This is a picture I took from a plane as I was landing into Malaysia for a Hugo OECD meeting on the innovation. Clearly, this is a major source of oil either both for edible oil and vegetable oil for other purposes. And clearly it's an important source of biofuels and there are ways. This is a plant that actually uses very well CO2 from the environment. So it really, it's a clean technology to produce biofuels. And so it turns out that this oil in Asia and in Latin America, its economy is growing as it is in the rest of the world. But turns out that this oil comes from the seed or the fruit that we see here. And this is the most efficient combination in the fruit so to produce most of the oil. And it turns out this proportions of the kernel both in the mesocarp is controlled by the shell gene. And by selecting through the shell gene, you can identify those two that are really no use for oil or fairly use for oil production. But having the Heruzaiga, the Tanera version actually increases significantly the amount of use produced. So clearly this model has been just the selection, the timeless selection using genomics in these plants can save up to six years. While people wait to see whether their plant turned out to be the Tanera or the other ones, the useful or the other ones. And so these programs are now being implemented in Southeast Asia and Mexico using genomics as it is for other industries. This is agriculture, but in terms of food, the poultry industry that is growing like crazy, this is the US, but it's growing like crazy and around the world. Well, now genomics has found its way into this industry since one single male birth in this what is called the breeding nucleus can have impact in millions of animals and millions of a lot of, you can see here, a lot of meat, if there's a mutation that or more mutations that you don't want or are not useful for this industry, that would pretty much kill an industry. If, say for example, disease gene gets into the picture at early stage, 21 years later when the chickens pop up, you get to see disease chicken that are produced by millions. So nowadays, genomics have identified polymorphisms that got to do with all these high value traits. And this has moved the industry so dramatically. So for example, if you see here how this has been has been useful for selection, poultry selection throughout the years, you can see what the breast was in the 80s as to what they can get now. This is not transgenic, this is just selection and you can see the percentage of yield, breast and fat that people can get from selecting these animals. And not only did this, but other traits that are of high economic interest, such as feed conversion, how much an animal has to eat to produce the right amount of food in the 42 days period of time they have to production. Clearly, those polymorphisms they've found to select animals with a better feed conversion turn out to be highly valuable to the industry that has that once again, meets or has to do with one of the major challenges around the world which is food security for around the world. So with this and other examples, different countries including this one has put out their national bio economy strategies and this is not only for the most developed countries but also others like South Africa, Korea, Malaysia, other countries around the world, Brazil included have put out the strategies as to how to use the biotechnology of bio, mostly when you go through these documents you see that they're mostly genomics. This is genomics all over the place in agriculture, food, health, environment and many energy and other areas. If you're not familiar with at least with the US document I will recommend reading this really easy to read document as to how governments are trying to use biotechnology mainly genomics for economic growth. And so clearly life sciences, the common denominator, bed genomics and healthcare is again a major denominator, a common denominator in these bio economies agenda. So clearly around the world, genomics is getting down to the bottom of the most important challenges, both for health and access to food and clean environments mainly. So with this in mind, we decided to go one step beyond and we established a new organization in Mexico to follow this path called genomics and bio economy. Actually it's called in Spanish genomics and bio economy which actually what it does is it has this, it stimulates this innovation virtual circle by putting together a business, government and academia focused on high value economic value or health value projects, programs. And actually, even though Mexico doesn't have bio economy blueprint, actually it has this in a practical sense. This is a major initiative that we launched a couple of years ago and we launched it in a meeting with the OECD and we see here the secretary general at the OECD, from the OECD in this middle genomics innovation and economic growth and we see Eric Green here collaborating and joining efforts with us and we're very grateful for that. The interaction with NHGRI throughout the years has been just tremendously useful and I'd like to close by sharing with you a couple of examples of what we're doing in terms of bringing genomics to bio economy. One is strategies to get more and better food by introducing genomic selection into the dairy industry in Mexico. So again, disparities, health, economic, social disparities we're trying to bring genomics into here into one of the major sources of protein for the Mexican population. So the way it works is pretty much the same we do here with diabetes or hypertension. It's just that it's a different mammal but it's just at the beginning it was a little daring to start analyzing different genomes from this species but clearly the point here is that they look alike they're very much look alike but after two years that you invest in feeding them growing them, cultivating them turns out some of them produced a lot of milk and some of them don't. Some of them actually produce very little milk. So how do you get to know which will produce what volume, what amount of protein, what amount of fat, how come industry can select these cows will be for the other industry more fat less volume. These cows will be for more volume less fat for the milk market and so on. Well, turns out the difference for all that is just haplotypes and in this industry they don't even care about the gene they just care about the haplotype. They just identify haplotype link to associate it with your trade of interest and boom, that's that. So in the US this is done routinely and what's happened throughout the years in terms of economy is that the average net merit that is the value in dollars of these herds have been increasing gradually as genomics has made a way into hosting and this is not an atomized population. This is a very pure population when there is no random making there is very clear protocol as to how to make them and reproduce them. So it's a pretty clean strategy. So the straightforward thing to do with our strategy since most of the Mexican hosting industry purchases their semen