The goal of this R01 proposal is to uncover biological mechanisms underlying association at known CAD and MI loci to provide insight into treatment and prevention of cardiovascular disease. Our research team combines strengths in cardiovascular disease, high-throughput genetics and genomics and development and application of innovative computational and statistical methods and genomics technology to maximize the benefits of genetic studies. Building on our previous work where we identified genomic regions associated with CAD and MI, we propose to uncover the mechanisms underlying association at CAD and MI loci using genetics and epigenomics. In the previous funding period, we performed whole genome sequencing of 2,202 early-onset MI cases and healthy controls. We also newly genotyped ~100,000 samples with a GWAS array with enrichment for coding variants.
In Aim 1, we propose to assess the phenotypic impact of the ~19 million variants and 20k indels and SVs identified from HUNT sequenced samples by imputing them into 100,000 new GWAS samples with many cardiovascular phenotypes. We will perform integrated analyses with epigenomics data to highlight clusters of loci with related function.
In Aim 2, we will perform targeted sequencing of 200 genes in 13,400 multi-ethnic MI and CAD cases and controls to search for high effect genetic variants that implicate CAD genes.
In Aim 3, we will experimentally determine the functional variants and the mechanisms of action at 6-8 loci by introducing genetic changes into cell lines, iPS cells or animal models. Completion of these aims will provide new insights into disease mechanisms that have the potential to catalyze breakthroughs in cardiovascular disease prevention, treatment, and diagnosis.
Coronary disease is the leading cause of death in the United States. Improved understanding of which genes and genetic changes impact risk of MI and CAD may provide insights into understanding disease etiology, support identification of novel drugs and therapies, and enable better targeting of preventive and therapeutic approaches. We propose to hone in on functional genes at newly identified genetic regions using statistical genetics, bioinformatics and experimental models.
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