 Okay, welcome back everyone from the break. We're going to reconvene the open session of the advisory council. We're coming back to a concept clearance that will be presented today on the multiomics for health and disease. Before NHGRI can publish funding opportunity announcement that has a set aside of funds associated with it, must obtain concept clearance in an open meeting. We will use our council for that purpose so the council is aware of all of the FLA's that are issued by our institute. So Joannella Morales, Program Director and NHGRI's Division of Genomic Medicine will present the concept to you. And we'll have a discussion answering questions from council and when the discussion is on its course, I will ask for a vote to approve the concept. So Joannella, are you ready? Yes, I am Rudy. Thank you. So good afternoon everyone today we are seeking council's input on a concept entitled multiomics for health and disease. And I will be presenting the concept on behalf of our multiomics team, which includes Aaron Ramos and Terry Manolio. In my presentation I will follow the outline you see on the slide. First I will provide a summary of the preliminary discussions we had late last year with a subset of council members, and then I will proceed to describe the concept in a bit more detail. Okay, so first the preliminary council feedback. In 2021 the NHGRI paid increased attention to the topic of multiomics. In June 2021 we hosted a workshop entitled multiomics and health and disease, where we convene leaders and multiomics technologies to discuss the state of the field, and to gather recommendations on potential areas of research based on multiomics. As council may remember, in September 2021 or at the last council meeting, Dr Chang and I provided a report on the proceedings and the recommendations that stemmed from the workshop. During the fall our multiomics team started developing the concept idea, and in November 2021 we sought preliminary feedback from a subset of council members. I would like to point out that this represents a new approach to the concept clearance process. And so the goal here is for council members to have the opportunity to offer feedback early on in the concept development process, and for program directors to have time to incorporate their input in advance of the council meeting. So in November as I said we met with the six council members listed here, and we received very valuable feedback so I would like to thank them for engaging with us in this way. So during the two meetings we presented an outline of the concept, and council members were generally enthusiastic about the concept idea and did not request a follow up meeting. We did however have a few recommendations that I have listed here. We were encouraged to clearly articulate the primary objective and the desired outcomes to increase the linkage between sample collection and data production to centralize the data analysis while still ensuring the analysis remains a collaborative consortium wide effort, and to expand the proposed list of omics assays. So we took these recommendations into account as we finalize the concept that I am presenting here today. Okay, so next the background and rationale. As all of you are aware recent advances and high throughput technologies over the years have led to increased access to distinct types of molecular data or omics data, some of which are noted here genomics epigenomics transcriptomics proteomics metabolomics. The concept we will be discussing today is focused on multi omics, which we have defined in the past as a systems biology approach, where the focus is on the biological system as a whole. And the data sets of interest or are the multiple owns, and the hope here is that the integration of the distinct molecular layers will provide insights that go beyond what each single omic layer alone can produce. The systems biology approach implies a comprehensive assessment of the biological system be that an individual a tissue or a cell, including the environmental exposures to which it is subject. This of course requires high throughput technologies, and this generates a vast amount of big data that of course then requires interdisciplinary expertise to understand and interpret. Now multi omics integration has been shown to be particularly useful in a number of areas, including some of the ones listed here. For example, to improve the way disease sub types are defined, or to identify more precise biomarkers and the ones identified in single omics approaches, or to define relationships between omics data types, for example, and some successes have been recorded in the literature though I note that most studies to date only include two or three types of omics data. One successful example is shown here on the right, published by Lee and colleagues, and there they integrated genomics transcriptomics proteomics and metabolomics from multiple anatomical locations to improve the accuracy of classification of chronic obstructive pulmonary disease cases. Despite the successes, some gaps and opportunities do remain. For example, the production of multiple homes from the same sample is still challenging. This is in part due to enter and intraome variability, non uniform content across platforms and assays, and the lack of consensus approaches for quality assessment and for dealing with missing data. Also computational methods to integrate, analyze and interpret data are still underdeveloped. For example, when integrating multiple homes from the same sample or integrating multi omics with other types of data such as clinical and environmental exposure data, and also the