 Hello, everyone. Can you hear me? I told in the past that I have trouble speaking into Mike, so just wave if you're having trouble hearing me. So I'm Charlene Schramm. I am a program officer with the National Heart Lung Blood Institute. And I appreciate the invitation to come today and talk to you about the top med program, which stands for the trans-OMIC for precision medicine program. So I've said that I'm a program officer, probably the only people in the room who understand what that meant are those of you who are also program officers at the NIH. But what that means is that I serve a role within the top med similar to Collin's team at NHGRI for the COMP program. And I'm hopeful that a colleague of mine, Rebecca Beer, has joined us. She, oh, here she is in front. Great. She would be the orthologue to Collin for the top med program. So I'm going to start with the take-home message. And that is top med program really wants to partner with IMPC COMP. So to get to there, how do I forward? There we go. I want to just go over a few of the top meds history, its origins and goals, talk a little bit about how it was launched and how it's being implemented, a very brief update on the scientific progress, and a couple of references here and there on plans for the future. This is amorphous enough that I don't even have its own dedicated slide. So the top med program grew out of a workshop that was convened by the NHLBI in 2014 where they generated a number of recommendations, first and foremost, to establish a whole genome sequencing program to generate a diverse resource with diversity of ethnic racial populations to invest in additional omic measures and to support studies of disease-associated genetic variants and their functions and to establish a scientific data commons with the web portal. So the last one I'm not going to talk about very much because NIH has, in the intervening time, stepped up to start work on NIH data commons. So I'm hopeful that in future meetings of this group you'll get updates from that group. I'm also not going to talk a lot about the supporting studies, functional analysis of variants. That is something that, again, we're very much interested in a partnership with your group in order to help us move forward in that direction. I will talk to you about how we have implemented the first three recommendations. So the TopMed program consists of a number of components and a number of roles of those components. There's a group of cores that provide data generation. That data goes to Informatics Research Center and Data Coordinating Center for the Harmonization and QC. The data then is shared within the organization through a cloud platform. And there are a number of investigator groups that draw on that to develop new tools, conduct analyses, and then that data can go back into the cloud. There also is working groups such as our ethics, legal, and socioeconomic indicators group. What's not on this slide but is also a huge part of TopMed is a huge number of other working groups that are either disease or organ focused. So if you want information high level on the TopMed program, here's our URL to NHLBI's website. My apologies for not quite getting the animation correct on this slide. But to get more detail, I would refer you to the data coordinating site, NHLBIWGS.org. And here what you can't see on the slide, what's indicated by the oval, is a tab that's available to the public of the list of projects and studies that are part of the TopMed program. There also is a tab that provides information on the data that's available and also data standards that have been developed by the program. So these data standards include sample randomization, whole genome sequencing data formatting, processing, there's RNA seek pipeline, and there's the URL for the data standards. So there are a huge number of cohorts currently participating in TopMed. So the question is how did NHLBI select which cohorts to include? We used an X01 funding opportunity announcement. So an X01, for those of you who are not familiar with it, and that's a lot of people because it's an unusual mechanism, is one that does not provide any money whatsoever. But it does provide access to data production cores that are supported by the NHLBI. So these data production cores include, for example, the whole genome sequencing. The X01 applications are received and peer reviewed, and then the NHLBI selected cohorts to put forward based on their scientific merit, that is the impact score from peer review, and also whether a particular cohort fills a gap in a disease area, ethnicity, or a type of omics proposed for data generation. The selected cohorts are then assigned to one of the omics production cores. They do what they do, and then the data gets returned to the IRC and DCC, the Informatics Research Center and the Data Coordinating Center, which then provide the data back to the investigators to work together on analysis of the data, and then after the standard embargo, the data does become available through the dbGaP request process. And one goal we have is to make that access to data easier. So hopefully in the near future, we'll be able to tell you more about shortcuts to getting to the data. So as I said, there's a large number of projects that are already participating in top meds, so we have about 40. Amongst them, there's 65 different studies. This comes out to over 1,000 investigators, and some of the ones that I would point out to you here, there's here at the top asthma in African descent populations. We also have a couple of the major NHLBI cohorts, the Framington Heart Study and the Jackson Heart Study. We also have over here sickle cell disease. So even though the main focus of this program is common diseases, we also are including rare diseases. So what are the diseases that are being included? And what is the racial-ethnic breakdown? So top med is organized into phases, and each phase is essentially a year or one solicitation of XO1 and one selection round of XO1 cohorts. For the first three phases, recall that we didn't get started until after 2014, so we have just made the decisions on our fifth phase, but we have the data generation only through the phase three. So what we do have through phase three is for whole genomes, over 120,000 genomes sequenced, actually more than that, have passed the QC. Of the breakdown, the largest amount is European ancestry, but also a good chunk of African ancestry and Hispanic populations. The breakdown by disease area is shown on the pie chart on the right. As you can see, asthma is a major disease focus. There also is a large chunk of studies that are kind of thrown together because they are multi-phenotype cohorts. That is, they were chosen because they had significance to the heart, lung, blood mission, but they actually are collecting phenotype data much more broadly, so we had trouble putting them in a category. Their disease areas are chronic obstructive pulmonary disease, hypertension, stroke, coronary artery disease, and as I have already mentioned, sickle cell disease. The racial-ethnic distribution varies by study and by disease group, so here are some examples standardized against percentage per study. Some of these studies are much larger than the others, but what we can see is that this is the kind of data that we're looking at to assure that we have representation across