 Good morning. It's nice to see such a huge crowd for the joint CSER Emerge network meeting. It's amazing to see so many people. This must be everybody in the world that cares about these topics, I think. I don't know. So just to build on some of the points that Bruce has made, I'll just give you a little bit of an overview of Emerge. Use the pointer here. So Emerge is actually maybe a little different. This geographic distribution from CSER, we've got a lot more in the middle of the country. So it's maybe a little more Midwestern, but we have we have the I guess we were pointed out, this is a coast, but we also think of this is a coast too. So we call it the third coast. But including from the East Coast, Broad and Harvard partners, Boston Children's, Mount Sinai, Columbia University, Children's Hospital, of course the NHGRI colleagues, Vanderbilt, Baylor College of Medicine, Northwestern, Marshfield and Mayo, and then the lone other coast, Kaiser Permanente University of Washington. The specific games and Emerge is in its third cycle, so a little bit more long in the tooth than CSER. I mean that in a positive way, not in a negative way. But the specific games of Emerge III are to sequence and clinically assess relevant genes, presume to affect gene function in about 25,000 individuals. This includes the ACMG-59, the famous ACMG-59 as we start to learn how to think about using that information. To assess the phenotypic implications of these variants, integrate genetic variants into electronic health records for clinical care. And this has been a real challenge in an interesting problem, but I think one where we're making a lot of headway. And then to create community resources that can be used broadly by everybody in this room and then anyone else that's interested in taking on these challenges. Most of the work of Emerge, as in CSER I suspect, happens in our work groups. We have a total of about seven work groups, clinical annotation, and you see the co-chairs listed here, HR integration, genomics, outcomes, phenotyping, pharmacogenomics, and return of results, and LC. And one of the main activities, as I'm sure for CSER, at each of our Emerge meetings, we spend a lot of time in the face-to-face meetings working together in these work groups to help address the issues that are related. And then of course there are the endless, it seems, phone conferences that help move these during between the meetings. We also have been fortunate to receive some supplemental support from NHGRI to do some things that we think are fairly important, one of which is to create the ability to, one of the topics that Bruce mentioned that's important is how do we share data and not always sequence data, but one of the more complicated things is phenotype data across the sites. And so the OMOP group and the supplement is to help create a standardized data set, data approach for us to share phenotype data. Obviously, if you're thinking about genomic medicine and how to implement it, the healthcare providers play a critical role in that process, and so we've got a supplement to understand healthcare provider engagement and understand what the influences, barriers, and factors that affect them are. And then we all believe that one of the missing pieces in our ability to really do future groundbreaking activities based on sequence data and phenotype data is actually how do we start to begin to think about environment? Gene by environment interactions is something that I think we all believe is still a challenge for the field at large. And so as an effort to try to start to do that, we have a supplemental group that's working on geocoding and trying to think about how to use those data together with locations of the people and then integrating with whatever other data sets might become available because of the crosswalk using geocoding information. Our main steps in terms of sequencing and return, I've already mentioned is that we're doing a panel of 109 genes, includes the ACMG, and then a series of genes that were defined by each of the sites just relate specifically to phenotypes that the sites were interested in pursuing both in the case of discovery and in the case of return of results. After the group very early on did a lot of work and focused on 68 clinically actionable genes that we think about returning results for. And then some of the other sites are returning results that might be not necessarily deemed as clinically actionable but to understand how to use those. Looking at single nucleotide variants, we are down to about 14 that are actionable. And so the final consensus list is 68 genes and then 14 SNVs that are returned by all sites across the 25,000 samples that have been recruited across the network. All samples have been sequenced at this point, all 25,000 and the results have been returned to the sites and the sites are now in the process of actually returning those results and understanding what the implications are for the healthcare system, for the patients, and for their families. And there's a harmonization paper now that's under review at some journal that will hopefully see the light of day at some point. And then samples returned to participants and a six month outcomes follow up has also begun. So we have a pretty hard target coming up in October of trying to have some real lessons to be able to provide based on the first set of outcomes. These outcomes are a challenge of course and that's one of the things that Bruce mentioned as well is how do we think about measuring outcomes. We're trying to harmonize 19 different outcome forms and our outcomes working group has been focused on that. You can see a list of these over here. I won't go through every single one of them but the goal is to provide abstraction guidelines to assist in data entry. We think this is an important part of this question of data harmonization outcomes and these collection forms will be collected by the coordinating center. And I think just to note here's a great opportunity where Caesar's done a lot of terrific work in terms of