lack of consensus approaches to integrate multi omics across diverse populations. Finally, there is a lack of data collections that are prospective in their study design that are consented for broad data sharing and general research use that have well described and harmonized metadata, and that are comprehensive in terms of the types of omics data that are included. And the concept we are discussing today aims to address some of these gaps and challenges. So now I will speak about the objectives on the scope of this program. So the overarching goal of this program is to validate and enhance generalizable multi omic approaches to identify biological changes related to disease. And this will be done by establishing a consortium that will bring together experts to apply multi omics approaches in several disease contexts. These experts will first explore the use of multi omics to detect and assess molecular profiles associated with healthy and disease states. They will then leverage these exploratory studies to develop generalizable data harmonization integration and analysis methods best practices and standards. And finally, using all the data generated as part of the program, the consortium will create a multidimensional data set and a visualization portal that is available to the wider research community and is interoperable with existing resources such as top med GTX and so forth. I would like to note that while this program may provide some insights into disease ideology for the diseases that are included as part of the program. The primary goal here is to validate and enhance generalizable approaches, or to provide a set of established consensus based methods best practices and standards that are generalizable across diseases. So this concept proposes three funding opportunity announcements to support a consortium that's composed of the three components listed here, disease study sites, omics production centers, and a data analysis and coordination center. I will now describe each component in a bit more detail. We have a list of envisions four to five disease study sites. We expect each site to propose a study focused on a disease area where there is evidence that integrated multi omics would be particularly impactful. Now some examples are listed here, including relapsing diseases with exacerbations, or heterogeneous diseases with clinically relevant sub types, or diseases that have clear or distinct stages, or transitions. The hope here is that these types of diseases would allow us to observe aspects of disease progression in the timeframe of this program. Each DSS should also demonstrate the ability to recruit or re-consent a minimum of 200 to 300 participants. This could include, for example, two thirds of the participants with disease and one third of the participants without disease. These controls from each site will be pooled and used as a consortium wide comparator group, and this group with a minimal set of standard phenotypic data will provide opportunities for standardization and quality control, and will produce a unique data set that can be used in future The DSS should follow appropriate consent and community engagement processes, should ensure that at least 75% of the participants are from diverse ancestral backgrounds, including persons from populations not well represented in genomics, and were available. The DSS should use standard measures for phenotypes and environmental exposures, including social determinants of health. And finally, each DSS will also collect specimens at a minimum of three time points to account for baseline levels and disease states, for example, exacerbations, remissions or treatments. The concept proposes one or two omics production centers or OPCs, and the OPCs would utilize state-of-the-art high throughput molecular assays to produce omics data from the samples, including tissues and cells as needed that are provided by the participants that are enrolled and collected by the DSSs. Now the data types and assays that you see in the white box are currently in scope, though there is some flexibility for the OPCs to be innovative and suggest other data types. However, each OPC is required to propose a minimum of three ohms, and one must be non-nucleic acid based, for example, proteomics and metabolomics, and this will of course ensure that these more challenging data types are included in the program. The third component of this program is the data analysis and coordination center or DAC. And this center will work closely with the DSSs and the OPCs to receive, track and catalog all the information on all the participants that are enrolled in the program. The DAC will also be responsible for the coordination of consortium-wide activities, such as those that I've included, I'm including here, for example, building consensus on recruitment strategies and the choice of omics data types and assays, developing the consortium-wide data analysis process, liaising with the Envol to facilitate data sharing, establishing working groups for methods development, producing the standardized multi-dimensional data set with the characteristics that I've included here, developing the visualization portal, and finally rapidly disseminating consortium outputs and findings. We anticipate all the components of the consortium to work closely together to fulfill the aims of the program. In order to perform the collaborative analysis and the consensus-based methods development that we anticipate, all components of the consortium must have understanding and expertise in the following areas, multi-omics assays, computational and statistical integration and analysis