from US companies we thought must be same hosting so we can use same predictive equation than USDA and boom when we did it those positions were right there those predictions were not that accurate as they were in the US. So we went back to the lab and this is worked by Felipe Ruiz in Mexico that where he shows blue hosting from the US in Canada and red and green are hosting from Mexico. So even though they purchase their semen from the US something happens in the way namely not random making but some mating that provides some heat tolerance to the Mexican cows that actually make them shift from the initial group of the Canadian and the Mexican I'm sorry the Canadian in the US. So they're similar but not that similar they look identical but that in the inside they're not identical as we can see here. So clearly we needed to run some G-wases and some ways to retrain those SNPs that would identify haplotypes of interest and by doing that we'll refocus the target for prediction on these traits and so we chose to do volume, milk volume, protein, fat, longevity and structure. There are some traits that compose the structure of the cow that are important for production and so we once again went around the country with this time with the industry and this is one of the largest dairy industries in Mexico and we joined forces and went throughout the country this is where they're located in trying to identify what exactly their model of production was what having a catalog of all their hundreds and thousands of animals. So basically what we do with this we are genotyping right now about 6,000 selected individuals with cows with the specific traits. So just in the same way we do with humans for specific diseases and then with that we will be able to identify those that serve for they have a higher genetic value and of course that helps to assist the production and we will be able as they do in the US and Canada to predict as they do in US, Canada, Australia and pretty much all Europe to predict traits of high economic value and moving genomics into the bio economy and one last thing that we're doing and to facilitate access to underserved communities and have them engage with our health programs in Mexico is we're testing some of the pharmacogenetic tests available to all of the population. Initially those were analyzed in blood samples as we did with our HAP map and then in tubes of saliva samples but then we moved this technology in our laboratory to have those in filter paper, paper saliva samples so that we could reach almost any community across the country and so what we did, we did a filter use analyze and what are the most pressing health challenges in Mexico cardiovascular metabolic cancer and accidents are and then we ask, okay for which of them do we have a pharmacogenomic test that serves the drugs that are mostly used for those common disorders? Once we have them we ask two things, for which of them there is FDA label recommendation where the genetic test can be, pharmacogenomic test can be used and for which of them are their international therapeutic guidelines so that we can really maintain our efforts within the range of tests that have been proven in other populations so we ended up with the dozen drugs that are used in Mexico and translated that as I said in a small kid is pretty much a zip lock bag with the filter paper that we are using throughout the different communities in the country to survey what the different polymorphisms are and whether there is need for pharmacogenomics and I was mentioned earlier today that in Mexico there is no policy for pharmacogenomics and so what we're trying to create here is the evidence that there need to be pharmacogenomics policy in Mexico and one of the part of the evidence that we're creating and we were asked by policy makers was is there really evidence that those polymorphisms exist in Mexico, is there really need for pharmacogenomics in Mexico and this is just a sample of the population across the country that we did and the percentage column indicates the patients that require those adjustment based on these tests. So clearly this is pretty much as common as in every nation particularly by the warfaring result where the majority of the Mexicans test that require those adjustment. I was telling Eric earlier today that included myself, my wife, my kids, almost a lot of people around us, 71% require a tremendous significant adjustment in warfaring but for the rest of them, hey, there is market, there is need for this kind of testing and we're providing this evidence for lawmakers for our FDA equivalent to produce this kind of policy that we need and in my enclosing I like to tell you that we're moving this even forward. We decided to select a series of genes, polymorphisms for obesity diabetes and dyslipidemias, many of them that have been discovered in the Mexican populations and others that have been replicated in Caucasians and other populations and sort of have a survey in different areas of Mexico, Mexico as a country, not Mexico City where there are different prevalences of the disease. So there is a question, do these mutations accumulate in certain areas where there is more of the disease? We know certain, what is the component of the environment? So for example, there are communities where there is no obesity nor diabetes, then these individuals migrate to the US and after a couple of years when they come back they're obese and diabetic. What happened? What happened in terms of the environment? What's the genetic makeup in terms of risk alleles in that population? And like that, there are other interesting phenomena that we're assaying through these experiments. So there's no question what we do in the laboratory, in the institute and what we do in terms of research that it's important to ameliorate health and economic disparities, whether it's genomics research, technology convergence, integrating these into electronic medical records, cost-effective analysis, education and all these other efforts that we do. But in order to move them through the bio economy, so my reflection is that it is key that we move that knowledge into getting producing new products, new services, new ways to meet the most pressing challenges that a society has. And particularly as I said before those communities, those underserved communities within a society. So that's why it's so important to include them from the beginning. But in order for this to become a reality, well, number one, we need serious research and funding and all the things that I mentioned before. But in my mind, it is clear that doing all these, so in order that we can integrate them into one effective platform, we clearly need sound forward-looking policies. Without these kind of policies, it is very difficult to make a difference than to help genomics ameliorate health and economic disparities. And as you know, I'm talking, I'm referring about policies that is needed depending on the country, depending on the community in different areas, including here are the ones that I list, education, access, reimbursement, funding mechanisms in science, knowledge