Epigenetic fine-mapping of cardiometabolic disease loci in the human liver Summary Cardiovascular disease (CVD) is the leading cause of mortality in the world: an estimated 17; 500; 000 people worldwide died from CVD-related illness in 2012. While disease altering therapies such as statins have had a tremendous health impact, many individuals are unresponsive to treatment or go undiagnosed until a fatal event occurs. Moreover, while clinical risk factors and family history are significantly predictive of CVD risk, risk prediction and early clinical intervention must be improved to diminish the lethality of the disease. Scientific studies have uncovered common genetic variation at more than 182 separate genetic loci that contribute to variability in CVD, coronary artery disease (CAD), myocardial infarction (MI) risk, and associated metabolites including blood lipids. However, several critical limitations have restricted the translational impact of these study findings on clinical medicine. Importantly, while we know that temporal, genomic, and cellular context varies dramatically across individuals, current GWAS studies assume a static context across all samples. Indeed, it is precisely this dynamic context that will shed light on how a specific genetic variant impacts molecular traits, which, in turn, modulate disease risk. Furthermore, a primary tissue involved in CVD is the human liver, which has been difficult to deeply phenotype because of the difficulty of acquiring liver samples. In this proposal, the PIs will address these critical limitations by creating a deep molecular phenotype map of 200 human liver biopsy samples, by developing essential statistical tools to predict the genomic regulatory signals in these rich liver data, and by using these predictions to drive experimental validation of regulatory signals through reporter assays and genome editing in order to study the mechanisms of the genetic regulation of CVD risk. In Aim 1, in collaboration with two transplant surgeons at Penn, the PIs pro- pose to build a comprehensive map of the genetic and epigenetic traits of 200 human liver biopsy samples. In Aim 2, the PIs propose to develop statistical methods to identify regulatory genetic variants using paired sample design to share strength across the multiple epigenetic traits. While study data of this type is cur- rently rare, we anticipate substantial growth in studies of this type and broad use of our analytic approaches. In Aim 3, the PIs propose to develop experimental methods to validate the mechanisms by which functional SNPs impact CVD risk. In particular, we will develop massively parallel CRE reporter assays and genome engineering in iPSC derived hepatocytes to characterize the precise mechanism of multiple CVD risk vari- ants. Throughout this proposal, the PIs will develop, evaluate, and make public new analytic tools that take advantage of many-core computing environments, and will make publicly available all of the genetic and epigenetic data generated from the liver samples.

Genome-wide association studies have identified more than two hundred genetic variants that contribute to the risk of cardiovascular disease. However, at the vast majority of these loci, the biological mechanism responsible for these associations is unknown. This work aims to combine deep molecular phenotyping on liver biopsies with novel statistical model development to predict and validate the genetic mechanisms responsible for cardiovas- cular disease risk.