Whole Genome Sequencing to Identify Causal Genetic Variants Influencing CVD Risk

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DESCRIPTION (provided by applicant): Cardiovascular disease (CVD) remains the leading cause of death in the United States. Although CVD risk is heritable, identification of causal genes in risk pathways has been slow. This project focuses on the identification of causal genes that influence variation in susceptibility to CVD by concentrating on genetic dissection of quantitative endophenotypes including carotid wall thickness, lipids, obesity-related phenotypes, blood pressure-related phenotypes, the insulin/glucose axis, inflammatory markers, oxidative stress markers, hemostasis/coagulation factors, and measures of brain white matter hyperintensities that are genetically correlated with CVD risk. We will utilize existing samples/data from a valuable genetic resource, the San Antonio Family Study (SAFS), involving large extended pedigrees of Mexican American individuals. This long- running highly successful project has produced a large number of quantitative trait locus (QTL) localizations of relevance for CVD risk. In this project, we move from QTL localization to causal gene identification. Our approach to CVD-risk gene discovery is comprehensive; we will utilize whole genome sequencing to capture all possible functional variants in 1,957 individuals from 45 large pedigrees. The large pedigrees to be used represent an optimal study design for the detection of rare functional variants. Advanced statistical genetic methods will be employed to identify the likely causal genes/variants in quantitative trait locus (QTL) regions influencing CVD risk. To achieve our objectives, we will (1) localize additional CVD-related QTLs due to rare functional variants using novel pedigree-specific localization methods, (2) obtain whole genome sequence information for 1,957 Mexican American individuals, (3) identify causal genes underlying existing QTLs influencing CVD risk using WGS information, (4) perform agnostic genome-wide direct association scans using non-synonymous coding variants to identify novel rare functional protein-altering variants influencing CVD risk and, (5) use a novel whole genome assay measuring variant-specific functional regulatory potential to permit genome-wide direct association scans using the predicted functional variants to identify novel rare regulatory variants influencing CVD risk. Given the enormous impact of CVD to mortality rates and the economic burden this disease imposes, it is clear that new methods of genomic analysis are necessary to enable the identification of novel genes and pathways involved in disease risk. The results of this project should identify causal genes underlying CVD risk. Identification of the causal genes will obligately generate on the pathways of these genes and will directly identify novel drug targets.