Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight adipocyte biology
Year: 2019
Type: article
Abstract: Population-based biobanks with genomic and dense phenotype data provide opportunities for generating effective therapeutic hypotheses and understanding the genomic role in disease predisposition. To characterize latent components of genetic associations, we apply truncated singular value decomposition (DeGAs) to matrices of summary statistics derived from genome-wide association analyses across 2,138 phenotypes measured in 337,199 White British individuals in the UK Biobank study. We systematically identify key components of genetic associations and the contributions of variants, genes, and phenotypes to each component. As an illustration of the utility of the approach to inform downstream experiments, we report putative loss of function variants, rs114285050 (GPR151) and rs150090666 (PDE3B), that substantially contribute to obesity-related traits and experimentally demonstrate the role of these genes in adipocyte biology. Our approach to dissect components of genetic associations across the human phenome will accelerate biomedical hypothesis generation by providing insights on previously unexplored latent structures.
Source: Nature Communications
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $5,200
Funders: Novo Nordisk, Novo Nordisk Fonden, National Institutes of Health, School of Medicine, Stanford University, Armstrong Flight Research Center, Stanford Bio-X, Chemistry, Engineering and Medicine for Human Health, Stanford University, Medical Research Council, National Human Genome Research Institute, National Institute of Diabetes and Digestive and Kidney Diseases, U.S. National Library of Medicine
Awards: Beyond GWAS of insulin resistance: An integrated approach to translate genetic association to function, G4620202387, G5850737185, SOFTWARE FOR LARGE-SCALE INFERENCE OF THE GENETICS OF LIFESTYLE MEASURES, BIOMARKERS, AND COMMON AND RARE DISEASES, Center for Multi- and Trans-ethnic Mapping of Mendelian and Complex Diseases, Characterization of novel insulin resistance genes by gene editing, high-throughput phenotyping and in vivo studies, Biomedical Informatics Training Program at Stanford
OpenAlex ID: W2971431550