STATISTICAL METHODS FOR GENOMIC DISSECTION OF CARDIOVASCULAR DISEASES

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DESCRIPTION (provided by applicant): Statistical Methods for Genomic Dissection of Cardiovascular Diseases Abstract This mentored career development grant application proposes a training program to integrate Dr. Sung's previous research in statistical genetics into cardiovascular disease (CVD). Her long-term career goal is to establish herself as an independent statistician in CVD genetics research so that she can more effectively participate in multi-disciplinary research programs with clinical and translational CVD researchers and be better equipped to develop and apply statistical methods to contribute more meaningfully to the field of CVD genetics. This will be achieved through building a strong foundation in the clinical and research aspects of CVD and enhancing her understanding of genomics and whole-genome sequence data. Dr. Sung's mentoring team consists of multi-disciplinary researchers with a strong research track record. Complex cardiometabolic traits including hypertension, dyslipidemia, and diabetes contribute to CVD, the leading cause of mortality and morbidity in the industrialized world. Genome-wide association studies (GWAS) have led to many exciting discoveries. However, most GWAS discoveries have not been translated to clinical care because the functional mechanisms underlying the genetic associations remain elusive and the roles played by the environment in modulating these associations remain poorly defined. The research objective of this K25 application is to decipher the genetic and environmental architecture of cardiometabolic traits by incorporating GxE interactions and regulatory annotation information. We hypothesize that joint analysis of the environment, regulatory variants and coding variants will enhance the discovery of putative genetic variants and the functional mechanisms underlying cardiometabolic traits. To evaluate this hypothesis, we aim to identify genetic variants involving GxE interactions and identify putative functional variants by incorporating ENCODE regulation information.