 I was just hoping I wouldn't have pushed the wrong button. I think I need to get off of here. Is that all right? Is that better? Yeah, it's fine. Is that better? Yeah. It's certainly audible. And we have your slides up. OK. So I'm getting a lot of feedback on you. No, we're hearing you clearly. All right, so it's just me. In thinking about emerge three and reacting, I certainly don't want to think that sequence is the only way. I just wanted to bring it up because emerge two is moving into sequencing. And I think that there's a lot of reasons to think about sequencing in the future. And it brings together all the phases of emerge. I can have the second slide. I mean, from applying sequencing on a large scale and error was certainly a big part of this, everyone has highlighted that fact that there is an extensive amount of rare variation in the human genome. And if you go to the next slide, most of the variation in the genome is rare. And if you go to the next slide beyond this, what I've done is I've logged the minor allele frequency. And if you just advance, I'm highlighting 1%, which most GWAS chips with imputation will capture. And you can see that's just one part of the picture of natural variation in the population. If you advance again, you can see that there's a large fraction that we're not capturing. So I think everyone is interested in running the gamut of allele frequencies across the population. So if you advance in the next slide, if you look at deleterious alleles, and there's lots of ways to look at this, you can look at it by loss of function, splice variants, as Emerge is doing. And you can also look at applying every predictive method that you have. There's more than seven, more than 10 of these. But if you look at rare alleles in the population that are predicted by a large number of methods, those alleles that are predicted by all to be deleterious are very young. This is the age of the mutation in the population. So smaller is younger. And so these are alleles that also we would like to get at. If you get advanced to the next slide, Emerge 2 is certainly getting at that through a collaboration with the PGRN by applying a targeted-based sequencing platform that has been developed by the DEC Group. And the genes that are covered there by looking at extensive data. Although individual variants may be very rare in the population, if you add up their frequency, just the rare variants, it's suggested that 7% to 12% of the population may carry a rare variant that's unique to the individual, but across the population is very important for phenotype. So looking at these questions of the overlap or that in between for rare and common is very interesting and important question. So if you go to the next slide. So the case for sequencing is obviously the majority of variants is rare, but it can be collectively common. The most impactful are not only rare, but also young and ancestry. Maybe even family specific. When we talk about family history, is there some way to bring families into the study? And that's just the concept. And I think that sequencing is the perfect segue from merge 1 and 2 into 3. And just to state that sequencing is not the only way, but also looking at expanding the data set to across the genome would be very important. And if you advance to the next slide, it interfaces with large projects like ENCODE that has given us new knowledge about non-coding variation and where it's present in regulatory regions within the genome. And if you go to the next slide, this is just a slide that I stole from a colleague, John Stam, that his graduate student Matt Murano actually looked at the intersection between GWAS hits and DNA sensitivity sites and found that there was an enrichment of GWAS hits in these areas. And I know this is an area that ENCODE is very interested in. The intersection of these non-coding variants and where they overlap with the merge sites. If you go to the next slide, there are external groups interested in mining electronic medical records. And this is just an example that was in cell in the past year where 110,000 medical records were looked at for connections. And they found a connection between associations and Mendelian diseases. And it's intriguing to think about how Mendelian disease variants may interact to contribute to common complex diseases or traits. We've known in cancer for many years that underlying susceptibilities in specific genes provide the first hit. Is this true for other diseases beyond cancer, like cardiovascular disease, diabetes, et cetera? Sequencing would enable us the ability to look at this. If you go to the next slide, there are also great interest in reporting incidental findings from sequencing. And obviously, recommendations have been put forward about the genes that you would want to look at in this regard. And that's an important, certainly something very important for a merge to look at. So if you advance to the next slide, so sequencing would allow us to explore the spectrum of actual variants in the sequence. And also, at the same time, will permit the intersection of rare and common, the contribution of rare, perhaps, Mendelian, because many of those actionable genes are related to Mendelian diseases. How do these contribute to common diseases is an important question. If you go to the next slide. So the idea of sequencing and talking about sequencing, whether it's selected targets like the pharmacogenetics panel or the return of results targets or exome sequencing. I mean, genome seems perfect. The price is dropping. We're hearing $1,000. It's coding plus non-coding, which extends into some of the areas of interest for mining for the GWAS studies. And then in terms of looking at the phenotype, I just want to end with what was been most successful in applying sequencing in some of the large-scale sequence-based consortium I've been a part of. And if you advance on the next slide, and that's really skipped a slide. Okay, so go to the next slide. This slide, this is it. Okay, so sequencing the tails of the distribution. I think that phenotype and mining phenotype, the way you've done in a merge, is perfectly suited for finding the best types of phenotypes to think about sequencing. And the outcomes that have been positive from the large-scale application of sequencing has been looking at the extremes of a treat. Anytime we really went to an extreme, and I'm talking about using like an emerge-sized record-based, like 100,000, and picking out the extreme tails there. Obviously, you also find mistakes out there, which emerge is very familiar with. But you can get down to the most important set, and I think that Josh Denney talked a little bit about that with Stevens-Johnson syndrome. And the fact that you, if you can get down to that small handful of individuals sequencing would have also an addition to finding the location. You can do it by GWAS, but you can also get to the functional variance by sequencing. And just to give an example of this. I'll ask you to wrap up. Yeah, I'm wrapping up. If you take an example of high and low LDL, you go to the next slide. If you look at the distribution of variance by these little triangles at the tails of the distribution, at the high level of LDL as a trait, LDLR had a burden of rare variance in the population. This is expected from indelian. Hypercholesterolemia is pretty common in the population, estimated at 1%. But also at the tail of low, Pesky-9 actually fell out. It was there was a burden, but also a more common rare variance that was present. And this was sequencing hundreds of individuals, not thousands. So a few vests. I think there's a discussion, and Steve Leeder is going to take it from here.