 All right, I think I'll just move on into the concept clearance, and I'm going to start with a little bit of introduction and just lead into the common disease variant discovery discussion. I'm going to begin again with the workshop wish list and note that today, even though clinical applications of sequencing was discussed as a central topic, I'm not going to discuss that today. It has its own program, it has its own planning process, but obviously those programs, those groups of programs, are going to consider the discussions at the workshop. The same with the big need to enable capture interpretability and analysis of the world sequencing data and a bit about creating this virtuous cycle. Those are items that need their own discussion, and we just heard before Phil Bourne talk about some things that bear significantly on this one. Similarly, we're not today going to talk about or talk very much about doing genome function especially related to interpretation of variants. There's a significant program that we have at NHGRI on those areas, and they have their own planning process, and again they heard the advice and discussions at the workshop. But I will talk a little bit about that. So that leaves us with these four areas representing up to six concepts. And I can talk about these a little bit, and you'll see in my write-up I previewed these, but these three areas, comparative and evolutionary genomics and the gold genomes, we're going to leave to a future opportunity to discuss because it's already a lot to just discuss these three concepts that comprise this area. So the concepts are common disease variant discovery, centers for Mendelian genomics, and a genome sequencing program coordinating center. And again we'll leave these topics four and five for another day. Just to orient, the total proposed here for items one through three is $71.5 million in 2016. The 2015 estimated cost of the current large sequencing programs at NHGRI is $85 million. This includes some co-funding. This doesn't yet include any co-funding. The timeline, we are here at council. So even though a lot's been done, just sort of at the beginning of the timeline right in the middle of it, planning for RFA release around the new year, receipt in spring, review in summer, and then a year from now these would be brought back before council for funding plans. So common disease variant discovery. So what's the point? What's the point of this? To establish a collaborative large-scale genome sequencing effort to identify genome variants contributing to multiple common complex disease phenotypes. To explore comprehensively, and I want to flag that word comprehensively, a range of disease types and architectures to learn general principles of biology of disease and lessons about how to approach these studies. To undertake and compare a range of study designs. This would entail doing multiple disease studies. They would be very large. At least one of them should be whole genome sequencing study to push the field, including pushing costs, methods, analyses, all in aid of understanding non-coding variation. And finally, to develop foundational deliverables such as data resources for disease research communities, know-how for similar studies, technology innovation, data handling, standards, policies, and possibly common controls. So why do this? Well, common disease affects a lot of people and understanding the genomic variants and this influencing risk or protection from these will provide insight into the basis for important individual diseases and diagnosis implications for treatment and even identify targets for therapy. This also would provide general insight into the biology of disease and relationship between genotype and phenotype. It obviously requires a lot of genome sequencing, especially to discover rare variants systematically. Studies need to be comprehensive. They need to be well-powered. Large sample numbers and numbers that were discussed for some of these in the workshop were up to 50,000 or more. And again, better or few comprehensive than many partial. We need scale to compare across multiple diseases to learn to compare across multiple designs. And finally, a well-chosen set of comprehensive studies and data sets have high potential to provide a resource that will be catalytic and both for individual disease communities, so large sets of variants and association information, but also for developing tools for interpreting non-coding function, et cetera. So one scientific consideration that I think requires some considerations, how many studies will be enough to do this, to explore a range of disease types, architectures, and allow examination of a range of design? And here we propose, again, we propose 6 to 10 over four years, and that's as a minimum. And another scientific consideration is what is comprehensive? This is something that we can only define partly in advance. You can do it with reference to power. You can do it by saying that you'll keep going until your discovery curve falls off. There are always qualifiers about the population studied, and there are practical limits. So just for practical reasons and scientific reasons, the program will need to have a starting point and then iterate on it and refine it as a program goal. In addition to the primary objectives of this program, there are some desirable features that we would like to include. That is to allow some production of non-genome sequence data. For example, epigenomic data or transcriptomes, but that has to be coordinated with the function program. We would like the flexibility to do projects that are not directly related, large sequencing projects that are not directly related to a specific disease. There are ideas about that that have been floating around, and this could be an opportunity if it came up. And finally, opportunities for outreach or liaison with other investigators and programs like a hub and spokes model, which I've seen you successfully in other programs, for collaboration with outside investigators. For example, on pilot level