Recent large scale GWAS, including ours in the NHLBI Family Heart Study (FamHS), have identified and validated many loci for heart disease phenotypes at GW levels of significance. Yet the importance of these loci remains uncertain as nearly all appear to explain minimal amounts of the variance for the traits studied. A number of large scale exome sequencing projects are now being conducted on case-control cohorts to ad- dress whether rare coding variants may be behind this "missing heritability". But so far, signals have been difficult to distinguish from noise, partly due to large number of evolutionarily recent variants found in human populations (e.g. Coventry et al., 2010). For such nearly private, lineage-specific mutations, unrelated case- control studies result in a large number of singleton variants with low individual power, and which collectively pose a challenge to burden testing. The recently developed exome-chip addresses part of this problem by focusing on the "not so rare" exonic variants (seen in multiple unrelated subjects), but neglects the evolutionary recent lineage-specific exonic variants which the Fisher-Wright model predicts would have the greatest penetrance effects. More importantly, the exclusive focus on exonic variation in both case-control sequencing and exome chip studies ignores regulatory variants, which may be most important to many of the quantitative endophenotypes of cardiovascular disease (e.g. serum lipids, cytokines, obesity, CAC). To address these research gaps, we propose using the large, well characterized family study, FamHS, as a platform for Whole Exome Sequencing (WES), plus Targeted Regulatory Sequencing (TRS) for variants associated with cardiovascular disease, atherosclerosis and associated endophenotypes. The FamHS represents the ideal resource for the proposed studies, having a unique combination of features which no other NHLBI cohort possesses. Results from GAW 17 show (Wilson and Ziegler, 2011), and our own simulations confirm, that family studies have greater power to detect near-private, lineage-specific rare variants than studies of unrelated subjects, allowing us greater ability to detect novel associated exonic variants than the current case- control WES and exome chip studies. Further, our FamHS pedigrees continue to show provocatively strong, independently replicated linkage evidence for a variety of cardiovascular traits, unexplained by GWAS SNPs, suggesting that these particular regions may contain rare coding and/or regulatory variants. We propose a two-stage, WES+TRS experiment on all N=5,763 European-Americans (EAs) (in 1,253 families) from the extensively phenotyped FamHS cohort. In Stage 1, we will obtain WES information for 3,389 FamHS EAs comprising the largest 491 pedigrees (mean family size=6.9), to scan for novel rare coding/regulatory variants for CHD, atherosclerosis, and their risk factor phenotypes, such as obesity, hypertension, dyslipidemia, diabetes, insulin resistance, and inflammation. In Stage 2, we will validate these regions by sequencing all implicated loci in the remaining independent FamHS subjects (N=2,374 EAs in 660 families). We will also validate these findings in the N=622 (F=221 families) African-Americans in FamHS by conducting parallel WES+TRS.
We will sequence the exomes (those parts of the human genome that encode for all proteins, the basic building blocks of life) in large numbers of human pedigrees. We will also sequence known functional regulatory regions (the parts of the human genome that regulate protein production, which ultimately drives creation of different cell types as well as normal operation of cells) in selected regions for which we have suggestive evidence that these particular pedigrees might be harboring one or more genetic variants for cardiovascular disease or one of its key risk factors through genetic linkage (the co-inheritance of nearby gene regions from parents to children). We will analyze these genetic variants against the already collected extensive medical information on these subjects, so that we can find those pieces of the genome that may be causative of heart disease and related traits and conditions, such as high cholesterol, high blood pressure, obesity, diabetes, inflammation, atherosclerosis, etc. If we succeed, this knowledge could lead to new diagnostic tools for disease, and point the way to new understanding of biology, which could lead to new personalized medicine therapies.