 Well, thank you very much for accommodating me, I apologize I couldn't be with you in person. So I wanted to reflect a little bit on the key questions and challenges that as an outsider I see this program facing, why don't we start with the second slide on key driving questions. So as I think about this going forward, you know, I think we need to ask a very fundamental question like, you know, why study the diverse populations in the clinical genomics setting, you know, what are the potential scientific gains that we hope to realize here. And as a way of setting this up, I thought about what is it that we've learned from, you know, broadening representation in GWAS and Mendelian disorders, and a little bit about what we're seeing as part of ClinGen, and wanted to speak a little bit about that. In particular, if we are going to go down the route of having, you know, multi-ethnic studies, how do we design those so that they have maximum power for discovery and interpretation? And you know, the key question to sort of set up is, do we want proportional representation, you know, should it be, you know, roughly the proportional to the population representation, or do we want stratified sampling, or how do we think about that problem? And then, you know, really as an outsider, and this is something I'm obviously not an expert on, but one that, you know, it seems to me is important, is how do you think about modifying existing protocols, and what you're already doing in recruitment, consent, enrollment, return of results in a multi- and trans-ethnic clinical genomic setting, you know, are there differences that we need to take into account, and how do we set up research in order to make that happen, you know, do individuals with different ethnic backgrounds interpret genetic results differently, and if so, you know, how do we then modify and think about all those ELSI issues? Next slide. So, you know, some of you know we've been interested in this problem for a while, and in the context of NHGRI, probably some of the best data out there are the results of the Thousand Genomes Project, and, you know, what we described in the first phase has now been seen in phases two and three, namely that common genetic variants are actually pretty rare, even though they tend to be shared across populations, so if we think about a common genetic variant that's at about 15% frequency, well, they're nearly exchangeable across European populations, and so you don't really need to think too hard about population stratification because it all ends up working out, and furthermore, what you would learn in a European population is likely to translate into other ethnic groups. However, common genetic variants are very small sliver of all genetic variants, and those rare variants that are now much of our focus and what you end up finding in both sequencing studies, particularly clinical genetic sequencing studies, are pretty rare, and of course, population five it, and now how you design the study makes a huge impact on how you are able to interpret the results, and one of the key issues, and Jim talked about this in Heidi and others, is that the VUS rate is different across different ethnic groups, and so how do we begin to challenge that and think about that problem? So in our work, what we've done is try to pilot broadening representation across global populations, and so we've got active projects all over the place, and try to use patterns of genetic variation to try to draw inferences about what the world could look like if you were to scale up in different ways, and I just wanted to give you one quick example of this in the next slide, which is the back of your slide in HLA, and this is an association that's been known for years and years and years, it's incredibly important, it's on the pharmacogenomics VIP list, and it's actually a pretty common variant, and of course, as we all know, you need to test for it if you're going to put a patient on a back of ear, because if the patient carries a particular HLA haplotype, then they're going to have a really nasty reaction, and if you keep giving that patient a back of ear, then you're going to put their life at risk, okay? And what's interesting from a population genetic perspective about this variant is that its global distribution is anywhere from extraordinarily frequent, so if you happen to be implementing this in India, then you really need to worry about this. This is a 20% frequency, and you should be genotyping everybody all the time, whereas if you are in Japan and oddly enough in West Africa, then it's at very low frequencies. And the point I want to make with this is that there is no real population genetic model out there that would tell you, yep, the Gujarati and the Messiah are going to cluster, and then it's going to be Utah, and then you don't have to worry about your Rubens, right? It's all basically empirical, and so you really need to go out there, characterize these variants, and genotype them in a broad array of populations with good clinical outcomes data so that you can really assess what's important, what's not important, and what populations. And that's really kind of the nuts and bolts of it, right? As you go forward and try to take this project to the next level, you need to figure out where to invest, you know, and how to make those key investments so that the results are interpretable. You don't want underpowered studies, and so, you know, enrolling 2% of the population to be, say, you know, ethnic Hawaiians may give you 10 ethnic Hawaiians, but, okay, you know, is that going to be useful or not, or so how do you think about that? Next slide. So what we are seeing in broadening GWAS results is that it works, right? You know, if you go out and you properly power GWAS in a multi-ethnic study or you go out and you study other populations, you, number one, find new variants at existing genes that are important, and, interestingly, you're also led to new genes that you wouldn't have found by just focusing on, say, Europeans or Asians, and there's some very nice examples out there. I've just, you know, pulled together some here. There's the work by the Broad, for example, in Mexican populations in SLC-16A11, which is a very common risk factor for type 2 diabetes, and largely