methods, participant recruitment approaches, and community engagement strategies. This program proposes the first year to be a period for consortium-wide development, protocol development, to discuss and make key decisions. For example, the recruitment strategy, the community engagement and informed consent processes, the core phenotypic and environmental exposure measures that will be captured, the omics data types and assays, and the procurement processing and analysis methods for the bio-specimens that are collected. During the next several years of the program, all sites and centers are expected to contribute to data integration and analysis. It will apply computational approaches, interpret molecular profile associations, explore gene networks, and assess causal relationships. During this time, the consortium will also develop generalizable methods, best practices and standards, and will create the standardized and harmonized multidimensional data set. Towards the second half of the program, we anticipate the consortium will work on developing the visualization portal following fair principles, and will increase activities to disseminate the methods, the data, and the findings to the wider research community. Now, one important area of emphasis for this concept is diversity. As you are aware, there is an over-representation of European ancestry individuals in research, and there are scientific and ethical challenges associated with this. For example, undiscovered genetic variation, the inaccurate risk prediction tools, or the inequity in the distribution of benefits from research. And for this reason, this concept proposes that a minimum of 75% of the individuals recruited by each DSS should be from ancestral backgrounds underrepresented in genomic research. Of course, to do this successfully, each DSS should establish recruitment, retention, and meaningful community engagement strategies, including outreach to racial and ethnic minority communities. There is also an increased understanding that the promise of genomics cannot be fully realized without a diverse genomic workforce. Therefore, to enhance the excellence and inclusivity of the research environments for this program, applicants are strongly encouraged to assemble study teams from diverse backgrounds, including individuals from underrepresented groups. In terms of the relationship of this concept with ongoing activities, we view this program as complementing existing NIH investments such as those that are listed here. And this, of course, is a sampling and not an exhaustive list of the some of the significant efforts and years. And while we view this initiative as being complementary to the other existing initiatives, we also know that it is distinct in a number of ways. It is its perspective enrollment and study design, the fact that it focuses on multiple disease areas and intends to collect specimens at multiple points, aims to produce the major omics data types from the same sample, and it's seeking consent for future use and broad data sharing without data use limitations. And moving on to the budget. For this concept we're proposing about a budget of approximately 8 million per year and a program duration of five years. Therefore, the total budget for the five years would be 40 million. Of course, a total number of samples and sites will depend on the funds that are available at the time the applications are processed. So in summary, NHGRI proposes a new collaborative initiative to explore the use of multi-omics to detect and assess molecular profiles, to leverage these exploratory association studies to develop generalizable methods, best practices and standards, and to create a multi-dimensional data set that is available and interoperable with existing resources. Now while we expect this initiative to provide insights into disease causes and disease biology, the primary goal is to validate and enhance generalizable multi-omic approaches to identify meaningful biological changes related to health or disease. And with that, I'd like to thank the many colleagues who helped shape this concept. Thank you very much. And I will now turn things over to the three discussants to start the question and comment session. And I would like to first call on Dr. Howard Chang, and then Dr. Suryanskaya and Dr. Coulomb. Thank you. Thank you, Dr. Morales for the update and this exciting concept. So first I'm very impressed with how this concept has come together. I think the timing was ripe for this idea, this idea of applying multi-omics to health and disease was something discussed in the NHGRI on-term vision and has now really come together also from input from the community. And I like many of the components very much. I think it reflects some of the key aspects that were discussed in the workshop that we both participated in, especially the idea of the increased diversity of the participants and trying to really incorporate a range of technologies, including new ones that are being developed. One sort of feature that I want to comment on is that as currently envisioned, there is a separation of the disease sites that collect samples and an analysis center. And so it seems like that this needs some really significant communication between the two groups because that the analysis center would then be expected to be able to basically work with very heterogeneous disease types that's going to be coming from these different sites. And so I am glad that there is that one year planning at the very beginning to really think through how this will work together. But this is something that I think of because unlike one group proposing like