In this project, we propose to use an integrative genomics approach to identify potentially functionalregulatory variants in novel candidate genes for cardiovascular disease (CVD) risk. These objectively chosencandidate genes were obtained using large-scale transcriptional profiling of lymphocyte samples from 1,240San Antonio Family Heart Study (SAFHS) participants. Target candidate loci were nominated based on theexistence of significant correlations of quantitative gene expression levels with two major CVD risk factors,HDL-C levels and plasma triglyceride levels.Using our unique family-based resource of quantitative genome-wide transcriptional profiles, we willexamine 50 novel candidate genes that exhibit both strong evidence for c/s-regulation of expression levelsand significant correlations between expression levels and HDL-C or plasma triglyceride levels. Our priorlinkage-based evidence for c/s-acting sequence variation can be exploited as a probabilistic causal anchor tomaximize our chance for finding functional variation within proximal promoters. For each of these objectivelychosen genes, we will (1) resequence approximately two kilobases of putative promoter region in 182founder individuals to identify promoter variants; (2) genotype all detected promoter variation in the 1,240SAFHS samples for which we have transcriptional profiles; (3) test whether promoter sequence variants areassociated with gene expression levels of the appropriate candidate gene; (4) test for associations betweenpromoter sequence variants and CVD-related phenotypes; (5) confirm observed associations in twoindependent samples, and (6) perform molecular functional analysis of the most likely regulatory variantsThe proposed integrative genomics research paradigm to be used in this project should increase thepace of discovery of the constituent genes of human quantitative trait loci (QTLs) influencing major riskfactors for CVD risk. By focusing on genes whose transcripts show evidence for both c/s-regulatory variationand a strong relationship with the focal clinical phenotypes, we should maximize our probability for findingcausal players in the CVD risk cascade.
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