; This is an empirical project designed to evaluate the joint contributions of rare and common genetic variants to variation in transcript abundance in human peripheral blood samples from three large cohorts in Atlanta, GA. We will deeply sequence up to 20 kilobases of DNA encompassing the regulatory regions of 96 genes from 2,100 individuals, and measure transcript abundance of these genes by nanoscale quantitative RTPCR as well as allele-specific transcription by targeted RNA sequencing. 700 individuals each will be included from a healthy adult cohort (CHDWB), a coronary artery disease cohort, and a pediatric Crohn's disease cohort, all in collaboration with investigators at Emory University. Statistical methods developed in the other projects of this Program will be used to generate a comprehensive picture of the joint contributions of rare and common variants to gene expression variation. Reciprocally, the experimental data generated by this Project will support the refinement of statistical methods for estimation of identity-by-descent, controlling for population structure in rare allele association studies, inferring the existence of genotype-by-environment interactions, and other applications. All three cohorts are clinically well-phenotyped and encompass several important aspects of human diversity, including both genders;the ethnic diversity present in a large American city;pediatric, adult, and aging populations;and including atherosclerotic and inflammatory disease patients. This will be one of the largest genotype association with gene expression studies to date, the first to deliberately address rare variant contributions to regulation of transcript abundance, and incorporates longitudinal measurements to evaluate the robustness of associations in repeated sampling at yearly intervals. Furthermore, the selection of genes related to coronary and pediatric Crohn's disease ensures that we wilt also evaluate the medically relevant contribution of regulatory polymorphism to the development of disease-related gene expression profiles, and their potential utility for predictive health.
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