this is our disease, we're going to collect this and we're going to analyze it. And so we're going to have to have this connection that's going to get a little bit, you know, could be a vantage to really harmonize and also look broadly across different disease, different kinds of diseases for common themes, but also there's a chance that you're really missing some of the key insights right that a topic experts may actually have. Thanks so much, Dr Chang for for the encouraging words and thank you also for your input, you were part of the workshop planning committee and also part of the subset of council members who provided input and and this point of that first part of of protocol development, we do think it's really essential in order to provide that linkage that as you mentioned is required in order to make sure that the analysis and the production and are linked and and are synergized and also to avoid doing a silo that are then not connect not connected because obviously the goal here is to develop these approaches these methods that are standardized and are generalizable across diseases. Any, any other questions on that point or if now we'll move to Dr Trianskaya for her comments. Thank you so much for a great presentation indeed a very exciting RFA I think. I think especially the perspective setup and the longitudinal sampling are very important and of course the emphasis of multiomics is indeed very timely and I really like the goal of developing a data set that will really see the development of new methods for this. I think that's the right emphasis, especially for an HDRI specifically. I do think, you know, sort of to follow up on Dr Chang's point about the set the fact that the disease sites are separated from the analysis site I would also emphasize that that might be a challenge and we would need to make sure you would need to make the interface very closely. And then to add to that I would really put in some thought about somehow seeding research on new methods development, which it's tempting to say that that's what the DACC is going to do but of course an effective DACC is going to also mostly focus on really making the data accessible harmonizing it all of the things that you listed. And once they do all that there's not that much time for methods development so thinking about how to seed that would be important. And then finally I think one of the big strengths but also challenges of this is diversity of every type right. And that's, of course, it's very exciting because you're emphasizing diversity of both samples and techniques and diseases and I think the key would be to make sure that there is really a knife samples of a certain type. And if I don't just mean of the individuals of ethnic background I also mean for example, if metabolomics let's say is one of the sub types, but there's not just a very small subset of samples that has metabolomics, a couple of metabolomics, etc, so that there's not to develop robust analysis, but a very exciting initiative I think this will be great. Thank you so much and I did want to comment that we view the role of the DAC as as being a coordinating center so so in fact the analysis we hope will be done by all the components of the consortium working together. The DAC, we wanted for there to be one component that can take responsibility for making sure that it gets from beginning to end, or that, that the analysis is integrated. We do understand that each DSS will have its disease area of expertise. And the idea here is that all the components working together would would produce the the analysis and the developmental methods and then the DAC will serve as a coordinating entity. So I hope that that that is, that is clear. That sounds great. So the individual disease centers would also have analysis expertise and would develop methods. That would be very powerful. Yeah, that that is, that is the expectation here is that they would have the expertise to also be able to contribute to that analysis component. Olga, I would add, Joan Ella mentioned that planning period, and that's a great opportunity for us once we see the applications that came in and we're awarded then we can sort of look at the menu of things that are in front of us and like you said make sure that we have sufficient sort of data sets that we can produce generalizable results. So, so thanks for raising that. Yeah, the planning period is a really smart idea that's great. Okay. If there's no particular comment on this point, we can move on to Dr. Cullo for his comments. Thanks. Yeah, I like all guy and Howard I'm very excited about this particular initiative. I think it kind of heralds a new paradigm in addressing biological complexity. And we've hitherto use flat approaches, a unidimensional approaches but this is a really a paradigm shift in that using multi dimensional data, and also, you know, temporal profiles of disease so I think it's heralds a new kind of era, potentially in addressing biological complexity. And I think the groundswell is really apparent in how it's phrased in the strategic vision that was published and also the feedback that you got from the focus conference so there's obviously a realization that this is the next step you have to take to address, you know, disease complexity. And I think I respect the fact that you recognize the limitations of this that you are not really expecting tremendous insights at this stage but you're setting the kind of the path for standards and methods development because I know with these examples, you know that might be a bit tricky. I mean ideally you'd have 10,000 people that you'd sequence every year, you