 All right, thank you very much for the many people that responded with example questions. Could we put up the question slides, please? And so what I've done is not taking the information you've given me verbatim, but rather tried to group them. And I don't think I'm going to go through them in great detail. I've lumped them by category. The first was we have a series of questions related to genetic architecture. Some of them are obvious, but that doesn't mean by the way they're not important. They're obvious and I won't review the obvious ones. We had a couple of people that suggested that we talk a little more about the role of families and I've added that to this category is as we think about how to tease apart the genetic architecture of human disease we should consider the role of families. I think one of the most interesting points both for me personally and maybe for this workshop and having a large sample of deeply phenotyped individuals is this concept of plytropy that we can actually begin to understand the role of genetic variation on multiple traits simultaneously in the relationship among traits. For example, metabolic disease, obesity, metabolic disease, and cancer. I think there's some great emerging data on the role of genetic variation in that relationship. Genetic modifiers, we're going to talk more about that later. The second category was a lot of input on novel drug targets. Again, Francis mentioned this idea of the human knockout experiment, loss of function of protective alleles. I think novel pathways, I think that's another area where identification of rare variants for human disease will give us insight into novel pathways that pharma can use as hooks for developing new therapeutics. Another point is we have a number of common diseases where really we're focusing today on palliative care. I think having information about the role of loss of function variants in disease we can actually begin to have better treatments for those diseases and not just palliative care as I said. Pharmacogenetics, and I put pharmacogenetics by the way different from novel drug targets even though there's some drugs in both, obviously one is the identification of targets and the other is sort of, I lump it in my little head as a response to treatment. So there's the obvious quantitative response to treatment. I think there's a lot of help in drug dosing, my own personal interests, and I think something we could be using a lot in the clinical setting is adverse outcomes. There's enormous numbers of adverse outcomes and by linking genomic information to healthcare records we can begin to get insight genetic and otherwise to the etiology of many of those outcomes. One of the things that large-scale cohort studies, one of their characteristics is most of them tend to be longitudinal and they're not just a cross-sectional study, we're collecting information and following individuals over time and collecting again. Particularly in young people we have a chance to understand normal development and by understanding normal development we probably have better insight then into abnormal development. For the epidemiologists in the group that obviously we can start to begin to predict incident disease, what are the predictors of future disease and I think that's probably very important for gene environment interactions so they don't have a confounding between disease and treatment. Again Francis reminded me yesterday about the healthcare overhaul bill has a large component on outcomes research and I think we can begin to understand the role of genetic variation in outcomes whether it be mortality, second events, response to treatment, etc. The other comment that we had multiple times in the questions was somatic variation over time. I'm not an expert in cancer but I think we have the ability in large-scale longitudinal cohorts to understand the role of changes in the genome and how that gives rise to malignancies over time. Health disparities came up both yesterday and in the list of questions. I think we have an opportunity both to look at the role of genetic factors. Probably more important though is the role of gene environment interaction in health disparities and particularly in this country I'm not as familiar worldwide but there's just a growing economic divide in this country and I think it's leading to larger and larger health disparities and we have an opportunity with large-scale cohort studies to better understand the role of genetic factors in gene environment interaction in health disparities. Then epigenetics, apologies for the experts in the audience, I lumped together epigenetic changes in somatic variation into one category here probably wrongly. We can look at the role of both in disease severity, particularly late onset diseases. The role of environmental influence on both epigenetics and somatic variation. Changes in life course, I think Steve did a nice job in talking about the upcoming on-code papers is looking at the multiple dimensions or new dimensions really of the human genome. We can look at changes in the genome across the life course. There's changes when we think about somatic variation, there's changes among tissues and I think it's going to be very interesting and very challenging by the way as I spend a normal amount of time in my office, people interested in methylation and the like. In humans, we pretty much have access to tissues that people slough off normally with maybe a needle, the help of a needle. You're not going to get liver, you're not going to get brain biopsies on normal free living people and we've got to figure out how to push this field with the tissues that we have access to. The role of obviously in somatic variation, mosaicism is going to be very important so we can detect it with at least today's technologies. Then finally, I think it's finally, one of the things I think we're all struggling with as we move from exomes to genomes is the challenge of annotating the whole genome and I would predict both in humans and the mouse that having large samples of deeply phenotyped individuals is actually going to help us with that annotation. We can look at the role of conserved regions, the role of hypervariable regions such as the olfactory genes and relate them to phenotype, distant regulatory regions, begin to try to map transposable elements in virus insertion sites and look at the role of those in human disease and then begin the task of assigning function to unknown motifs because variation in those motifs, naturally occurring genetic variation in those motifs are leading to phenotypic or disease variation and which I think this one's actually very exciting for the basic science side of me that we have a huge challenge annotating whole genomes today and I think actually having our data resource like this is going to help push the field. So this was a glimpse of what you all sent me. I apologize I did not assign a name to each individual because I would get it wrong but I will circulate this immediately to the whole group and if people can basically edit, add to, comment, you know this is ridiculous Eric come on and then send it back to me. If you do the ridiculous thing just send it back to me and then we'll begin to work this. The final document will need to address what are the potential questions or use case scenarios and okay, comments? I will, yep, great and Stephen and Thomas I'm going to turn it over to you.