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, 2) prediction of functional impact, particularly for rare, non- coding variants, and 3) comprehensive interpretation frameworks. 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 methods, genomic features, and clinical frameworks. In Aim 1, we will discover novel genomic variation from ~600,000 ethnically-diverse individuals and associate with plasma lipids. We will complementarily use annotation- and data-driven bioinformatic approaches to identify novel genetic regions. In Aim 2, we will examine the role of larger genomic features, called structural variants, in the role of lipids in these individuals. We will develop new methods to characterize their functions and incorporate them into association frameworks for both common and rare structural variants. We will jointly model the monogenic and polygenic components for risk of familial hypercholesterolemia. In Aim 3, we derive a comprehensive whole genome sequence diagnostic test for plasma lipids with a harmonized quantitative framework spanning variant types and their prevalences. In addition to showing their diagnostic yield for lipid disorders beyond rare commonly tested coding variants, we will also demonstrate their clinical utility for coronary heart disease risk prediction beyond standard lipids. Using longitudinal lipids datasets, we will characterize how this whole genome risk model explains lipid burden and variation phenotypes, including while lipid-lowering medicines are prescribed or titrated. This work leverages data being generated within the NHLBI Trans-Omics for Precision Medicine (TOPMed) program, NIH All of Us Research Program, UK Biobank, and others. We have extensive expertise in whole genome sequence analysis, statistical genetics, computational genomics, and cardiovascular medicine. This proposal includes methodological and computational 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 a broad framework for trans-omics analysis to identify causally relevant genomic variants for both research and clinical genetic applications.