efforts that link sequencing to function or develop new analyses or explore technology development efforts at scale, implemented at scale. There are some practical considerations, budget considerations. In the work up, in the write up, I showed what our current estimation is for the cost of a very large project, and like this. And the total for seven projects, which would include one whole genome study, assuming very large sample numbers, is about $292 million, or $73 million a year spent over four years. But we're only proposing to provide 80% of that, or $60 million a year. With the idea that we know that several factors will reduce cost over time, but we don't know exactly what the timeline is, how they'll come into play. NHGRI has always done well to be optimistic about costs of sequencing. And I think it's also reasonable to be optimistic about cost of data storage, which for whole genome studies are a substantial part of the cost. And these could easily come down twofold in two years. Study design. Not all studies will require the largest number of samples. And if common controls pan out later on in the program, it might be able to do a lot more and more efficiently. And there are also other efficiencies to be gained. I should point out that some studies might actually require more in order to be comprehensive, so this could play both ways. And finally, we are going to actively seek co-funding, and as I'll talk about in a bit, other funding collaborations. During all these factors, we have high confidence that seven studies can be done as described, and certainly if it's possible that more could be done with this budget. Organizational considerations. We are proposing cooperative agreements in a research network. There's, I don't think, any other way to do this. And an open competition. We propose a small number of awards, two to four, simply because the size of the projects is likely to be very large. And the more you need to split them up, the harder it is to coordinate. There are also other efficiencies, but that has to be weighed against bringing in different approaches. We need good peer review of individual projects. And that seems obvious, but the fact is with a project, with a program this large that will bring in multiple projects over time, we'll have to bring in, we'll have to have a mechanism to do a good job in bringing in new projects. So what we're proposing is that the initial year of work will be fully proposed in the application. And again, that's to show how, to make sure that the sequencing and the samples and the data analysis are all presented as an integrated package to begin with. But then bring in other projects over time. There is an X01 mechanism that allows for use of community resources. There are community, we could have community workshops to begin to design projects, to look at new projects. Some projects may be initiated by other institutes, and that would be an opportunity for co-funding, and possibly work through the X01 mechanism. And finally, over the years, there have been projects that have come up, new opportunities have come up for large-scale sequencing projects that have been very high priority for NHGRI, and we have wanted to take advantage of the opportunity. In the workshop, and coming out of the workshop, and even before, we'd consider how to leverage our funding, and thinking about new ways to do this. Whatever mechanism we come up with has to allow or incent co-funding, for example, through X01s to allow a route for participation of other institutes. We're going to need to reach out to other institutes to collaborate on this, on these. I have to say that so far we've had very good experience with a small number of other institutes collaborating on projects in cancer, of course TCGA, and with NHLBI, and NIA, and others. But we want to develop these further and broaden the number of collaborations. Second, the mechanism, whatever mechanism we come up with, should incent applicants to seek other outside funding to add to the number of example diseases that can be explored. And what we're thinking of is making partial rewards, and then providing additional funds to grantees over time, providing those funds, the held back funds to grantees that are successful over time in identifying resources for more or more comprehensive projects. And finally, whatever mechanism we have has to accommodate potentially significant changes in capacity or volume. For example, large increases due to identification of new opportunities, as well as changes due to the completion of projects in one place versus another. So we're always asked to talk about relationships to other programs that are ongoing at NHGRI, and this is actually easy to talk about because these programs will be related to almost everything else that we do. And I can talk about any of these in detail, but the fact is that we talked about, sometimes talk about a flagship, but in fact this is just one element in a fleet of fairly substantial efforts. And we don't know exactly all the interactions in advance, but we know that there are going to be opportunities that come up. Relationships to worldwide efforts, that's a little harder because there are many. There are going to be many individual complex disease efforts in the next four years, there's no question. And we also have talked already about some large cohort studies that are starting up and in four years we expect that they will be very productive. And all along we're going to need to look for opportunities to collaborate and synergize. One idea is just to piggyback on some of these or to avoid them if they're going to be done well and done completely, and expand the range of studies to get the better general insight by going elsewhere than these. I think with these there's a lot of opportunity for collaborations on analysis, that would be very interesting. So we have the technical, so some summary slides, we have the capability to comprehensively find genomic variants that contribute to common disease. The proposed program will attempt this