 Thank you so much. I'm Laura Rasmussen-Torweck. I'm an assistant professor at Sunrook Western University, and I've been involved with Emerge for about four years. And I'm presenting today, I think, as my role as one of the co-chairs of the PGX Working Group, along with Dan Rodin and Josh Denney. Can I have the next slide, please? So I'm reporting on Emerge progress in this area of genomic testing to date. And my presentation is going to be nearly entirely about PGX, because for Emerge 2, the vast majority of genomic testing that has gone on has been in PGX. So next slide, please. So I know we've talked a little bit about PGX in several of the earlier talks, but I just wanted to give everyone a quick overview again and catch people up who may not have been on earlier. There's three primary aims for Emerge PGX. In the first aim, across the network, we're recruiting almost 9,000 people. And the goal is to recruit people who we believe will be prescribed one of the drugs that have a CPIC guideline in the relatively near future. Then on all of those nearly 9,000 people, we are doing deep sequencing using a capture reagent I'll talk about in a little bit. And obtaining results that way. The second aim, the sort of actionable variance aim that you see on the screen, has several parts. One of those parts is that at each of the Emerge sites, they need to pick variants that they would like to return to individuals in the electronic health record. Then we need to generate clinical-grade genotypes so we can do this return. Then we need to figure out how to get those clinical genotypes into the EHR. And then we also have to develop clinical decision support to help our providers interpret that EHR information. And then in the third aim, we're taking all of this genotyping information that's coming off the PGRN seek, which is our Next Generation Sequence Platform, and creating a repository of variants of unknown significance. Again, mainly the rare variants. And then also pairing that with phenotype information that we've extracted from electronic health records across all the sites so that we hope we can initiate settings of function and genotype phenotype relationships. So that is the official overall aims of PGX. The reality is PGX does vary from site to site. Every site has had to tailor it, depending on what clinicians at that site are interested in returning. What the IRB at that site is interested in letting us do. So at times, I wanted to try to pull my hair out when we're trying to summarize and merge, or excuse me, PGX, because it can look a little bit different from site to site. But in some ways, I think that's really an opportunity for PGX, because since this is a diverse project, and it's being implemented differently, we're having a variety of experiences, and I think it's important to report on this. Next slide, please. So here is the progress of PGX as of mid-January 2014. We've recruited almost 4,000 people with samples to use on our Next Generation Sequencing Platform. And this includes a mixture of sites. Again, it's going to be a recurring theme, but PGX is implemented differently across sites. So some sites are recruiting Genovo. Other sites are recruiting from their existing Biobank. And some sites actually had clinical samples in their existing Biobank, and therefore, didn't need to re-recruit people for the sample. 2,200 of these people have been sequenced. And you'll notice the denominators are slightly different, because there are a couple sites that are sequencing more people than they are returning clinical results to. And then almost 1,400 people have had clinical genotypes obtained that we can put in the electronic health record. Next slide, please. So some details about the PGX platform, which we call the PGRN-Seq. It's a Next Generation Sequencing Capture Agent. And it was developed by our partners PGRN. 84 genes were selected by a vote of the PGRN community. And the sequence capture included the complete coding regions, and some sequence upstream and downstream. The platform also includes some known variants that are present on other commercially available platforms to make meta-analysis easier. Next slide, please. Batches of 24 or 48 samples are processed through Illumina flow cell lanes. And there have been really, really fabulous results from this platform to date. 32 diverse HapMap trios were sequenced. And on average, the depth of coverage per sample was 496x. And then when you compare those genotypes that were derived from the PGRN-Seq data, they were 99.9% concordantly existing SMD data from these samples in the 1,000 Genomes Project. Next slide, please. So again, the implementation of this platform across the PGX sites has been diverse. Because of the way this supplement was funded, there are seven sites that are running samples at CIDR. Two of those sites are only running samples at CIDR, but other sites are running some samples at CIDR and some samples at two locations. This is complicated, but again, it also provides opportunities to really understand what it's like to try to implement an NGS platform across lots of sites. We have some diversity in the machinery being used. One site is using high-entaurant, and the other ones are using a little bit of that. Next slide, please. And here you can see which sites are running at least some of their samples on-site, whereas opposed to others, are sending all of their samples off-site to run PGX. And then at the bottom part of the screen, you'll see that two groups may on Mount Sinai are hoping to return some results directly from PGRN-Seq, in that we mean they're actually going to try to obtain clinical grade results from PGRN-Seq, and I'll talk a little bit more about this in a second. Can I have the next slide, please? So again, when I was talking about the different specific aims for PGX, one of the most important aims for aim two was to get clinical grade genotypes so that we could implement things in electronic health record. Next slide, please. And here I'm going to try to clarify my language because it can get