Jeff Botkin, you're up next, he'll give the report for the Genomics and Society Working Group. All right, well thanks for this opportunity to give you an update on where we've been over the last year or so with the Genomics and Society Working Group. Here's our mission, provide counsel advice on short and long-term planning, priority setting for genomics and society activities, particular emphasis on LC research. Here's our group, and it's really a privilege to work with this group, just an outstanding group of folks from across the country, a lot of good disciplinary diversity as well as diversity of perspectives on a lot of these issues. We also just want to thank the staff for their outstanding support, and Nicole in particular, Lockhart is supportive of our group and unfortunately I guess can't be here today, she's nursing herself and the rest of her family today. So this has been a real pleasure to work with this group. So here are some new members who will be coming on here relatively soon I guess, Malia Fullerton, University of Washington, Laundra Nelson who's at the Princeton Institute of Advanced Studies, and then many of you know Lisa Parker, I think she was part of council not too long ago, right, or she was part of the Genomics and Society Working Group before, so she previously chaired that before my tenure from the University of Pittsburgh. All right, so a quick overview of some of the points that we've dealt with over the last year, so we had a wonderful presentation from Bardit Ravitsky, a scholar from Canada on lessons from their system, they're called gels, genomics, and its ethical, environmental, economic, legal, and social aspects research. And notably all genome Canada applications are required to have an integrated gels component. Pause for a minute to let that sink in. So as you see there from their name, their spectrum of issues they deal with is somewhat broader than our LC program as they also include environmental and economic aspects in their work. We also have a nice presentation from Chris Donahue who was here at least earlier. Thanks Chris, a wonderful addition to the enterprise here to get the scholar of history to be able to give us some insights on how this whole thing is developed. So back in May we had a broad discussion on how to consider the impact of LC research. And of course this is a challenge with any mode of research, but particularly challenging with a lot of the ethical, legal, social issues. How do you know whether you've made an impact or not? And certainly the genome legislation having been a primary focus of the enterprise two decades up till a decade ago, some real concrete outcomes in that regard. So as an academic set of disciplines, how do you know what you're doing? And part of this exercise was to really identify what are really a relatively large number of stakeholders for the LC enterprise. Patients certainly, research participants, clinicians, investigators, and the list goes on much longer than that. In addition to a variety of different metrics that might be appropriate to assess the quality and output of the research. And this says scholarship, of course you can count papers, we also want to think about what the policy implications are, what the practice implications are of this mode of research. We did recommend internally of course tying any metrics to the goals and mission of the LC research program and that that would be our boundaries around any comments we would make in this particular domain. We also have an update from the NIH Office of Science Policy by Adam Berger. They are now spending five million dollars annually for bioethics research. These are one year projects, so these tend to be supplements or meetings or fairly focused projects of that sort. It really prompted us to have some discussion about how we might foster bioethics research across the NIH. And the point being pretty obvious that genome is in the forefront here from our perspective, but virtually all the other institutes have relevant bioethics issues that might well benefit from some dedicated research. We spent some time also thinking about the strategic planning process and the LC piece of that. Key areas of input that we provided at the time. We wanted folks to distinguish between research education and great engagement that's all part of our in our basket. But to try to distinguish different elements there. Emphasize the research questions since LC is a research enterprise. Also to integrate the values throughout the strategic plan and values like the need for diversity, attention to privacy issues. These are issues that shouldn't be relegated to some LC subsection of the report, but really ought to be integrated across the entire report to illustrate that the institute takes these sorts of values to heart. We wanted the goals to be concrete and aspirational and really discuss strategy for outreach and engagement for the LC research community. How can we bring in additional scholars who have not been part of the fold here traditionally, but yet may have something to contribute to this enterprise. From January, we had a nice input about and this really joy presented from the work that the staff helped put together. Culinary analysis of LC trainee career pass and this was largely trainees who had come through the original SEER set of grants that were largely focused on postdocs. So we have a couple of years since those SEERS ran their course. The new SEERS