for a representative set of common diseases over four years, and doing so will explore and compare a range of genomic architectures and project designs as many as possible to learn general principles. The program will develop improved methods for obtaining and analyzing the data, which will lay a foundation for the wider community. And finally, the program is intended to develop resources for communities of those investigating specific diseases and also resources for genomics in general. With a couple of details, we believe that the recommended funding will be sufficient to have high confidence to achieve the minimum goals given reasonable predictions, but will work to exceed that minimum. To increase the number of studies that we can do, and the likelihood of successful seek co-funding, we'll structure the funding to incent institutional and other contributions. We'll probably need to try several approaches before we know what works. And again, finally, the scientific goals and requirements of the program mean that it will need as much flexibility as possible built into the program, into the solicitation, and into the funding. And I'm going to stop there and ask for questions or comments. Who are the discussants? No, but there are three, weren't there three discussants? I'm sorry, I forgot to, I thought you had picked, I thought you had picked discussants. But I'll start with Juan. So I'd like to see if you could clarify or expand a little bit on this concept of comprehensive. So you couched it, I think, largely in terms of the sample size, or maybe it's phenotype diversity or maybe it's sample diversity. But there are multiple dimensions you could think about here for comprehensive. And one is sample size, one is the architecture of diseases, and the other is the genome. And I think if you, in terms of the depth, exome versus genome. And I guess if one really wanted this to be ambitious, you'd shoot for all. And it seems in a cursory way what you've got here is deep on sample size, semi-broad on diseases, but not so deep on the genome. And I wonder if that was discussed and if you'd like to elaborate on trying to push the field further than something that in five years time probably will wish we'd gone deeper. Yeah, so there's really two parts of this. So first, I agree with you. I think that if in five years we are not doing, I'd be very surprised if whole genomes weren't the standard for this kind of study. And built into this, these kinds of programs is the ability to assess midway. And the hard part is in some ways getting the samples, right? Once you can adjust the amount of, the kind of sequencing that you do within the confines of technology. So we expect this program to keep up with the technology and the costs as they advance. But I wonder at the current amount, I mean, the amount, the discussion was set based on a fairly conservative reckoning of the resources that we could have for this and what we think the state of the art is, hoping that we can succeed it. I guess just put another way, you went for 80% of today's cost or something. Why not really push that number? Why did you come up with 80% of where we are today? Is that just seen as too far to reach to try and drive costs and other access? I think it's almost a matter of, it depends how you're driving costs. So this would be a vote of, I mean, stating it in a strong way would be a vote of confidence, essentially, that things will get there. It will be a challenge also to push things in that direction. So I think it's in the way of, it has to be stated that way between those two with elements of both of those. Howard? So I'd like to echo Lon's statement by saying I think the way it's written, I support completely, I think it's a great idea. But I think opening it up a little bit around the context, as Lon suggested, I think it would be key so that you can look at those different dimensionalities instead of locking it into you have to do this and this. So I agree 100%. Then I have a question related to seeking other funding sources, which I think is a great idea. How do you view what to do if some of the potential funders don't have the same data sharing guideline? So for example, I'll use a pharmaceutical company as an example. They'd be willing to share some data, but not other data for exchange for doing this. How would you manage that? Yeah, that's a hard question because how I would manage it completely depends on the importance of the sample sets. The default, of course, is maximum openness. And we would ask for that first. And we always do and we always have. Most of the negotiations that we've had to have is over details when we've done collaborations with other ICs. And those have worked out. But I agree with you. I think our first preference would be to say no to those samples if there were any alternatives. But we would have to do an assessment. So why don't I help you out here because I know there's a lot of hands going up. So I see Didi and I saw Bob's. You know if there's anybody else, but Didi and then Howard, OK? So I would say in general, the concept ideas seem OK. But some of the things were brought up at the beginning are about blurring boundaries versus defining boundaries and also measurable progress. And so I feel like this kind of goes in a different direction from that. And I feel like the concept as a whole lacks a big vision. And I said this earlier about something that's really exciting and visionary like sequence to human genome or $1,000 genome or something like that. So I don't see that pizzazz and focus to the concept. And maybe it can still be done with these general principles that are in there. But in terms of how it's presented, I think it's really important so that there are really clear goals. And I feel like this is the largest resource really for the Institute and to be able to have this opportunity to do something really big with tremendous impact. So how will these centers work together in a coordinated fashion so that they can really leverage their capacity, large scale capacity and