data harmonization and I think Emerge has been able to benefit significantly as we think about data harmonization by leveraging the work that Caesar's, nice work that Caesar's done. And the outcome data work has been collected and as I mentioned earlier, we really have a target date of October to actually start to have a first pass at putting this data together. One of the other things that we've been able to do over the course of the Emerge network is put together 150,000 participant genomic data set. So it's a nice, large data set that's rich. It includes a variety of different types of sequence or a variant information. E1 to E3 imputed data set is about 99,000 participants. We have about 12,500 exome chip data. We have whole exome sequencing and about 3,700 participants. Pharmacogenomic sequencing on about 9,000 that came out of a project that began in Emerge too to look at a series of genes that were important in drug metabolism. We have 1,800 whole genomes that are there for us to start to learn how to use whole genome sequence information and then the Emerge Seq platform where we have 25,000 people with 109 genes sequenced and all the variants on that. So we're at about 148,000 participants with genomic information at this point and we expect to get up to about 151 by the end of the project. Another major activity in Emerge is the use of electronic health records to do phenotyping. And so we began in phase one where we were just starting to figure out how to do data mining of electronic health records to produce phenotypes, completed 14 in phase one added another 29 phenotypes in phase two of Emerge and are about halfway through or so 27 phenotypes that we'll be adding in Emerge three producing a list of about 70 electronic algorithms for phenotyping that we think our A have given us a lot of lessons learned about how to generate phenotypes out of electronic health records but also give us tools that other people can adopt to try to validate those phenotypes across their sites as well. Emerge as I indicated thanks to that supplement has moved to OMOP standards and we're focused in this phase in the next year or so on really increasing natural language processing ability. A lot of the phenotypes that we are working on now in phase two are shall we say a little more complicated than some of the relatively simple ones that we did in phase one and so we're trying to think about how to better increase the tools and the sophistication in terms of this data set and our ability to create these phenotypes and then all this information is collected in a common variable set that's maintained at the coordinating center and that is updated twice yearly since it's a longitudinal study and we have ongoing access to our participants. Emerge has been strong in the area of publishing, total of almost 700 manuscripts at this point, citation count of almost over 27,000 citations so not too shabby for a scientific project that's been ongoing now for a little bit over a decade. So that hopefully gives you a little bit of a sense for Emerge and I think probably also, I think hopefully highlights based on what you heard from Bruce, some of the real opportunities that we would like to explore in the next few hours together and just to highlight a few as you are in your breakout groups, one of our goals for today is to actually create formal collaboration opportunities between Caesar and Emerge. It's one thing to be able to do this across the sites in Caesar or the sites in Emerge but if we can then take and put those together at a meta level, I think we really will be able to produce some useful lessons that can be very broadly applied across this whole area of genomic medicine. So as you meet together in your breakout groups, I think explicitly about a few things. I think about this question of data harmonization. Are there things that we can be focused on both at Emerge and at Caesar that improve our ability to harmonize data and those hopefully could be used far beyond Caesar and Emerge going forward? We've seen that collecting outcomes is a non-trivial activity. Everybody has a different view on outcomes and so I think the two networks together have a great opportunity to think about how do we share outcomes data and so let's focus on that as we meet together in the next few hours. Certainly Emerge is quite far along in return of results especially from the Emerge 3 25,000 person data set. Caesar's got a lot of experience with this as well and so let's think about lessons returned from the return of results and I know one of the topics that both networks are very interested in is family and cascade screening that is a consequence of the return of results so I think that's an opportunity for us to think about going forward. Bruce mentioned variant interpretation. That is something that both networks are very actively engaged in and let's see what we can do to improve variant interpretation. I have to mention the work that's done by a lot of the people in this room as well with ClinGen to help us with standards and thinking about variant interpretation and so I think together collectively those of us in this room have a lot of good information that we can perhaps contribute to those groups and then finally, and Bruce mentioned this as well, mechanisms of data sharing especially about things like phenotypes but maybe even more complicated is how do we share lessons that are learned from the return of results from the clinical impact of the work that we do and then from just in general how do we think about rolling genomic medicine out as in the real practice of medicine going forward. So that's I'm sure an incomplete list but just a few of the thoughts that sort of were obvious as we thought about going forward into the next session and so I think we're right on time so I think we'll turn it over to Mark who's gonna moderate the next session. I guess I should take a quick second since we are on time and see if there are any questions or comments people wanna make at this point from the introduction that you've heard.