are an RM1 mechanism, and really relatively impressive outcome from the trainees there in terms of their career pass having stayed within the field and demonstrating good productivity. Also a nice update on the H3Africa program from Ebony Madden. Good progress on the LC research there, as folks may recall, there had been series of assessments of the original proposals in the LC program that were challenging and subsequently the new crop of submissions was felt to be fundable and of good quality and they're proving to be productive. So wide ranging set of topics for this last meeting. And probably primary among those was polygenic risk scores and I think you've already heard from Terry how much energy is going into this particular domain, I think the LC community, certainly our group and the LC community broadly is pretty concerned about polygenic risk scores and how rapidly those have moved into clinical application. Of course you've heard and NIH or Genome Institute is addressing issues of reproducibility across populations and groups and the lack of good data to sustain polygenic risk scores in non-European populations being a significant issue with the test validity still at this point. We spent a lot of time thinking about the clinical utility issues. And some of the projects these are developing data sets on clinical utility and those are going to be forthcoming, that's terrific. And we also think we need longer term data points. We have to get past the point of people's attitudes and intentions for healthcare behavior, but track that healthcare behavior over time and really see what the impact of this information is. Obviously for folks who are increased risk, but those two who are decreased risk. I think there's a perception at this point that there's not good clinical guidance for clinicians. What do you do with a patient who's got a 4x relative risk of cardiovascular disease or what have you? And I think clinicians might well benefit from some focused recommendations about here's the best evidence we have for how you want to respond to patients who are coming in with this type of information. And I think we're quite concerned about the expansion of polygenic risk scores into behavioral traits and prenatal testing. Reports of companies testing embryos for intellectual capacity, testing children perhaps for school and learning aptitude. These sorts of things we think are a serious concern and may well tarnish the genetic enterprise if this turns out to be entirely unvalidated information. Also talked about genetic genealogies and forensic uses there, a very hot set of topics. Right now, many states are beginning to think about state level policy to deal with using genealogical information for tracking violent criminals. So active area for policy development there. And it's still unclear I think at this point how much the general population is concerned about this issue. I think there's some data out there that suggests there's a pretty broad split in the population. We know the companies are very much opposed to the use of the data in this fashion, but the general population we're less certain about how people are responding to these nonclinical uses of genomic information. The third issue, gene editing and gene therapy. Lots of attention being paid to heritable genome editing. That's of course not something that the NIH is funding, but very exciting developments on somatic tissue interventions. And I think our sense was a renewed attention to some of the research ethics issues in the somatic domain would be worthwhile. Some of these issues are somewhat familiar from other sorts of interventions over the years, but now it's real. And the studies in particular technologies each will raise their own set of ethical, legal, social issues that we may need to pay attention to. And then of course cost and access to the resultant therapies is a huge concern. And folks are aware of the large numbers that are being put on these interventions that is going to create significant challenges for society. All right, other issues, long-term outcome data collection. And this sort of reinforces much of what Terry had already said. We need an evidence base for both clinical utility and personal utility. So we have to structure these studies in a way that there's sufficient long-term data collection that we can make determinations about utility. Clearly questions about when sequencing should be implemented, when it's less useful. And as this crowd certainly knows, it's a test to a certain extent, but you get enormous volume of data across a whole spectrum of conditions. And we're very likely to find some of those are enormously useful and helpful for patients, other ones. Less so, how do we figure that out as time goes on without long-term data acquisition? We want folks to consider both clinical and non-clinical end points. And in this is a circumstance where the question was raised. So what's the LC piece of this? And I think it's our perception that the LC is just seamlessly integrated with the rest of the medical enterprise at this point, in terms of psychological responses, behavioral responses, issues of access, diversity, third-party payers, et cetera. And then lastly, achieving diversity in genomic research participants. It's an issue that a persistent problem. And I think that the sense here is that folks are trying very hard in this domain and certainly making some progress. I think the sense is that