Project summary Nearly one in three people will have an arrythmia during their lifetime and up to 10% may die of sudden cardiac death. Arrhythmias and sudden cardiac death are heritable and often caused by problems of cardiac conduction or repolarization. Nevertheless, the genetic causes are not well-defined. Moreover, interpretation of genetic variation is limited by the frequent discovery of variants of uncertain clinical significance. Our preliminary data suggest that using high-depth genomic sequencing data from large-scale biobanks with routinely measured electrocardiogram intervals – indicators of arrhythmia and sudden cardiac death risk – has the potential to address these challenges. Our overall goal is to minimize morbidity from cardiovascular disease. The specific objective of this proposal is to utilize high-depth whole genome and exome sequencing data to identify, functionally evaluate, and clinically characterize genetic variation that predisposes to arrhythmia risk and sudden cardiac death. To do so, we will leverage a unique and massive repository of individuals with genomic sequencing, electrocardiograms, and clinical data. The electrocardiogram is a widely utilized and inexpensive screening test. Standard electrocardiogram intervals are reliable and reproducible measurements that are associated with a variety of cardiac conditions, most notably arrhythmias and sudden cardiac death. Our overall hypothesis is that functional and clinically relevant rare genetic variation underlies population-based electrocardiographic interval variability. In Aim 1, we will identify rare coding variation associated with electrocardiographic intervals. We will use data from a unique resource of over 220,000 individuals with electrocardiograms and whole genome or exome sequence data in the National Heart Lung and Blood Institute’s Trans-Omics for Precision Medicine Program, UK Biobank, Geisinger MyCode/DiscovEHR cohort, and Mass General Brigham HealthCare Biobank. In Aim 2, we will validate and characterize the electrophysiological and structural impact of identified genes in stem cell derived cardiomyocytes. In Aim 3 we will assess whether variants with large electrocardiographic trait effect sizes are associated with arrhythmia risk using electronic health record data in nearly 400,000 sequenced individuals, and variant pathogenicity using ClinVar, a repository of clinical variant adjudications. Studying how rare genetic variants affect the electrocardiogram is an innovative approach for understanding arrhythmia and sudden cardiac death risk. We anticipate that this paradigm will be broadly applicable to other quantitative endophenotypes and heart diseases. We submit that our aims are consistent with the NHLBI’s mission of understanding the causes of disease and enabling translation of basic discoveries into clinical practice.