know see every year and you'd get some meaningful power for certain events. But I think this is a great start I just have three or four points that perhaps are worth discussing. Firstly, this is going to be have tremendous emphasis on statistical analysis you have potentially millions of data points for maybe 500 individuals or 1000 individuals so this will require machine learning and other novel statistical approaches so I think that has to be emphasized. The second unique characteristic is the, the multiple measurements over time so that again has, you know, requires statistical approaches to address repeated measures. And also related to that I wonder whether we should harmonize how data collection is spaced or multiple collections of space because you mentioned that it might be done after an exacerbation but that would potentially not give you because of reverse causality when something happens, you get changes that are consequent to the exacerbation so one option would be to set aside, you know, spaced intervals and if you get lucky, when you did the second measurement maybe the exacerbation happened right next to it and then you would be able to basically derive some insights into that but I worry about doing it after an exacerbation because you would. You're perturbing the whole system and you may not know what actually triggered the exacerbation so I think there may be some part needed for that. Something would be related to a presentation earlier today about the environmental factors and really making sure that we emphasize or have some element there to do a really good granular assessment of environment because then you'd have a really that and same goes for phenotypes. If you can do deep phenotypes and that will really complement you know the, the, the multiomic approach. And my last comment is, is this at this point just single initiative or, or do you feel that this is going to be renewed or is this something that'll let a thousand flowers bloom kind of hard here so so those are my overall I'm very enthusiastic about this. Okay, thank you very much for that and great comments. Yes, I mean I think the the statistical component here is critical. And as you noted we are hoping that there will be machine learning approaches and other. Important computational approaches here. The, the measurements over time that that also is is is key. And as you said, in terms of the spacing, obviously a lot will depend on the diseases that that do end up being studied at the moment we don't know exactly disease with exacerbation is one potential example but we may end up with with with other types of diseases. But I think that first year of planning that is part, I think of what will be discussed for the longitudinal aspect of this. What is a frequency when will it be done I think all of those discussions we're hoping will take place during that first year. And then, yes on the environmental component. We do hope as I noted also during that first year that their standard measures would be used and and for example we have in mind existing tools like the phoenix toolkit but we heard of that other program from the NIH has director that would be something also to to consider looking into of the for the last question I'm not sure at the moment we we just were focused on this initiative and and getting it off off the ground with Council approval. I don't know if Aaron or Terry want to comment on that last last point. So I think that's a fair statement Joe and Ella we do have some examples of programs like Caesar where we felt it was evident that sort of the next phase would be really beneficial and I think we'll have to wait and see a year, a year and a half or two into the program and then decide from there. Lisa. Your hand is up. Yes, thank you and thanks for the presentation throughout it. I was, I was thinking of features of the common fund bridge to AI, the prospective data set the emphasis on methods tools development development of best practices and standards. And then when if the car raises the issue of machine learning and statistical power and so on. Is there any hope interest in some sort of synergy between these two initiatives that seem to be sort of coming out at approximately the same time. Yeah, thank you so much for that. Yes, so that, as I mentioned there's a number of initiatives that are currently working or in progress but that there's there's some that are getting off the ground right now. And we do hope that's why we emphasize that anything that we produce will be interoperable and generalizable so that it can be linked with other existing existing programs and so certainly, we would hope that the applicants or we would hope that once a consortium is formed that they would try to make clear links with these existing initiatives that would be so complimentary. Okay, I've got Laura mark and then Steven Laura go ahead please. I have a question about tissue, and is there going to be a push to have the same tissue examined, or allow different tissues to be examined, you know, given the disease. And then my other question is about over the lifespan of do we have do we really know what children look like versus adolescents adults and then older adults. No, that's a good point about expanding as and we actually also heard this at the workshop that currently there's there's an over emphasis on using blood as the main specimen and that we should consider looking beyond that to to the relevant tissues. I mean of course there's no, we don't know at the moment which diseases will be will be part of this program so I'm not sure that there could be an exact match of the tissues that will