 Well, it's really great to be able to talk a little bit about Merge. Actually, I just thought about it this morning, realized that this actually represents the 10th year of Merge's activity. We started in 2007. A few of you in the room were at that first meeting of Merge, and boy, I can say we've come a really long way. The first meeting of Merge was really quite an interesting one. There were like five tables, and each site sat around their table and didn't talk to each other. So I think Merge has really gelled into a really great network of really great collaborators who work really well together, and you can see all of them indicated here on this slide, if I can figure out which, yeah, there we go. All of the sites, both past, present, and once you become a part of Merge, you're always part of Merge, so it's been a great ride. The goals of Merge have evolved over time, but right now they're really to focus on sequence and assess clinically relevant genes, presumed to affect gene function in about 25,000 individuals, assess the phenotypic implications of those variants, then integrate those genetic variants into electronic medical records to provide clinical care, and of course, as with Caesar, to also provide community resources that the world of genomic medicine, writ broadly, can actually have an opportunity to use. One of the things that I think we're quite proud of is the productivity of Merge over time, and just to point one number that I really like, almost 16,000 citations of Merge work, so I think this represents the external world's view of the value of Merge. So the top five, some of my friends have been, have been sort of asking me about what the top five were going to be, so I'll see if I got it right, I'm sure they'll tell me. The first is high throughput phenotyping and a database that holds that, and I think when we began Merge, I often heard from my physician friends, oh, you can't possibly use electronic health records for research purposes, the data's just not good enough, and I think the sort of number one product I'd like to highlight from Merge is the fact that I think we've actually demonstrated quite to the contrary that in fact electronic health records data, while noisy for sure, is actually high enough quality when the right algorithms are applied that you can actually both do discovery to identify new gene associations which we've done, and then to also use them in the process of implementation and actually get back to sort of the starting point which is how do we actually use this information to improve clinical care. Fee KB is the database where we actually collect together all of those algorithms and the knowledge that's derived from running those algorithms, and we continue to develop tools and processes for computational algorithm development across the collaboration. The second thing that I wanted to highlight is that we have approximately 100,000 participant genomic data set, 95,000 if you want to just talk about certain sets of data, but by the time you add all of the people for whom we have phenotypes and some level of genomic information, we are at about the 100,000 level and with the addition of the 25,000 that'll come through Emerge 3, this number will continue to grow. So we think this itself is an amazing resource for people to come in and use to do both discovery and especially discovery work going forward. We've also developed the Emerge Record Counter which is available publicly to anybody that wants to see what Emerge might have, so you can go into the link you see on the slide here and actually ask, you know, how many type 2 diabetics with some other characteristic are there for example, it's possible to get a variety of useful bits of information. This is also helpful I think as an important note to help us be able to identify things we shouldn't be able to, we can't, we aren't able to do. And that's always a useful thing to know how, what not to pursue. And then Sphinx, which is the sequence and phenotype integration exchange, is a catalog of genes, drugs and pathways that was developed as a part of the Pharmacogenomics Project, which is the next big highlight that I wanted to describe. This resulted in it from a collaboration between the Pharmacogenomics Research Network who had developed a terrific platform for assessing genetic variation in genes important for drug metabolism and Emerge was seen as a great site in which to deploy that platform. We ended up recruiting and sequencing 9,000 participants and that data set is available. There are 82 Pharmaco genetic genes on the panel and what we've identified are both things for which there are Pharmaco genetic guidelines for clinical activity. So the CPIC guidelines, the Emerge sites have taken a subset of those and put them back into electronic health records and are clinically using them to fire clinical decision support that can help inform prescriptions as a provider wants to provide a medication. So that if a person is a known non-metabolizer, it gives them the information to go to an alternative medication and then sites continue to collect utilization and outcomes data on this and you can learn more about this at this link. The fourth key result from Emerge I think is the development of FIWAS. This was led by the Vanderbilt team but it adopted widely across the network. This led to a variety of ways to think about for genetic variation, what are the phenotypes that are associated with that particular genetic variation. So G.WAS stood on its head if you will. And it's being widely used way outside of the Emerge network for a lot of people to look at comorbidities and to look at a variety of features related to the genetics. I will just highlight one sort of really kind of fun thing that came out of this which was a paper that appeared in science again coming out of Vanderbilt but with collaboration across the Emerge network where we were able to identify FIWAS based on the amount of Neanderthal DNA and variants that an individual carried and able to identify associations with what the consequences of carrying a little bit of our ancestors DNA might be for our current day health care system and really interesting project. And then we've also developed methods for large scale genotype phenotype analysis and then implemented them across a collaborative network. And then finally I think but maybe very I won't say most important but I think a really important way of the future is that Emerge is really focused on integration of genomic data into electronic health records with the goal of informing clinical care. So a variety of infrastructure and tools have been put in place things like figuring out how to put a genetic variant into an electronic health record as a computable laboratory value as opposed to the PDF reports that we're all familiar with getting from our genetic providers. I've spent a lot of time thinking about how do we not only do put that in but how do we use that to actually compute on it so to fire clinical decision support best practice alerts that go to providers. Also thought have spent a lot of time working on whoops it's not the screen here but a lot of time looking at how do we use this as a tool to communicate so the very large button using the open source info button system to provide information to the people that have access to this information through electronic health records. And then finally the clinical decision support knowledge base which is a partnership with another one of the NHGRI genomic medicine projects the Ignite Network and the goal there is to catalog and share clinical decision support implementation items and think about the design features that we need to have in order to move genomic medicine forward and to really begin the first steps of putting genomic medicine in the forefront and really implementing it going forward. So that's an overview of eMERGE.