a little complicated when we're talking about PGX, but PGRN-Seq is generally run on research grade samples. And in a merge, generally, when we're talking about PGRN-Seq, we're talking about sequencing results on research results. Of course, to return results to electronic health record, we talk about needing CLIA-validated or clinical level of results. Often in PGX, we refer to this as genotyping because in most of the sites, they are using more traditional genotyping techniques to generate these clinical results. However, there is this exception of some sites that are trying to generate clinical grade results from the PGRN-Seq. So I'll try to be careful about my language going forward so we all understand what I'm talking about. Next slide, please. And here's another sort of view of how it's being implemented across sites in terms of which specific variants are being validated, so which ones we're generating clinical grade results on so we can put them in the electronic health record. These genes vary across sites. Several of the sites are all genotyping CYP-2C19, V-core CYP-2C9, and SLO-1B1, but not all. So again, we have a fair number of sites that are doing at least three pairs in common. Next slide, please. And of course, there is diversity in the way we are clinically validating PGRN-Seq. Six sites are validating some samples at the Johns Hopkins Diagnostic Library. This is using Sequinum panel. Most of the sites that are doing that are not validating all their samples this way. Other sites are using Sanger, Illumina-ADME, Sequinum-ADME, and again, many sites are validating at more than one location using more than one method. It is complicated, but it also provides opportunities to compare across these different measures and even within the same site. Next slide, please. Okay, so what are some things that PGX is really lending to the conversation and genomic testing? Next slide. And for these next couple slides, I really must thank Marilyn Richie. She gave them to me, and she's very actively involved at the Coordinating Center. So we have several calling pipelines. Again, these are required because we're generating the sequence data at many different sites. So in order to make sure that we have comparable information as a group, we're doing several, there's cross-site comparisons. So each site is performing sequencing on 32 HAPMAP trios along with the Emerge Study Samples and the Coordinating Center is calculating the concordance for these trios. And so that's the first of the two concordance checks mentioned at the bottom. The other is that the Coordinating Center is comparing VCF on Emerge Study Samples generated by the sequencing facility and VCF generated by the Emerge Coordinating Center pipeline. Next slide, please. So here's a cross-site comparison of these HAPMAP trios across different sites running PGRN-Seq. And as you can see, we have excellent concordance. Next slide, please. This is quickly an overview of the Emerge Variant Calling Pipeline that has been implemented at the Coordinating Center. They've used GATP. You can see the different filters that are used. And then there's two variant calling runs at two different time points. Multi-sample calling is run on the batch and the sequencing center for each site independently. And then quarterly they're running a multi-sample calling run on the entire site. Next slide. So here is the multi-sample calling by site at the CC compared to the single sample calling by the sequencing center. And as you can see, we have very good data to start with and it gets even better with multi-center calling. Next slide, please. And similarly here is the, excuse me, multi-sample calling of the entire Emerge Set with single sample calling by site. Next slide, please. Another type of QC analysis that we're doing is we're comparing the research and this is generally, of course, the sequencing results from the PGRNC and the clinical pharmacogenetic results which are generally genotyping on orthogonal platforms. The idea of this was to evaluate the PGRNC research platforms. It's complicated by different report formats. The PGRNC, we get data in VCF format. VCF is not a file format meant to be read by humans. It can be easily manipulated to get data out that's easier to understand. And then typically, the clinical grade results are often coming in the form of star alleles, particularly for the CIF genes. So there, you have to take the star allele, translate it to a haplotype, then translate it to individual genotypes. And even then, when you go to compare genotypes from that to those pulled out of VCF files, you can have strand orientation issues. So. After you're out about 11 minutes now, we'd like you to wrap it up. Okay, so standardization of reports would benefit the wider community. And it's also forcing sites to develop policies about non-concordant research results with clinical genotyping. We know our research results are really good. Obviously, we have to report the clinical results, but it's an interesting situation. Next slide, please. And just my final points. There's been a lot of development of systems to integrate genotypes as computed results. I'll let the EHRI groups give you the details, but as someone mentioned earlier, typically, genotype results are imported as PDFs or mentioned in the notes, they're not computed results. So how do we integrate these results and also document clinical interpretation as part of these systems? I think that the documentation of clinical interpretation can be complicated with the computed results. And this is particularly complicated when you're receiving results from multiple outside laboratories. And finally, what do we do if this interpretation changes? The CPIC guidelines are fabulous, but particularly for some of the rare star alleles for some of the CIP genes, the interpretation is changing. And how do we handle that in the electronic health record and how do we document it? Next slide. So this is just a summary of everything I've talked about today, and I will hand it over to my other panelists.