absent from non-American populations, right? So this is an allele that arose on a Native American background. Interestingly enough, it actually comes from Neanderthals, but, you know, to make a long story short, you basically only see it in populations of Native American ancestry and low frequencies elsewhere. And that's a totally new diabetes gene. From our work, we've looked at skin and hair pigmentation, and we found that in different parts of the world, skin and hair pigmentation actually may be under different genetic influences. And so the same may be true for lots of other traits that you'd like to study. And so, you know, I think the NHGRI investment here has been quite successful in broadening representation, and I just want to give out a special shout-out to Paige, next slide, where the next round of Paige, we've been, you know, privileged to be part of this design of the new multi-ethnic genotyping array, where we've put together, you know, variants that are clinically relevant, we've created a GWOT scaffold that will, you know, be properly powered in lots of different populations by giving a lot of thought as to how we do that, and have kind of set the stage to make this happen, and we've got the first bolus of data in, and are beginning to see really cool results. So, if we turn to slide A, you know, I would say that the current state of the field is that, you know, 1,000 genomes population-based sequencing has taught us that common variants are rare and shared, and that rare variants are common and largely population-private, right? So, we've kind of solved that population-genetic question. Secondly, that properly powered GWOT studies and understudied populations are leading to novel variants of previously associated genes, and new genes underlying previously studied phenotypes. So, this will work, and it'll help, and it definitely makes sense if, you know, what we're seeing in common diseases and now rare diseases is going to translate into a real kind of clinical genomic setting. I would say that the other piece of data that for us has been very interesting is we've been working with the California state testing program for CFTR and have now about 4,000 CFTR variants that are the variants that we see here in California, and what you see is that, you know, yes, you know, we still have, of course, our Delta 508 in European populations, but when you look at, for example, you know, Hispanics that don't have a lot of European ancestries, they have a whole different set of variants that are actually also really important, and the only way that we can break that BUS curse is to actually bring in the clinical data, and so it's, in fact, because through the Stanford CF clinic, we've gotten access to several hundred patients that have been carefully followed that can actually go in and make good clinical determination for those variants. Now, you know, in many ways it's a very modest step, you know, we've taken a well-studied gene, a well-studied system, and have begun to break down the BUS rate and help ameliorate some of these issues, but it's going to be a kind of boots-on-the-ground effort. You know, I'm afraid it really is likely to be a slog to make this happen, and so, you know, to properly think about the program, we're going to have to make some tough decisions about where to invest. So, you know, I'm, of course, very supportive of building diversity and disease, and it's likely to yield new opportunities to learn new biology and improve patient care, but we've got to really think hard about how to do it. You know, just kind of going out and getting the samples you can, the samples of convenience, may not be the best way to go, and so you may want to pilot, for example, investing in the next Caesar site to be a minority Caesar site, because then you'll have one properly powered site, and if you think about the ESP project, you know, they did a very nice job of having equal number of whites and African Americans, right, because we wanted a properly powered study, and I'm sure Debbie can speak to that. So, my last slide is just some thoughts as to how to frame the discussion, and what I see is the key challenges. I think the first one is do we want representation to be kind of proportional, so 65% whites, 15% African Americans, 10% Hispanics, whatever, or do we want stratified, right, and how do we think about that, because those are tough decisions to make, and you know, because you're in a clinical context, you may not even be able to get stratified sampling if that's what you want, so you have to think hard about the next set of sites. I'd say the second big challenge is how are we going to break the higher VUS rate in non-white populations? I think that's actually a really important question, and one that's going to require some deep thoughts, you know, and some pilots to make that happen. The third point is that, of course, inclusion doesn't just mean ethnic diversity, right, and we can imagine that socioeconomic status, education, are all going to impact enrollment, maybe genome interpretation, and certainly return of results, right, so if you're dealing with a topic as complex as genomic medicine, and you're dealing with vulnerable populations, individuals that may have variable insurance coverage, and we know what they're going to do with this information, you know, it's a very deep LC question, and something I'm no expert on, but even a naive population geneticist like me can see that this is going to be important. And then the last point is that, and we had a meeting about this two weeks ago, that new technologies pose a risk to broadening health disparities, and so while genomic medicine could improve, and we hope it's going to improve health outcomes across all populations, the rate of improvement could really be different in different ethnic groups, by ethnicity, by race, post-economic status, and so, you know, are we going to accept that? How do we study that? I actually think that this is an area that a bit of LC funding could go towards, and really think about how to develop the countermeasures to exactly that issue, and so, without, I just want to thank you all for allowing me to give my thoughts, and as always, it's a privilege to participate, and happy to take any questions you