Individualized Risk Stratification Using the Genomic Contribution

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DESCRIPTION (provided by applicant): Coronary heart disease is the leading cause of death in the United States with atherosclerosis of the coronary arteries being the major underlying etiology. Patients with atherosclerotic coronary artery disease (CAD) are significantly more likely to suffer a myocardial infarction or sudden cardiac death. As such, CAD patients receive the maximum preventive therapies, and their clinicians maintain a low threshold for imaging and invasive vascular procedures. Clearly, knowledge of a patient's CAD burden has a major impact on clinical care, but it is not practical to definitively diagnose CAD on a population scale. We are proposing to use genetic information in conjunction with traditional risk factors, as a means of identifying individuals at increased risk of having CAD who could then receive aggressive risk reduction and/or undergo definitive coronary imaging. To identify genetic risk factors for anatomic CAD, we are engaged in a Genome Wide Association Study (GWAS) of 2000 cardiac catheterization patients using the Affymetrix SNP array 6.0 platform. We are currently accruing a second validation cohort of 2000 patients, in which we will genotype the top association signals from our GWAS. The combined datasets will be used to prioritize genes for sequencing to identify putative functional variants. Any functional or validated risk loci will be analyzed for their utility in risk classification. Furthermore, the diversity of our patient population will allow us to identify CAD gene variants that are common and unique to African American, Caucasian and Hispanic subjects. To our knowledge, this is the first GWAS specifically for anatomic CAD in the context of significant racial and ethnic diversity. PUBLIC HEALTH RELEVANCE: This project will detect genetic risk factors for anatomic coronary artery disease by using genome-wide genotype data and next-generation sequencing. We will use the genetic risk factors in conjunction with traditional cardiovascular risk factors to improve artery disease risk classification in a clinical setting.