expertise and to be able to do something that couldn't be done otherwise. So instead of discreet little projects, I know you're emphasizing the large scale, but I think that needs more. And then when you talk about comprehensive, I see it even there are more dimensions than that. And I know you say, yes, we have the other programs like encode and other ones. But I feel like this is an opportunity at this stage to try to be more integrative. We're going to have all these samples, 25,000 cases, 25,000 controls. They can be utilized and gain even more comprehensive data that can be integrated to help truly solve several diseases. And then in terms of you talked about the set aside. And I don't know what proportion that is, but I guess there's different ways to do that. But it seems to me that one scenario would be that you're working right now to build these big partnerships so that when the grantees come in, they hit the ground running and they're off on these big projects. Or if that's not possible, that a large portion of the set aside is given so that when the centers are awarded funding and they're chosen as the centers, they can work together on these huge collaborative project definition and execution. So those are some of the thoughts. So you raised a number of issues. I do want to address one which is working together because whoever these grantees are, they will have to work together probably a bit more tightly than the current program does. Although I should say that the current program on select projects has worked very well, different elements have come together very well for large projects. And that's going to have to be made very clear in the solicitation that that's the expectation. And I think we're going to talk later in closed session about some specifics, about some aspects of what you discussed, that there could be some incentives also built in for combined work. There are ways to address that that I think can easily be part of this program. I mean, for your scenarios, Dede, that you just gave for some of this cost sharing, they're all on the list. We're not going to be locked in any one of them. Every one of the things you just said are possible. And as we go forward, we will be exploring having sort of ideas in advance of the partnerships we're ready to go on, some partnerships that might be brought to us by centers. I think the list, we want to have maximal flexibility. And I think any and all those mechanisms might be used. My concern I might have is based on the current centers. And they get momentum and they have these samples. And then things get kind of locked in. And they're also doing some smaller projects. And I just feel like it's how this all is implemented and is extremely important so that we really gain the benefit of having the large scale aspects. Eric, you said you had one quick point that related to Dede's. I thought that the point she made about sort of maybe mundaneness of the current proposal was easily addressed by many of the points on your slides Adam, I thought were fantastic. They're not reflected in the current write up. So as you formalize the concept, if you could take some of these more exciting points about the bridges between functional and study design, et cetera, movement to the document, I think would help a lot. OK. Happy to do that. OK, we have Bob, and then Howard, and then Dan. I don't have an answer to the following issue, but I thought I'd raise it anyway, which is this question about completeness. So I viewed this project in the entire context of going back to when first we wanted a genome sequence, then we wanted a DB SNP. We wanted to know what the variants are. Then we wanted to apply the variants to understanding complex diseases and all the GWAS studies that were done, which I have had some successes. And in some sense, the things that GWAS has failed to do was also informative. So GWAS has given us both positives and informative negatives. It would be nice to have informative negatives from this. So in other words, pick that cohort so you really have enough environmental information and you have enough longitudinal information so that if we fail, which we might, to find the variants that you think might be contributing, it's going to tell us something. And not just, oh, well, we just didn't have enough information to really answer the question. That's a good point. Howard? Two different unrelated points. So one is, I think part of the difficulty is we all want a different ship. You talk about the flagship. And in the past, it's really been more of an aircraft carrier in which exciting projects could take off and land and move forward. And now, often, we're talking about it more in this, right up to more of a destroyer-type model or maybe we'll have to love boat or something. I mean, we need to figure out what is it that we want and how do we do it. And I think there's a vagueness to it with the write-up that reflects that we want to have the whole armada and not just a particular flagship. So I think part of it is reframing it that way. And even the title, I mean, the title could be changed to kind of change the mindset in which we're approaching it. And so I think there's some aspects there because it really doesn't reflect the catalytic nature in which you've now talked about it. The second thing is phenotype rules the day. And the way things are written right now, it's the cost of genotyping and ancillary elements around it. And pulling together a couple of thousand carefully phenotype patients is doable. Putting together 25,000 for many of the phenotypes, we're gonna have to cobble together data sets that might be high quality individually that weren't designed to be pulled together. And so things get dumbed down often. And we've seen that even with some, we can pull together 100,000 patients for height, but we can't really do that for some of the other diseases we're interested in. And so I think some care needs to be taken maybe even partnering with institutes. If an HLBI is going to do 100,000 patients worth of prospective trials