Two major developments in the field of Genomics inform the aims of this proposal. Recent large-scale efforts using DNA biobanks linked to electronic health records (EHR) have demonstrated a cost-effective and rapid approach to human genetic discovery with the enormous potential to uncover trait-associated loci hitherto inaccessible because of inadequate power or difficult-to-procure phenotypes. Meanwhile, Functional Genomics is generating large catalogs of functional elements in the genome using a broad spectrum of molecular assays in diverse tissues, cell types, or conditions. This proposal will develop an integrative methodology that advances our understanding of the physiological mechanisms through which genetic variation influences disease risk or quantitative trait. Building on existing research collaborations, this work will develop a set of analytic approaches and computational tools for the analysis of the human medical phenome and present a novel framework for the study of gene function, using large-scale biobanks linked to extensive EHR data (BioVU, UK Biobank, and All of Us) and the enormous breadth of functional genomics data (from GTEx and other consortia) that are being produced. We will develop a new Phenome-Wide Association Study (PheWAS) methodology, an approach to discovery and replication with enhanced capabilities for proposing relevant mechanisms.
High-throughput biological interpretation remains a critical need in the analysis of genetic discoveries that have emerged from Genome-Wide Association Studies (GWAS) while the recent growth in the number of studies that are being conducted in large-scale DNA biobanks with extensive electronic medical records is likely to lead to a dramatic expansion in the number of genetic discoveries. Concurrently, the field of Functional Genomics is generating comprehensive catalogs of functional elements in the human genome, using a range of molecular assays and high-throughput technologies. This proposal will devise approaches to bridging the gap between Functional Genomics and Genomic Epidemiology through the development of statistical methods and computational tools and has the potential to uncover novel molecular processes contributing to human disease.