PROJECT SUMMARY / ABSTRACT Coronary heart disease is the leading cause of death worldwide. Characterizing the inherited basis of plasma lipids, the strongest risk factor for coronary heart disease, has led to key biological and clinical insights. Large- scale deep-coverage whole genome sequencing is now feasible and offers the opportunity to characterize full genomic variation within a given individual. For any one individual, however, the interpretation of genomic variation is limited by 1) ethnic-specific impacts, and 2) prediction of functional impact, particularly for rare, non-coding variants. The goal of this R01 proposal is to fully characterize the inherited basis of plasma lipids through a novel `trans-omics' approach – complementing whole genome sequencing with novel statistical genetics and functional genomics. In Aim 1, we will discover novel genomic variation from ~100,000 ethnically- diverse individuals and associate with plasma lipids. We will complementarily use data-driven bioinformatic approaches to identify novel genetic regions. In Aim 2, we will improve the genomic diagnosis of familial hypercholesterolemia, characterized by severe hypercholesterolemia and marked increased risk for premature coronary heart disease. We will incorporate ethnicity, functional annotations, and pleiotropy to develop a novel polygenic model for familial hypercholesterolemia. We will jointly model the monogenic and polygenic components for risk of familial hypercholesterolemia. In Aim 3, we identify functional rare non-coding hypercholesterolemia variants with cell-based massively parallel reporter assays and CRISPR-based methods. We will further use these insights to improve power for discovering novel genes from whole genome sequence analysis. This work leverages data being generated within the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. We have extensive expertise in whole genome sequence analysis, statistical genetics, functional genomics, and cardiovascular medicine. This proposal includes methodological, computational, and experimental innovations, and builds on established collaborative relationships between investigators with complementary strengths. Completion of our aims will yield novel insights to inform prevention, diagnosis, and treatments for coronary heart disease. Furthermore, we will demonstrate broad framework for trans-omics analysis to identify causally relevant genomic variants for both research and clinical genetic applications.