Heart failure (HF) increases with age markedly and is associated with a 50% 5-year mortality. Abnormalities of cardiac structure and impairment of cardiac function precede the development of HF and underlie HF subtypes. Cardiac structure and function are heritable - genome-wide association studies have identified 57 common variants associated with cardiac structure and function, however, genetic contribution to newer cardiac function measures have not been explored. In addition, few data exist comprehensively characterizing genetic associations (i.e., rare and structural variants) of cardiac structure and function, especially in minorities. Over the past decade, we have investigated the genetic effect on HF and cross-sectional echo measures within the setting of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium. We now propose to extend our effort to the Trans-Omics for Precision Medicine (TOPMed) Program by combing our rich longitudinal phenotypic data with cutting-edge whole genome sequencing data in seven population-bases cohorts. Our central hypothesis is that specific common, rare and structural genetic variants will be associated with cardiac structure and function, as well as HF; and incorporating genetic predisposition to cardiac structure and function alterations will improve HF risk prediction. In Aim 1, we will characterize genetic architecture of cardiac structure and function and their longitudinal changes, and evaluate their effects on HF. To inform understanding of HF physiology, we will assess the causal effects of proteins and metabolites on echo and HF using Mendelian randomization approaches. In Aim 2, we will construct polygenic risk scores for cardiac structure and function and assess their impact on HF risk prediction. We will apply a new approach for PRS construction, and will use a machine learning algorithms to evaluate its prediction on the risk of HF. With the completion of this project, we aim to generate insights into the biological pathways underlying progressive cardiac dysfunction and HF, and to provide targets for drug development.

Abnormalities of cardiac structure and impairment of cardiac function precede the development of heart failure (HF) and underlie HF subtypes. To identify specific genomic variants, captured by whole genome sequencing, associated with cardiac structure and function, as well as HF, will provide insights into the biological pathways underlying progressive cardiac dysfunction and HF and highlight targets for drug development. Furthermore, to incorporate cumulative genetic risk of cardiac structure and function alterations will improve HF risk prediction.