we, to a large extent, or at least to a significant extent, we know what works better than perhaps what we've been doing frequently in the past. And there are tools, and there are experts out there who've been substantially helped with this problem. So it's not, we don't have to invent a whole new paradigm here. So we need to continue efforts to develop new tools and workforce resources, certainly, in this domain. But really use the tools that we already have. And so one set of considerations is whether RFAs ought to more consistently embed within them expectations that the clinical teams have the right expertise and have the right concepts in there to make sure that the recruitment is diverse. And when you get to year two of your four year project and you're, oh my gosh, all we have is white people here. It's kind of late at that point to fix that problem. So to the extent that we have some tools that we know work pretty well, we have some expertise that we know can be helpful. Try to encourage embedding that within the application itself so that the study section can look at that as part of its review. And you have that as an integral seamless part of the project from the beginning rather than trying to fix it later. So that's the scope of our discussion and thank you. Questions for Jeff? Sure. Yeah, I appreciated some of the discussion points that you raised. And I just had another discussion point to add to the polygenic risk score concerns. I think, I didn't see this language used, but I think the language that might benefit some of the points you're making here is that of interpretability or lack thereof. So PRS scores, as we've talked about in previous council meetings, were a type of predictive model. And unlike more simple genetic predictors like, GeneX is mutated, indicate or counter-indicate this drug, those simple ones are typically mechanistically interpretable. You know why. We have some explanation why when you mutate the single gene, you get drug resistance or sensitivity, say, in that P450 or whatever, right? The problem with PRS is it's 200 plus features that you stick into this thing and it spits out a risk and you really don't understand why. There's been lots written in the past year, especially that I've seen come out in the machine learning community where they talk about the need for interpretable non-black box predictive models, particularly in cases where you have high stakes applications. And this is definitely a high stakes application you're talking about if you're trying to take the features in Google images and predict cat or cucumber. Maybe that's not so high stakes, although you'll notice it's very predictive. But if you're trying to take the features in your genome and predict your risk to cardiovascular disease, that's a high stakes application. And you'd like to have some understanding of why it's making that prediction. And maybe if you could solve that problem, it might mitigate some of the LZ issues. I think it's the sort of useful spin on that comment. Good, Matt, I like that comment quite a bit. And I guess from a clinical standpoint, without that sort of, I mean, part of the issue with a lot of the previous generations of genetic testing, mutation positive, mutation negative, mutation positive folks, certain age you have something you know what to do, right? At this point, without any underlying mechanism and having a continuous variable where maybe your response at relative risk of three is different than eight, maybe it's different when you're 15 versus 75, man, well, I mean there's so many variables that then go into this that it's going to be a huge challenge for quite a few years. Just a quick question. So who's driving it the most, the use of the poly of the polygenic risk course in this context where you're talking about especially when it's with respect to, you know, intelligence and this sort of stuff. It's the direct to consumer testers for the most part who I think are driving this. And then some of the, it's the commercial, I think, folks who are definitely driving this enterprise from my perspective. Yeah, I'm not sure that's totally fair. I mean, several of the largest groups publishing what they think is the clinical impact of their polygenic risk scores are large academic medical centers. I mean, there are a lot of clinical, I won't take clinical geneticists because they're not all medical geneticists, but there are many physicians working in this space. I do think one of the most attractive aspects of it from a clinical perspective is that right now in the more traditional genetic testing for Mendelian disorders, we sort before the test. We say, well, you're at high enough risk to get this test and you're not, so you don't get the test. And then even once you get the test, it's likely you're going to be negative. Whereas from a clinical perspective, the advantage of polygenic risk score is you just apply it to everyone and you get an outcome. That's actually much easier from a clinical perspective. Of course, the problem is we don't know the utility yet and we don't have scores that work in all populations. But I think from a clinical perspective, it's a much easier model. And I think that's what's driving a lot of the interest. Especially in fields like cardiovascular medicine where there already are good models, they're already basing it on family history and cholesterol and whether you have hypertension. And they want to add this as one