in these next few years, maybe some targeting needs to be done so that the phenotyping consent or whatever is in a way that that could be the platform on which this is done. So I think right now we're gonna say, oh, there'll be phenotype samples. And let's just focus on the genotyping. And I think that'll get us in a bad place. Farron? I don't think I'm gonna say anything that hasn't been said before, but I'll say it a different way that, so one of the concerns has been that there's not a pizzazz to this. And something you said in your director's talk that made me think maybe the title of this should be Pathways to 21st Century Cures or something like that. The congressmen and congressmen will be very flattered if they did that, I guess. Well, so some brief dumb down articulation of what the vision is. And no matter what the, I mean, I still think, and I think that that's not an unreasonable way of phrasing it, even if it comes from congress. So, oh, and we're in public session. I'll be careful, I'll be more careful. Right, right, so. So, but one of the concerns that I have is at the end of the day, we're not gonna be able to measure whether there's any kind of success. So that the idea of metrics is built into the document, but it doesn't really say what those metrics might even look like. And one metric might be how many, how many functional variants have we found? How many of those might be really useful markers for clinical use or markers for drug development or whatever. And then the other thing that I really want to echo is what Howard just said, and that is that phenotyping is not free. To get 100,000 well-phenotyped diabetics or bald people or blue-eyed people, that will require resources of some kind that aren't sort of a central part of this document. There's this idea that we'll partner with somebody, and magic will happen. And then there's the great danger then that the magic will be that you don't get the right phenotypes and the lesson we learn is that you should have got the right phenotypes. So I think that more attention needs to be paid to that. Okay, Jay, and then Jim. Hold this, I mean, I'll echo the point again. I think the title is not entirely inspiring, but I could imagine if you really think about what this is about, it's about the contribution of rare variants to common diseases, right? That's fundamentally what this appears to be about, at least, particularly if you're focused on the exomes. And in many ways, that's still an unanswered question. We haven't really asked that question with appropriate power for most diseases. So I'm not sure, I mean, this kind of echoes some things that were already said, but I'm not sure, it does seem important to define success and failure, not in a way that's tied to whether we hit our production goals and things like that, but explicitly around, if this is the question we're gonna ask, then let's ask it. And to succeed or fail, at least, let's have an answer at the end of it. Yeah. So, yeah, I just wanna put my two cents worth in for not over-promising. I think that talking about cures, especially in the title, would be insane. Yes, that's what we're all after, but we aren't doing ourselves any favors, right, if we over-promise. I would also just, I wanted to amplify what Lonne and what Howard said. You know, it's my understanding that the majority, the variants that have been linked to disease phenotype are actually not in the coding region, and I'm, I really worry about focusing on the exome because it's cheaper when we already have evidence that maybe the most important stuff isn't in the exome, excuse me, for these variants. So? Yeah, and because it's a small number of awards, I would also encourage to build in some mechanisms of collaboration that they're not so dependent on other institutes as a means to engage the community at large, the scientific community at large, other than the ones who are positioned to be the leaders of the effort. And I say that because I think it's important to, again, get excitement, get a feel that people doing certain, studying certain diseases can be a part of it. Can you give it, just to make sure I understand it, can you give an example of what you might mean by that? What kind of collaboration? Let's say someone who is an expert in some relatively common disease, but is in a small institution and they wouldn't apply for the, I mean, it's too much work and too little chance to be able to get one such award. But if in the RFA, it's something written that you have to have two, three of such collaborators, then it's the time to make partnerships happen. Eric? Goes back to the comment about phenotyping, but a little bit different. Just caution about focusing too much on disease only. I mean, a lot of interest today is on finding loss of function variants, even non-protein encoding loss of function variants that are protective for disease. And if that's the goal, and I think that's a laudable goal, particularly for this institute, you're gonna need to define the phenotype of protection. And if all you have is a lot of cases of sick people, it's very difficult to do. So having people with low risk, low LDL cholesterol, low blood pressure, low X, Y, and Z, I think it's extremely important if that's a primary goal and quite a bit of thought needs to be put in to how to do that at scale, as you say. How do you think? It seems, oh, I was just gonna say, sorry, it was very quick. I was just, just back to the vision and being able to have a measurable progress. I mean, you should be able to, in one very concise sentence, state what it is you're gonna accomplish and what you're gonna have at the end of the day. Just to point a clarification, I know we're gonna be heading towards a vote on this as in open session, I think. And my understanding is that when we vote on this, it will not pertain to any dollar amount. It's just of the concept qualitatively, not of the magnitude of the effort. That's certainly fair, but I don't think, given the goals here, I don't think you're gonna see this FOA at half the amount