Despite the prevalence of atrial fibrillation (AF) and the burden it places on health care systems, there remains much that is unknown regarding heritable factors influencing its development and progression. In this study, I investigated whole-exome sequencing (WES) data from a cohort of patients presenting with early-onset AF to explore the role that metabolic dysfunction might play in contributing to disease onset. I curated a metabolism-related gene panel and, following in silico prediction of variant pathogenicity, performed gene-level burden testing using reference data from the Genome Aggregation Database (gnomAD) and the human mitochondrial genome database MITOMAP. I further explored genes associating with AF in the UK Biobank data set, and discovered associations with several AF comorbidities including diabetes, hypertension, and stroke.
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Thesis advisor: Tibbits, Glen
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