might have and be part of the discussion going forward. Katrina Armstrong from MGH. I guess one of the things I've interested in the panelists are Carlos on the phone. One of the things that I struggle with in this area is we spend a lot of time working on community health and health disparities, and you know, we spend decades really working with the community on what are largely now seen as the critical determinants, which are the social determinants in our communities that play a huge role in health disparities, and so, one of the challenges, Jim, I guess- I unfortunately can't hear the question, so I'm going to need someone to just repeat the question for me. Is that the question for me? Well, maybe I'll defer to the management on that one. So, Jim, maybe, I guess as you think about, you know, the, let's say, the challenges of participation, I think one of the challenges is we've worked, let's say, in precision medicine and communities, is that on their list of problems to solve, precision medicine is not high on that list, let's just say. And so, how do we also, I would argue, maybe bear responsibility for figuring out how precision medicine is actually useful to some of our most disadvantaged communities as we ask them to participate in that, and how did you deal with that, and do you have thoughts about how to do that? Yeah, I think that's, you're dead on. You spend some time in a general medicine clinic, and you realize that people, opportunity costs matter, and they're real. And everything that somebody does precludes something else, and when you've got a job that doesn't pay a lot, and you've got a, you know, social situation that's fragile, things like precision medicine don't mean nearly as much as approaches to, you know, getting people to quit smoking, and eat right, and et cetera. So I think that's a challenge for our field, and I think that we do probably, we are probably guilty of excessive boosterism when it comes to the value of precision medicine, when we know that we could get rid of a lot of diseases that are out there through techniques that are, you know, if they could just be applied, it would be effective. I think that the answer to that is that if the gains aren't gonna be as great as some of us would like in precision medicine, we have to try to make sure that what gains we do get are equitably distributed, right? So that's, so I actually think that pessimism, or I would say realism, towards the potential for precision medicine is an even stronger mandate to make sure that we include everyone, because if we don't, the smaller than expected gains will be absolutely minuscule in those populations. You know, would you say that, you know, maybe some areas like, you know, pharmacogenomics are ones where there is a pretty direct benefit, and so, you know, we wanna make sure that, you know, at least in that context, we're giving people the right medicine if we know that they've got a genetic predisposition that would not be, you know, not a lot of the good responders, right? I mean, is that a particular area that makes the focus on, or are there other areas? We, I guess, Carlos, we could quibble about that all day. I'm not sure pharmacogenomics has really shown the promise that many hoped it would. I think that it is an example where specific populations can be dramatically affected and others not. So the best example of that would be, you know, some of the HLA alleles associated with Stevens-Johnson syndrome being vastly overrepresented in Asians. So it might be an opportunity, but I'm not sure I'd wanna put all my chips into pharmacogenomics. I don't disagree with the point because I do think that social determinants are really important, and in my experience, I haven't seen any of the communities with whom I work say I really want more genetics. But I think that, but I think what excites me about precision medicine as it's being defined now is that it's beyond the scope of genomics. It includes data on social determinants and understanding the ways in which genomic or biological factors interact with social determinants. And one thing that strikes me just from the previous session is that even with a well-defined conceptual model of psychosocial and behavioral outcomes, I would just make an observation that there perhaps is not sufficient attention to social determinants and ways to link and integrate that data with the sequencing enterprise and activities. Carlos, can you hear Debbie? I can't hear the question. Will I be like, Debbie Neves is trying that. Hello. Hey, Carlos, I know you can hear me now. This is Debbie here. I don't disagree that we need lots of ways to bring in populations into medicine in different ways, and in precision medicine in particular. But I also think we need to increase the diversity of our workforce. We need to work really hard at this. Harder than we've ever worked in our whole lives to do this. And I think Cesar, with all of the many clinicians that are in this room and will be here tomorrow, could really impact and change this. And I think bringing some of the populations in as new groups in the future is so key. I mean, if we don't have the information, we can't provide new insights. You could have a lot more of VUSs like Carlos is talking about, but they'll never move from being VUSs to anything else if they just stay there and are not linked to phenotype. So I would urge us, more than urge us, it's past time that we did some large-scale studies in the many populations that are representing the United States that have not been presented in genomic research at really significant levels. You know, I would agree. I mean, I would say that in talking to my CF colleagues at Stanford, I've totally been one over to that point that CF manifests as a somewhat different disease in Hispanic Latinos, and that could partly be because of the different variants that are segregating in that population, but it could also be due to all kinds of socioeconomic factors, access to care factors, getting identified early on. And so if you really wanna put this into practice in perhaps some of the communities are most effective, then it's gonna happen by bringing in and linking the clinical data to the genetic data. And I wholeheartedly agree, I think we're in violent agreement, Debbie.