more aspect of those models. Yeah, and I think obviously the difference is if you've got a high cholesterol, you pretty much know what to do about that or high blood pressure at this point. And it's conversations with Lucia and Teri about this before from the Genome Institute's standpoint, my concern or a concern was the fact that if we're putting a lot of money into solving the validity issue by recruiting more underserved populations, more diverse populations to get better scores, is there some implicit promise here that this technology is going to be useful for those groups as well? And I personally just have to say I have some concerns that that's going to turn out to be the case. And so we ought to be thinking in parallel about the utility issues as we develop the better validity tools. So I guess in addition to stop smoking, exercise more and eat a better diet, we have the polygenic risk score. Yeah, but it strikes me that as Erica said before, genomics has spread across all the institutes and LC should really spread across all the institutes because they are investing in polygenic risk scores. Is there any way of leveraging this type of opportunity to engage the other institutes in helping support these types of initiatives that you're doing and really developing the information that's required to translate this out to the clinical realm? Well, I'll just say I think we need longer term studies. Part of the problem is the kind of four-year window in a lot of these things, you don't get the longer term data that you absolutely need to demonstrate some of this, but sorry. Well, I don't know if anybody from the LC Research Program wants to stand up and say anything, but I think other institutes are thrilled that we have the LC Research Program and I've seen very little teeny bits, but overall very little interest in dissemination and doing their own, even in their own context. The exception of that has been in the Common Fund, but then again, that's Common Fund money. I don't know. I'm surprised that there hasn't been more. Well, just from the standpoint of NHLBI and TopMed, we have an LC working group that Malia Folgen is leading and it's really important to have that working group talking about these questions, but it would also be important for them to contribute, as just as NIDDK is doing genomics and other institutes doing genomics. It just doesn't seem fair. Joy, can you give us some insights? Yeah, I'm not sure I can add that much other than there are other institutes who sign on to our program announcements and do fund some LC research, but it's certainly not the same level that Genome Institute does, and it's a constant struggle. But thank you for that input. So were there any lessons learned or insights from Vardit's presentation of how they do this in Canada? Is it just a top-down mandate, and therefore it shall be done? Or in terms of mandating more widespread examination of LC issues? Yeah, I think it took a number of years of advocacy before they adopted that particular model. But I don't think I had the sense from her that that's a highly controversial element at this point. And there's not been, to my knowledge, backlash from the basic science community say, why are we wasting money on this stuff? That it's sort of an expected integration. And I think, as she described some of the projects, of course, it would be minimal. There just aren't interesting LC issues. But at least folks have to deal with the issue. And to some extent, that's how NIH is dealing with other kinds of issues. You're now at least part of the IRB regulations. You have to say whether or not you're going to return results. And part of the NIH application, you have to say, are you going to enroll kids or not? And if you're not, tell us why not. If you are, tell us how you're going to do that. This could conceivably be something along those lines to say, speak to the ethical, legal, social issues. And if they're not relevant to your project, say so. If they are, how are you going to deal with them? Just relevant to the question about other institutes and return results. So NTI is actually having a two-day meeting, June 8th and 9th, specifically about returning results to study participants. So I think certainly there's a lot of interest there. They've lagged behind in many of their studies for returning results compared to NHGRI, but certainly a topic for them. And the other thing I was just going to say, since you mentioned directed consumer, I do think one of the challenges, and maybe your committee wants to look at this, is at the same time that I think some groups are really pretty thoughtfully trying to roll out polygenic risk scores in a clinical setting. There are both direct to consumer and frankly direct to physician. There are many companies offering a variety of genomic polygenic tests to practices that want to be able to show they're using genetics. And so you have kind of these streams happening at the same time where you have people trying out polygenic risk scores in kind of large settings and then you have commercial products where people are getting all kinds of predictions from a physician's office or direct to consumer that are not based on nearly as much robust. And the confusion I think that's generating in the field is pretty significant. Yeah, good, thank you. Okay, thank you. Thank you, Jeff. I'm going to let Eric introduce the next topic and Ellen, do you want to come up? All right, slide.