be focused on. But certainly I think that that's something that, again, during that first year of planning and network and developing the protocols. Those kinds of questions can be considered. In terms of the lifespan. I mean that's a that's an interesting point and something we're thinking about it. Again, I don't know what kind of diseases we will, we will get, but that could be that could be something we consider. Mark, go ahead please. I think this seems like a very interesting and worthwhile program. One thing that's not clear to me, however, is is what is it about the program that makes it fit squarely within the NHG or I mission, right as opposed to being a common fund or some other institute project. And one thought there maybe it's a question is has to do with the diseases selected so will it be emphasis or priority on diseases where the genetic basis of the disease is is especially murky yours. But there are other aspects that you think make this uniquely a unique fit for the NHGRI mission. Again, thanks. That's a great question. I mean I think the NHGRI has had some experience looking at general at trying to see what the community is doing and then coming in and and proposing a work on developing methods that are generalizable and so I would say from that point this fits within NHGRI's mission. In terms of the diseases again we we are not limiting. We don't have any specific criteria at the moment of which diseases we would, in other words we don't have a priority list at the moment, we would, we would review the application process but we don't have any set priority at the moment. I don't know if that answers a question but that that is how I would answer that. I know at Joe and Ella that thinking about emerge and going way back to some of the first GWAS consortia that we helped stand up it was really beneficial for the consortium to have the experience with multiple different types of diseases. And that sort of led to, I think, more robust and generalizable methods and sort of approaching the data integration in a way that could be, again, more robust and useful for the, for the broader community. Steve Rich. I, you know, I love the idea of multiomics, even though I don't quite understand it or know how to analyze it yet. To be honest, but the couple thoughts came to mind one was the general NHGRI concept of data accessibility which was historically as soon as the data is produced it's out there. I think it's really going to be important as part of this that when the data are produced that it doesn't get restricted just to the project teams that it does become available to the scientific community because that's one way of stimulating development of novel methods and ways of approaching it. So I think that's one thing that could be very, you know, it'd be helpful just to see that stated. Another aspect of that, of course, is making available smaller analysis grants through other sort of pots of money that that hopefully, you know, Eric will have stashed in his back pocket to help stimulate analysis of these important data. So another thought came about flexibility and ability to incorporate new technologies. You know, what we know today is probably not going to be what will actually be useful in the future, even probably a year or two years now when things are going after the one year discussion period. I think it's going to be important to figure out from the standpoint of the data production teams just how flexible and nimble they are to move to new technologies. So that's, you know, the the omics we know today may be different than the omics we know later. And I guess the final thing and I hate to say this, and I will defer to Peter on this. It almost sounds like you need a mod version of this project. I'm thinking about the variety of mouse strains that can be used and the types of tissues that can be accessible and being able to generate that type of data and use having maybe parallel of a model systems approach as well as human approach. And just thinking, because, you know, certain things you can get from people but you can get them from mouse a lot more easily. Maybe Peter can comment after, after, you know, we get past Stephen. Thank you so much for all of these comments are really helpful. As we think especially hopefully with concept gets approved and we think about writing the funding opportunity announcements. All of the comments that you made are really helpful in terms of clarifying and making clear some of some of these points. So thank you, thank you for that. Yeah, I want to pick up a little bit on what Steve was just getting at the last point. And that is that, you know, a lot of these technologies are very, very different states. You know, for example, a DNA sequence database has a lot higher level of precision than maybe an epigenetics database or, you know, depending on what you choose for metabolism and so on these could be very different. And it seems to me that there may be an opportunity here in the early days to put a special emphasis on where are the holes in the technology. And in fact, you know, articulating that can also help programmatically in which technology development efforts should be going on. And so on inside, I think, you know, and I like the comment about simplifying if possible for model organisms so that you can actually be more comprehensive and actually know what works before you go through huge standardization expansion. So I just think there's a real good opportunity here for direction in the very early days of thinking about where are the holes in the technology. Are we actually getting the correct data, and so on in order