the cohorts. The abbreviations at the bottom, the hypertension, blood pressures, the first, lipids, venous, thrombotic embolism, AFib, early onset, myocardial infarction, coronary obstructive pulmonary disease, asthma, sickle cell disease, and hemophilia are disease areas where we have very active investigations. So I said that I'm not going to talk a lot about our future plans, but I can talk about what we're trying to work on right now. We are trying to continue to build the components within top med. So we have, as I've said, been building cohorts using that X01 mechanism. It's still operational, but right now we have a call out specifically for soliciting X01 applications focused on blood disorders since that is one that's been identified by NHLBI, particularly sickle cell disease, as a major focus in our strategic vision, and therefore is one that is also of great interest to the top med. Right below that, in this group of components, we see the centralized OMIX resource, the acronym COR, which is appropriate, because these are the service CORs. This is where the data is being generated. It includes whole genome sequencing, as well as a variety of other OMIX services. There have been funding opportunity announcements, or RFA is released, that have now been awarded and are ramping up, and hopefully will be able to give you progress reports on them in the future. One is for what we call cooperative agreements for developing methods and tools based on transomic data, and then also a more recent R01 RFA specifically to integrate the transomic data using more than one at one time. We have a number of collaborations within NIH that we are setting up with partners. One major partnership is with the NIH Data Commons, so this is an effort led by NIH's Common Fund, but has the goal of developing a one-stop platform where investigators can go to get data. They're starting with a pilot project, and we're very thrilled that TopMed was selected as one of the entities to serve in that pilot. There also are model organism databases within that pilot. We also partner with CMG's sister consortium, the Centers for Common Diseases Genomics. And most important, we wish to encourage TopMed data to be used by the scientific community by encouraging investigators to sign up to access the data and do preliminary analysis and submit investigator-initiated grant applications. We also encourage TopMed data to be used for applications for career development awards. So this is a multi-pronged strategy, hoping to build and enhance the TopMed resource and to build on the diversity while adding new non-traditional cohorts. So I've been talking about transomic, but I never really defined that. So the data I've talked to up to now has been mostly the whole genome. That's certainly the richest level of data that we have right now. There has been a number of working groups in the disease areas that have got together, worked on harmonizing across cohorts, doing cross-study genomics association analysis, and they've identified not just millions of variants, but hundreds of millions of variants, which comes as no surprise to those of you who are working on other large genome programs. So how is it that we are going to get to figuring out which of those variants need to be followed up for mechanistic studies, confirmation through functional assays, development through clinical validation, and then use for translational medicine? So the approach for the top-end program is to use a transomic approach. So in addition to data production centers that do whole genome sequencing, we have resources to provide transcriptomics, methylomics, metabolomics, and metagenome sequencing. The idea, or the hope, the aspiration at this point is that the combination of these in combination with the new analysis techniques being developed through the program and other programs will be able to start from that hundreds of millions of variants and filter that down to tens of variants that are the top candidates that can then be followed up with functional analyses. So what has the TopMed program actually accomplished in addition to all this wonderful data generation? So we are well behind the CMGs as far as the total number of publications. I very much envy your list of hundreds of publications. This slide is out of date. It's more than five publications. It's more like 10, so it's still well below the 400-plus of the CMGs. But of these publications, most notably, there's one that describes the analysis commons, which is the infrastructure within TopMed that the investigators use to do their cross-studies analyses. There's also a number of papers that are identifying candidate variants. Some of them have also done some level of functional assays, particularly leveraging off of NHLBI has a RFA program that's in its final year for doing functional assays. And recall that I had said that the broad phenotypes include more than just those of interest to the National Heart Lung Blood Institute. One of these papers used the Framingham Heart Study data on brain MRIs to find loci associated with brain volume differences. There are also a number of other submissions that are accessible through the preprint server bioarchive. If you do give you a warning, if you do a search of either PubMed or bioarchive for TopMed, you will come up with a huge number of hits of papers that mention it, but may not have been using the data for the analysis in that paper. So that's why you'll see more hits than I've got listed on this page. But I'm hopeful that very soon I'll have a much more intriguing number to give to you on studies that have generated potential candidates for functional follow-up. There are over 200 papers proposed by TopMed working groups that are either in the concept, analysis, or preparation phases. And TopMed, as I said, is also working with the NIH data commons to improve the accessibility of not just the data, but the outcome coming out of these working groups. So I had confessed that I wasn't going to actually have a slide on future plans because it's a bit of hand-waving, so here goes the hand-waving part. So the reason I'm the one up on this stage is that it's my interest within NHLBI's program office to advance the application of functional assays to the discoveries that are coming out of this program. NHLBI is staff are talking amongst ourselves and getting input from our advisory panels on ways to do so. I certainly would love to hear any thoughts you all have during the break or later today. One thing we do participate in, we do already provide, it's about a million dollars per year to the COMP program from NHLBI. So we already show an interest in use of the mouse models, and we very much want to continue that in some form. Don't have a clock here, but I suspect I'm running over. I would like to just encourage anybody to contact me or my colleague, Rebecca, if you have any questions about the TopMed program, we'll be here, I'll be here all day and Rebecca for at least part of the day. And here's our email addresses. Thank you very much. Yeah, so one of our big discussion points yesterday was data integration with an hour review to these other large data sets and getting a foothold in the commons is a real objective for us. So I hope Charlene can be the contact person and work together on that. So now our most recent new partner, tell us about kids first.