 Last but certainly not least of our topics which is genomic medicine and pediatric patients and how can, are you ready to go? Yes, I'm good to go. Can you hear me? Yes, we can. Okay, great. So this presentation is sort of focused on obstacles but I am going to sort of address a few of the successes as well. So if I have next slide. And there are really three areas that I was going to focus on sort of collectively for the pediatric sites, review of current pediatric projects and largely focus there on phenotyping and sequencing. So Kyle Brothers has worked on the consent form but there is a separate session on that so I wasn't going to go into that in any detail. And then talk about new approaches to analyzing existing data and prospective directions where we felt that an inexpensive custom-based genotyping chip sort of focusing on functional variants or obstructive functional variants would be of interest. Next one. And go on. So this is just addressing some of the algorithms that we have been conducting across the pediatric sites at Boston Centenary and CHOP and we have basically cross-validated several between pediatric sites and across pediatric and adult sites and we have validated adult algorithms as well. I apologize for using CAC, this stands for the Center for Applied Genomics, but I really should say CHOP there. And we still have a few that are in development but both atopic dermatitis and ADHD have been validated sort of on our end. We have tested them on other sites and they were felt to exclude too many cases so we are sort of adjusting the algorithm before we take it forward. Next one. And this is just a representative example of the asthma algorithm which we were quite conservative so we for example here have asthma diagnosis in our epic database with about 15,000 cases but restricted it down to about 4,500 so that also impacts on the other site but these are really carefully confirmed and documented asthma cases and this is now under analysis as are many of the other phenotype algorithms that I listed earlier. Next. And on the adult side we have sort of done what we can to address some of the adult phenotypes but this is sort of one of the obstacles that we have. We don't have a lot of pediatric patients who have deep thrombosis and or de-vesticulosis or SOSTA for obvious reasons. So if you take a next slide and for those obvious reasons I mean the pediatric numbers are obviously going to dwarf sort of in the context of the adult several of the adult phenotypes but on the other hand many of the and multiple of the algorithms we are taking forward in pediatrics as I showed you they cross pediatric and adult fields so we can obviously address phenotypes longitudinally from pediatric and up to old ages. Next one. So sort of an obstacle point in that setting is the fact that many of the adult algorithms as I said they don't really apply well in pediatrics and vice versa when we look at developmental traits and pediatrics, language development, cognition or motor skills I mean there are not going to be big numbers from the adult front to address that either but you know the goal here of course is to try to optimize and bridge this to the best we can. So next slide. And the options that we have in that setting is to sort of you know proceed as we are doing on a case-by-case basis and not really necessarily worry about the fact that there are obviously going to be diseases that have no overlap such as Alzheimer's disease and dementia and other things in the adult front and the developmental phenotype as I mentioned in pediatrics. But then you know what we have though accomplished is the sort of cross validation of several of the phenotypes algorithms that we have come up with in pediatrics and validated in adult sites and vice versa so that's actually very nice to see. Next slide. And as a part of the sequencing program I mean obviously some of the gene variants are targeted there are much more applicable towards the adult diseases. We have now done preliminary analysis of the first about 280 or so samples of the sequence and we have another 140 going through about half of them through and it's actually quite interesting that there's a lot of novel variants absent in all databases that actually have come up from the panel and this is in the middle of an analysis and we can talk about that in the meeting tomorrow or Friday. Next one. And this sort of is the focus on the approaches to analyze assisting data, the new methods. Next slide. And as I mentioned briefly earlier copy number variation is obviously another whole really domain that can be leveraged across all the sites. We all have SNP data where we can derive local ratio of the allele frequency and we have an algorithm and you may also have algorithm of other sites that we can work on optimizing but same algorithm should be utilized across all the sites and then the data sort of meta-analyzed another sort of potential way of moving forward is to impute loss of function variants and drug gene interaction variants and I will show you an example of that. And then of course the CMV analysis across sequencing data which we have developed and many others as well is progressing nicely and sort of getting to the states of the CMV analysis and picking up obviously much, much smaller CMVs than we could with the arrays. Now I mentioned here also the high sensitivity GWAS, this is really an algorithm that was implemented in the R packets of assets and sort of focuses on subset analysis and we have applied this for example across multiple different autoimmune diseases and we have enriched our genome-wide significant finding multi-multi-fold by using this algorithm. It really picks for each SNP or for each locus the most optimal disease model and then it basically sort of from a common control analysis drives the most informative analysis from a subset standpoint. It's actually quite powerful way of enriching for significance and we definitely lever it. And then there are various functional biological annotations that obviously can be applied to optimize sort of genome-wide marginally significant hits, et cetera. Gene-based association testing obviously cuts down the multiple testing issues and various tissue-specific and cell-specific assays that can effectively be integrated into GWAS data are actually often very informative or pinpointing specific cells that drive sort of the significant signals across related diseases, et cetera. And then pathway and protein interaction analysis have been mentioned before and here's some of the newer tools that are used. Next one. And for the copy number variants, I mean obviously as you mentioned these are, this is really an untapped resource within eMERS across all the sites and with the 56,000 samples that sit genotyped, I mean this could be very, very valuable approach. And obviously these variants are very common even though the interest is more on the rare variant front and that's really what the Illumina platform is optimized to pick up. Next one. And this is just sort of the schematic of the different approaches depending on the array platform or sequencing platform you have and sort of self-explanatory that obviously the array data where you have both the allele frequency and the intensity data in my view at least is better powered than any of the other methods that rely on intensity alone. Next one. 11 minutes now, so it would be good to wrap up. Okay. So the opportunity exists here to basically, you know, genome-wide this across the sites and do a meta-analysis. Next one. So the pathogenicity and the database of the DV which is the genome variation, I mean this is obviously not optimal and so we could do a much better job I think in the next slide by figuring out sort of, you know, the proper control and the proper way of doing this. Next one. And this is just to sort of demonstrate to the example of the haplotype imputation for the TPMT next one and shows you can just roll these slides. These are the four variants that we used in PUDE 2, 87,000 samples. Next one. And this shows that, you know, there is actually, you know, ethnic difference in the prevalence of these variants. Homosagatity state is obviously much more rare but heterosagatity which is still influential is significant. Next one. And this summarizes the data set in terms of the individuals we picked out and these are rare variants so, and if you take the next slide, it shows the sort of the accuracy from the variants that are typed which is 99.8% to that of the imputation. And the Fanger sequence actually a full plate of 94 samples. Next slide. And you can see better the accuracy there that, you know, for the homosagatity state, I mean it's not perfect. There were a few individuals predicted to be heterocycles and one missed from each of the standpoint. Next slide. I think we're coming to the end here. So the accuracy here of imputation is obviously not perfect but still you capture the vast majority of the pace in the next slide. For the prospective sort of direction, next slide, what we were going to propose was sort of this cost-effective, inexpensive, custom-based phenotyping, next slide. And we have, you know, done this for a couple of projects before, proposed it for a third project that may or may not go. Next slide. And this is the sort of the contents of the organ transplant chip which is basically taking all fugitive, damaging variants, all capin under variants, g-mass loci and sort of content that is available from the public domain and other sites where we could access data and typing across 25,000 to 30,000 samples. And so this would be sort of the proposal for a future e-mers chip in that setting which is obviously allow us to integrate the, you know, every single samples by typing them on the same chip with informative content of data set that sort of can go as low as 0.1 percent frequency as was discussed before. And the last slide sort of, again, just sort of allows for this sort of coordinated effort to take place, so I'm all stuck here, thanks.