 Okay, so thank you, this is exciting to talk a little bit about this enormous topic. I think that I was really skeptic in the early days of CSER about whether these very diverse projects could actually work together to create a synergy that was greater than each and everyone together. But in part because of the slides and paper that Katrina and I with Lucia and Jean have been putting together and all of the U awardees and our awardee PIs, I have become a believer that the synergy we create with CSER has been extraordinarily important. And of course, when we talk about clinical utilities, we're not talking about a single utility, we're talking about utilities. If you look across the CSER sites, we are actually exploring every single one of these clinical use cases in some form or other, perhaps at only a single site, others at multiple sites except for NIPT. So that's an extraordinary diversity and it's really important because these different use cases, these different clinical contextualizations are playing off of each other and teaching each other and reflecting each other within the CSER consortium. And of course, you're aware of the Gartner hype cycle and where we are in this is different for each of these use cases. We are moving toward that slope of enlightenment on as many of these use cases as possible and I think that's really where CSER is moving us up that slope as quickly as possible. Now we've had a framework for talking about clinical utility since the very early days of the individuals who wrote about what we should be thinking about in terms of genetic testing. We've been talking about benefits and risks from both positive and negative results. We've been talking about psychological, social, and economic consequences as well as the implications for health outcomes. But I think it falls to the always valuable Mewan-Curry to have put this in a format that really resonated with me, the sense of a broadest sense definition down here at the bottom which is sort of any outcome considered important to individuals, families, and society. And that really includes the economic outcomes and the waste of resources chasing down unnecessary tests. The broader sense definition, any use of test results to inform clinical decision making. Now notice it doesn't say health outcomes, it's clinical decision making and I would include in this perhaps proxy outcomes. So if we make a clinical decision to change a medication or to follow up in a certain diagnostic direction based on a genetic test, can we really say that this is improving the health of the individual or of the group? And then the narrow sense definition where the ability of the test is actually demonstrated to prevent or ameliorate a specific health outcome. And this is the hardest one to get to, I would argue, and this is the one that all of us in genomics are struggling to get to because of the rarity, the singularity, the uniqueness, and the long-term impact of the kind of information we are talking about. I'd also like to draw your attention to a different kind of curve, one that comes out of more of the business world where they're talking about a kind of tipping point, this so-called chasm here, where there is going to be a society-wide acceptance of various genomic testing. And I think we're, because of the incredible narrative of genomics and what it is supposed to do for us because of all the money in the biotech field, because of the convergence of our initiatives and NIH hopes and dreams and of all of our society that we represent, we're really close to that tipping point at this point. And there's this concept of social proof in there which is a kind of evidence-free sense that if everybody's using it, then it must be good. And I think this is powerful, this helps us, this of course generates enthusiasm and excitement for our work, but it's also something we need to be very aware of and interpose evidence basis, not against that, but in parallel with it, so that as these technologies diffuse quickly out into society, we are in lockstep creating both the awareness of the need for and the production of evidence basis in order to do, in order to keep society from adopting it when it doesn't make sense. I think you only have to look to the nutraceutical world, to the sort of fraudulent pseudoscience that perpetrates that world in order to see what the alternative can be. So I think CSER is most distinctive for its exemplar studies that can contribute to the building of an evidence base. And I don't think that this is a little deceptive because I don't really think big data studies are really off-target. I think they're a different kind of target. I don't think learning health systems that propose to create sort of feedback loops and teach us what's happening in individual patients are misguided at all. I think we need them. But in the shorter run, I think we actually need what CSER provides, which is smaller studies that really help us understand the targets we are interested in and that then inform these larger-scale studies. And I'm really proud of the many things I'm proud of in CSER. I'm really proud of five different randomized clinical controls. The NCGENES has a randomized controlled trial. The NEXTGENE project has a randomized controlled trial. NEXTMED has a randomized controlled trial about usual care versus whole genome sequencing and hereditary cancer. ClinSeq is developing a randomized controlled trial around different ways of sharing information. And of course, my favorite, we in MedSeq have two parallel randomized controlled trials using whole genome sequencing in the progress of medicine, both for cardiomyopathy, a specific heritable disease, and for healthy adults. Now, the outcomes for all of these randomized controlled trials are what are the range of findings, what are the new diagnoses in people with suspected phenotypes, and what are the health care utilization outputs, more or less. There's some differences, but this is sort of the universe. And almost all of these are the broader or broadest forms of clinical utility. So now I challenge us, what can we do to explore the true narrow-sense utility? How can we get closer to specific health outcomes? And I have three sort of general challenges. And honestly, I don't know how we would do these. But I think if you asked for them in a CESAR II, that would be part of the creative process for the respondents to sort of propose great creative ways of doing this. But I would say that the first one is to measure actual morbidity and mortality, of course, along with cost and other issues, of genomic interventions. And this is going to require really interesting creative designs, perhaps N1 studies, perhaps Sentinel studies, where you take a very specific subset of conditions. It's going to require larger numbers. In some cases, it's going to require longer follow-up in other cases. It's going to require proxy outcomes that we all believe in and will trust. So that's one challenge. I just show you quickly the kinds of results we're getting in MedSeq. And as Heidi and others have pointed out, we really don't have phenotypes on these people, but if we were to dig down and look at them both at a deep phenotype level, a biochemical level, we might find those phenotypes there as less Beesekker and his group have demonstrated already in ClinSeq. I think to this end, we need to elucidate penetrance in a far more multi-dimensional way, stratifying genomic information requiring broad and deep phenotyping, accompanying this sort of large-scale sequencing that's going to be going on all around us. I think by the way, Seeser should open itself up to interactions with industry, to interactions with other clinicians, with all of the other groups in a much more transparent way than we've done so far. If you just look at EXAC as we've been doing with some preliminary data, thanks to Daniel MacArthur here, you just take one category of long QC variants and you look across the exact database, you find dozens, if not hundreds, of individuals who are carrying loss of function variants. What is this going to mean when you can reach out in Seeser to parallel individuals and actually drill down? You can not only use wearables like people are talking about in the PMI, but you can also drill down with skin biopsies and calcium channel measurements. You can drill down as deeply as you want in a study like Seeser where you've got the clinicians and the patients right there working together. And thirdly, I would advocate using sentinel projects and sound epidemiologic principles to explore utility in a disciplined way where benefits might accrue to a small subset of patients. This is not easy because there are so many one-off cases in genomics that we have to figure out clever ways of aggregating. So just to give you a preview of something we're very excited to be presenting at ASHAG in the coming weeks, we've looked at aggregate penetrance of the 56 ACMG genes in two large epidemiologic studies, Framingham Heart Study and Jackson Heart Study. And we are finding that suggestive clinical features are indeed more commonly associated with pathogenic variants in these genes and that we are looking at an incidence ratio of six times in Framingham, almost five times in Jackson, and very significant p-values in this prospective comparison. So to finish up, we're facing a society that's absolutely convinced that the more genomes we do, you can see the 66 million up there in the corner, the more genomes that we do, the more we're going to get close to this ideal of preventive medicine. And we're also facing a society and sets of our collaborative investigators where, for example, population screening. This is just one thing that we are in particular interested in, but just as an exemplar, where population screening is happening all over the place. And I think we need to ask ourselves whether this is a kind of social proofing that we need to keep up with in CSER. So I finish up just with the notion that, for example, again, with population screening, we're going from public health emergencies in newborn screening to public health service and what we're trying to create. And where is it that we might be creating a public health obligation? And we need to keep up with that, with evidence-based productivity in CSER and its like. It's a fantastic group, and thank you very much for your attention.