or twice the amount. The FOA. I mean, the dollars that are put up here are a realistic estimate of what the staff thinks are needed to achieve the goals that are listed. With all the caveat of dropping costs and whatnot. Ron? So, just rehearse one more argument. One more time to see how far we can go. At the risk of showing my age, it was almost exactly a decade ago, this same argument and debate was being had for GWAS on exactly what those studies should look like and how deep we should go on this and how many samples we should go and so on. And I think they did give both positive and negative results because actually we tried to compromise and get it all and do a largest number of diseases, many samples as we could get at the time and as deep as we could go on the genome. I'm just worried that one study of genome sequence will leave us flat here and I would like to encourage to push as far as we possibly can on all the dimensions and I've said it already once before but I just want to, as far as you can actually take this in all criteria I think will be better off. Ron, can I just clarify what you mean? So, do you mean in all the dimensions in every sub-study or you mean if you look across what this accomplishes over four years, the set of projects it does, we've touched comprehensiveness at least one or two times in every dimension, at least in one or more of the projects. Yeah, thanks. So I agree with this principle of trying more than one because you never know if you just, had we just picked at that time hypertension and bipolar, we might not be here doing what we did today. At that time, so I don't think one is enough, right? And I heard one or at least one that makes me nervous. So several in terms of the genome would make me feel better. So you get some shots on that to really test it out. And I know there are practical considerations here, but there are trade-offs one could give too. And I don't know if we're locked down exactly on the number of diseases and samples and so on at this point, but if we're not, I'd like to leave that open and I'd like to push for more. It's a judgment about comprehensiveness across all those dimensions including a range. So Howard and then Bob. Yeah, so I'm with Lon again on this. And so I think if I can look at from my perspective what Lon is saying is that rather than define how many diseases are gonna be looked at and defining whether it's whole genome or these others, I think it should be more open than that. If somebody can come in with a proposal that is able to do the whole thing on genome and the reviewers believe that that's the way to do it, why should we put the boundary on that? I think we should force the issue and figure out who's got the best ideas. If somebody is, can show that exon sequencing is the way to go, fine. But I think if we put too prescriptive on that, I think we limit the discovery potential. Bob? I would just like to say that I don't think we should be at a place where we're scratching our heads at the end saying well maybe we didn't get an answer that we wanted because maybe of this, maybe it's this, maybe it's this. We should have as many informative negatives and as few uninformative negatives as possible. I agree with what Lon said with one small exception which is that we now have 10 years worth of GWAS experience and we should be using that information to design this study more intelligently. And not that the GWAS was unintelligent, it's just that it was data-less. Any, Dee Dee? Back to just how to be more integrative with other kinds of data. Can you just elaborate a little bit on how you could see that happening? Yeah, so. I think it might be helpful. So there are some things that I can predict or have control over by the way this is set up and there's some things that are gonna be harder to but you can anticipate that there will be opportunities. So the mechanism for that in this, as described in this concept, there are two. One is fairly simple-minded and that's just to allow data types to be produced that can be produced on sequencing platforms that aren't genome sequence. So that's one step in that direction. I don't think that that's really a substantive way of addressing your question, but it's beginning to, beginning to reach out in that direction. The other way is we know that there are gonna be a lot of, we assume that coming out of this will be comprehensive sets of well-validated, carefully analyzed variants. As a data set. What if somebody had an idea about, somebody outside of the consortium had an idea about how to do rapid functional validation. Either different, either computationally with new algorithms and or maybe experimentally. Maybe there's some rapid way to do it. I think that this effort, inside the effort as part of the withheld funds should include some funds that are withheld that are sufficient for pilot studies to enable those. But that, so that's the short term and that's within this program. I think that there's a wider perspective on this. It's not within this program, but has to be considered as the function program is doing its planning and comes up and will come to council with its plans, several councils from now. Elise, are you here? Are you? Yeah, I was just, I'm gonna talk a little bit about encode and planning for the future and I think a lot of what we're thinking about for the future will take on from what was discussed at this workshop and will be presenting plans, I think in May Council. But I think it did, it was a theme in the workshop and I hear it here and I think it's good to continue to raise these. So when opportunities come up we can take advantage of them. Are there any other, are we ready for a vote? I certainly am. So can I get a motion to approve the concept as proposed? Second, all in favor. And I'm gonna ask you to keep your hands up. Those disapproving? Anyone abstaining? Thank you. Thank you, Adam. Thanks. All right, Lou, are you in the house? There she is, okay. So Lou Long is now gonna present the concept for the Centers for Mendelian Genomics. Thank you.