Integrated omics analysis of clonal hematopoiesis and cardiovascular disease risk in TOPMed

PROJECT SUMMARY Clonal hematopoiesis of indeterminate potential (CHIP) involves the gain of somatic mutations that confer a selective advantage to hematopoietic stem cells and lead to the expansion of a clonal population of blood cells. CHIP is common in older individuals and is associated with increased risk of blood cancers and atherosclerotic cardiovascular disease (ASCVD). Genes commonly mutated in CHIP include known regulators of DNA methylation (DNAm) and RNA splicing. In this application, we propose to take advantage of a novel omics data resource being generated by the TOPMed program to investigate the roles of DNAm, gene expression, and differential splicing as mechanisms potentially mediating the relationship between CHIP and risk for ASCVD. In Aim 1 we will use genome-wide DNAm and RNA-seq data collected from blood samples of tens of thousands of individuals to investigate whether methylomic and transcriptomic patterns differ in individuals with CHIP. We will consider individual genes and subsets of the genes most commonly mutated in CHIP separately, since each of these genes is likely to have different functional consequences given their different regulatory roles. In Aim 2, we will test for the presence of CHIP-associated methylomic and transcriptomic differences that are driven by specific immune cell types. In Aim 3, we will use Mendelian randomization to identify CpG sites or genes that may causally mediate the relationship between CHIP mutations and ASCVD risk. In Aim 4, we will integrate the multiple omics data in a biologically informed deep learning model to investigate how CHIP mutations and methylomic and transcriptomic changes may collectively contribute to risk for ASCVD and identify key biological processes through which these changes influence disease risk. By characterizing the epigenomic and transcriptomic consequences of CHIP mutations in specific genes and within specific cell types and examining the potential of TOPMed trans-omic data to predict disease risk, this work will advance current understanding of CHIP and risk for ASCVD.