Wednesday, February 5, 2025
2/5/2025
PROJECT SUMMARY/ABSTRACT: Coronary artery disease (CAD) remains the leading cause of death globally and the identification of individuals at high risk to target early prevention strategies is a major public health need. Commonly used clinical risk estimators are validated for limited age and ancestry groups and are poorly calibrated to contemporary populations, limiting their utility. Polygenic scores (PGS) – which quantify inherited risk by summing information from many common sites of DNA variation – hold considerable promise for improving upon available clinical risk estimators, however available PGS for CAD currently lack proper actionability due to limitations of input data, score methodology, and clinical applications. More comprehensive and equitable tools incorporating genetic risk are needed to predict risk earlier in life to enable preventive interventions and reduce morbidity and mortality of CAD. Dr. Patel proposes to develop more generalizable PGS for CAD using multi-ancestry genomic data; to construct clinically interpretable, absolute CAD risk prediction models integrating genetic and non-genetic factors; and to investigate new clinical indications for PGS use utilizing clinical trial data. In Aim 1, Dr. Patel will develop new PGS for CAD optimized for individuals of diverse ancestries by harnessing the principle of burrowing information from functional genomic annotations, cross-ancestry correlation, and cross-trait correlation to refine the effect estimates of genetic variants included in these scores. In Aim 2, Dr. Patel will integrate polygenic risk with clinical risk factors to develop a absolute risk prediction models for CAD, which he will then calibrate, validate, and deploy in external datasets. In Aim 3, Dr. Patel will define clinical use indications for PGS using secondary analyses of CAD prevention clinical trials within the context of individuals with inflammatory mediators including HIV and elevated C-reactive protein. The overall goal for this application is to advance the actionability of PGS by addressing their limitations in equity, interpretability, and indication. Upon successful completion of these aims, Dr. Patel expects to deliver better-performing and cross-ancestry portable CAD PGS, share externally validated, integrated absolute risk prediction models, report utility for PGS use in specific sub-populations for guiding therapies, and nominate inflammatory mechanisms to target in future trials. These endpoints will collectively advance the actionability of PGS models for CAD, moving closer to their clinical deployment for disease prevention. This research will be accomplished in the setting of a comprehensive career development program designed to provide Dr. Patel with the skills needed to become an independent physician-scientist in cardiovascular genomics. This proposal brings together a unique interdisciplinary advisory team of experts in the fields of epidemiology, genomics, statistics, and clinical trials research that will guide Dr. Patel in his transition to scientific independence.
HHS’ Tracking Accountability in Government Grants System (TAGGS) website provides access to detailed descriptions of grants, loans, aggregated direct payments and other types of financial assistance awarded by the HHS Operating Divisions (OPDIVs) and the Office of the Secretary Staff Divisions (STAFFDIVs).