to accomplish this but I love the project and I think it's very timely. Yeah. I'm sorry, go ahead. I was just going to say that on the technology point, this is a part of what we were also thinking that, you know, while this concept is not focused on technology development we figure that putting this consortium together would would allow us to see some of the gaps for other potential opportunities research opportunities. Okay, sorry, go ahead. Yeah, yeah, definitely agree with what what Steve is saying. For me, I think methods are going to be one of the most important things and you know any bioinformatician worth their salt would be able to come up with something to do with the multiomics data but we don't really have any any tried and true methods right now because there aren't that many multiomics data sets that that are really, you know, well tied to clinical data. And so I think we're going to need a lot of methods development and that would encourage you not to necessarily keep that with just within the centers but to as well but I think was suggesting to create some smaller grants to kind of spread around the creativity. And obviously to make sure that the data is is available to all those who want to try it out, whether funded or not. I think another thing I'd like to suggest and, and there's a slight conflict of interest because I was involved in this but the GA4GH is just published something called a phenopacket, which is a computational schema for capturing longitudinal clinical data including phenotypes treatments measurements and a few other things and I think it's not enough to capture the ontology terms it's it's better to capture the data in a schema such like this that's computable over over the course so you might want to look at that. I think the value I mean this I'm probably stating the completely obvious but you know the value of the model organ the mouse data might be that you know if you if you took a certain number of strains available at jacks there this is basically a model for precision genetics where we you know we have the genomes already we understand a lot about these mice. So we can do multiomics in much the same way and we can also do interventions to validate things and so having a component of the program for for mouse studies might allow us to kind of understand the genomic rule book a little bit better for how how to put together all of the multiomics data sets to actually discover therapeutically relevant straight on in the populations. Yeah so I joined it up. I wanted to follow up on Peter's comment with just an experience dealing with multiomics data that I have had I have had to do every once in a while with my collaborators. And it's every almost every time. Well actually every time we do it it's and we have to come up with something new. It's very specific to the problem. So just something to keep in mind when you know coming up in developing this concept that it it's you want to think about the possibility that every every single problem will have a different solution. Or maybe there's some way to to abstract it. It's not clear to me yet but it's something to keep in mind because it. You know you might you might want when you might be asking people to do the impossible if you're asking them to come up with a method that just those multiomic analysis. Thanks for that comment. Yeah, certainly kind of being complex, the data is complex and that's part of the challenge. Right but I'm, I guess the specifically the specific comment is that it could be very problem specific so is so that general solutions might be hard to come by. So just a recommendation not to be too optimistic about that. How go ahead. So I to very positive about this concept. I want to explore a bit the, the consideration of the diseases and the wisdom of being completely agnostic to what diseases are studied. It seems like a strong rationale could be made to pick ideal diseases for the purpose of developing paradigms for the use of multiomics data. Those might be diseases where multiple tissues are involved, the types of transitions that you were describing are routine and expectable. Where there are strong hypotheses about how multiomics data could be complimentary to each other. In that light, I wonder about the wisdom of creating too many obstacles for the recruitment center, you know to say you have to be able to collect 300 of these people, and 75% of them have to be underrepresented but the barrier, the, the, you know the bar may be too high to let a group recruit sufficient numbers to really achieve the purpose and I understand that's not necessarily to fully understand a disease but rather to develop paradigms. So, you know we've discussed this before but it seems to add weight to the concept of allowing recruitment sites to band together to recruit enough people that would allow the exploration of an ideal disease for this purpose. I would just add in the concept like you said I don't think we're necessarily we're not agnostic in that we want the investigators to justify diseases where omics are particularly impactful. And then I think your point about allowing researchers to come together as a network or a group makes sense and that that would be allowable. I would just describe that in the FOAs when we write them up. Other questions or comments about the FOA. Can I get a motion to approve the concept. Second. Second. All in favor please raise your hand and keep it up for that long. Thank you very much. Anyone opposed. Rafael are you opposed. Anyone abstaining. Okay. Joannella Aaron thank you very much council thank you that was a great discussion. Thank you so much.