TOPMed WGS and Molecular Epidemiology Analyses for Cardiac Hypertrophy Phenotypes

Medical College Of Wisconsin, Milwaukee WI

Investigators

Abstract

PROJECT SUMMARY/ABSTRACT Left ventricular hypertrophy (LVH) represents one of the most potent risk factors for cardiovascular disease (CVD), including ischemic heart disease, chronic heart failure, and cardiovascular death. The risk of LVH is determined in part by genetic factors and results from Genome-wide Association Studies (GWAS) and Whole Exome Sequencing (WES) already identified some distinct variants and genes. Recently, the NHLBI TOPMed program has been generating one of the largest Whole Genome Sequencing (WGS) data sets. Our proposal builds on over 26,000 WGS samples from most of the large CV cohorts with relevant echocardiographic structural phenotypes. We will utilize association results from these extensive datasets as they will provide unprecedented insights into the genetics of LVH and associated phenotypes. The next important challenge for complex disease genetics and genetic epidemiology will be to elucidate the role and function of identified WGS genes. Functional studies fundamentally rely on relevant human disease models, which capture genetic and genomic features and model the polygenic nature of complex disease phenotypes. Human induced pluripotent stem cells (hiPSCs) provide a ‘human in a dish’ platform to study genome function. For this application, we will focus on analyzing the effects of novel WGS gene variants for LVH and associated structural cardiac phenotypes by using hiPSCs for functional molecular epidemiology and network prioritization-based approaches. We expand on our previous work and propose to functionally test and annotate a subset of significant WGS association signals for selected candidate genes and variants using genome and gene editing in hiPSCs and derived cardiomyocytes. Genes will be selected using on a prioritization-based approach to identify high impact variants. We will also further refine and develop approaches for network-based expression data analyses. Subsequently, we will use expression-based network concepts to describe cellular mechanisms and provide functional annotations for specific WGS genes and variants. In combination, our molecular epidemiology approach is an innovative method to the functional analysis of WGS association signals.