share, intellectual property, commercialization, public engagement and several others where we in my mind have the responsibility or the opportunity to educate our policy makers so that we can have timely and forward-looking time policies to make genomics an instrument, as I said, to ameliorate health and economic disparities. I'll stop here and I'll be happy to answer any question. Thank you. Hi, so it's a very nice talk and I think my question is related to the bioeconomics. So how can you envision that if you're skewing, for example, a crop or a specific animal strain to make better food production, that you may have the risk of making them susceptible to a future infection or a plague? So at that point, is it important to have variability or how are the rules that we'll actually cover for the skewing as of a specific crop for bioeconomics? I don't know. That's a very good point. So we've learned that diversity is key and I would completely agree with that. What I'm talking about here is selecting for a specific trade as opposed to making identical animals, namely transgenic animals or plants, for example. Not that I'm against, but that's not the point of discussion. The point here is just as the Asian population, Asian countries have selected for submergence, tolerant rise, is just selecting for the top one gene and their seeds with the rest of it being as diverse as it is in nature. Same applies for the cattle experiment here, both in the U.S. and Canada and Mexico, is just selecting certain traits, I'm sorry, certain habitats that provides higher value in terms of prediction, but the rest stays the same. And this is just to say I absolutely agree with you on the point of the diversity or the risks that convey not having diversity in a population. One question I had is in thinking about, especially economic stimulation catalyzed by genomics, compare and contrast what's happening in Mexico with other parts of Latin America and South America? Yeah, so what happens in Latin America is the most important genomics core, are in Brazil and Mexico. When you see the level of investment, Brazil has three times more investment in genomics innovation as opposed to Mexico. One of the reasons I tend to believe is that they're very focused in an economic, high economic value of projects that pushes them to increase and they don't have the U.S. nexor that would help with technology and other means, whereas in Mexico, number one, we have a lot of interaction and number two, due to other social phenomena that don't help innovation. I think that people are more cautious as to how much they invest in bringing genomics to bio-economy, but this is growing and growing, and we saw the same pattern with South Africa, with Korea going little by little and then taking off. So while Brazil is investing more, is it distributed across similar areas or just take healthcare in genomic medicine specifically? Is it apportionable? No, healthcare, Brazil will be focused in cancer period, and that will be very located. But genomics in agriculture, that is across the country, and they're very focused in agriculture, whereas in Mexico, it will be medicine and very little in other areas, now that we're beginning to explore other areas. And you emphasize pharmacogenomics, but you must be also doing this in cancer as well. Right, absolutely, yeah. And what I found with our, what I described with our private variants for diabetes in other areas, there are some mutations found in Mexican tumors that are more amenable to certain treatment. Pearl. Yeah, great talk. I just, it's really great that you're linking genomics to the economy, but I'm wondering, is there evidence or are you beginning to see evidence that this improvement is not just a way to increase the profit of the big companies, is it really trickling down to the people in terms of reduced cost of food? So increasing profit from international companies across the world, that's a rule across the world, whether it's genomics or not, right? But what we've learned is that if we have society, government, academia, business all together, trying to funnel this knowledge into benefits for the general population, that can be done as well. And that is not, that can be running parallel with the other big companies that look for their, increasing their profits. Now, what I've learned is that when we show the kind of market that they can access by having huge volumes with low cost tests or otherwise, that becomes interesting. And what they need, I mean, that becomes attractive to them so that they be part of these efforts. So they are able to separate their big business efforts with their innovation, global, general society or even ethnic groups and less serve a population. So you can work both of them, at least in Mexico, we have worked both channels at the same time and it does work. It's not to be against profit. That's big business thing and everybody, it's really the fact that you tied your talk to her disparity, to disparities. And therefore, if we are going to go through this process and we're going to impact on equal access to food and health and all of that, then there has to be a policy structure that takes advantage of this improvement. Otherwise, it stays at the level of just production. So that's when I see the benefit of hardening, government, academia and business under a non-for-profit like our organization, under a non-for-profit organization that can make sure that these benefits are funneled to the population. We're running four or three pilots at this point. I wouldn't be surprised, and I'm not surprised that we will have to fight at some point with other interests in this equation. But at least in the four or three pilots that we're running, this is happening and they're being part of it. Last question here. Thanks for your talk. Are there any projects that involve next generation sequencing in Mexico? Any big population type studies? So the answer is there are good ideas, good initiatives. When you, I recently surveyed the number of next gen equipments that are at our NIH campus and they're way too many as opposed to the number of projects that were there's funding to run those experiments. So people focus on getting their own machine, which is part of our human scientific nature at some point or developing country culture. But then they're not able to get any funding for their next gen, particularly nowadays when this is becoming a commodity and you can send your samples to Korea or anywhere else to the next gen. But the answer is there are several projects, few of them are running, tons of machines in Mexico, although Illumina reports that they sell three times as much equipments and reagents in Brazil as opposed to Mexico. But most of the next gen machines that I know, which are many, not really producing the data that they should be. And that's because people think of the machine, not the reagents and the support of the project. Okay, please join me in thanking Horado for a terrific talk. Thank you. Thank you, Eric, thanks so much. Muchas gracias. Muchas gracias. Muchas gracias, muchas gracias por venir.