Genome-wide association and multi-trait analyses characterize the common genetic architecture of heart failure
Year: 2022
Type: article
Study design: Observational Study
Abstract: Heart failure is a leading cause of cardiovascular morbidity and mortality. However, the contribution of common genetic variation to heart failure risk has not been fully elucidated, particularly in comparison to other common cardiometabolic traits. We report a multi-ancestry genome-wide association study meta-analysis of all-cause heart failure including up to 115,150 cases and 1,550,331 controls of diverse genetic ancestry, identifying 47 risk loci. We also perform multivariate genome-wide association studies that integrate heart failure with related cardiac magnetic resonance imaging endophenotypes, identifying 61 risk loci. Gene-prioritization analyses including colocalization and transcriptome-wide association studies identify known and previously unreported candidate cardiomyopathy genes and cellular processes, which we validate in gene-expression profiling of failing and healthy human hearts. Colocalization, gene expression profiling, and Mendelian randomization provide convergent evidence for the roles of BCKDHA and circulating branch-chain amino acids in heart failure and cardiac structure. Finally, proteome-wide Mendelian randomization identifies 9 circulating proteins associated with heart failure or quantitative imaging traits. These analyses highlight similarities and differences among heart failure and associated cardiovascular imaging endophenotypes, implicate common genetic variation in the pathogenesis of heart failure, and identify circulating proteins that may represent cardiomyopathy treatment targets.
Source: Nature Communications
Authors: Michael G. Levin, Noah L. Tsao, Pankhuri Singhal, Chang Liu, Ha My T. Vy, Ishan Paranjpe, Joshua D. Backman, Tiffany R. Bellomo, William P. Bone, Kiran J. Biddinger, Qin Hui, Ozan Dikilitas, Benjamin A. Satterfield, Yifan Yang, Michael P. Morley, Yuki Bradford, Megan Burke, Nosheen Reza, Brian Charest, Renae L. Judy, Megan J. Puckelwartz, Hakon Hakonarson, Atlas Khan, Leah C. Kottyan, Iftikhar Kullo, Yuan Luo, Elizabeth M. McNally, Laura J. Rasmussen-Torvik, Sharlene M. Day, Ron Do, Lawrence S. Phillips, Patrick T. Ellinor, Girish N. Nadkarni, Marylyn D. Ritchie, Zoltan Arany, Thomas P. Cappola, Kenneth B. Margulies, Krishna G. Aragam, Christopher M. Haggerty, Jacob Joseph, Yan V. Sun, Benjamin F. Voight, Scott M. Damrauer
Institutions: Philadelphia VA Medical Center, University of Pennsylvania, Emory University, Icahn School of Medicine at Mount Sinai, Stanford Medicine, Stanford University, Regeneron (United States), Broad Institute, Harvard University, Massachusetts General Hospital, Atlanta VA Health Care System, Mayo Clinic, VA Boston Healthcare System, Northwestern University, Children's Hospital of Philadelphia, Columbia University, Cincinnati Children's Hospital Medical Center, Geisinger Health System, Brigham and Women's Hospital
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $5,890
Funders: U.S. Department of Health and Human Services, U.S. Department of Veterans Affairs, American Heart Association, Office of Research and Development, University of Pennsylvania, Institute for Translational Medicine and Therapeutics, Regeneron Pharmaceuticals, Fondation Leducq, National Institutes of Health, Perelman School of Medicine, University of Pennsylvania, National Institute on Aging, National Heart, Lung, and Blood Institute, National Human Genome Research Institute, National Institute of Diabetes and Digestive and Kidney Diseases, National Center for Advancing Translational Sciences
Awards: G104208513, G1240806148, G1300756762, G1335139853, G1554891411, G1617165148, G1852267882, G1900021819, G2464355595, G2770651626, G3033820005, G3041263631, G3348201328, G3385113560, G3422964195, G3457235877, G3721240354, G388764848, Big Omics Data Engine 2 Supercomputer, G4672149634, G4744512369, Northwestern University Clinical and Translational Science Institute (NUCATS), G478760277, Algorithms to identify non-coding mutational burden and disease-relevant pathways, G5789886938, G5946272055, G6353510222, G6862384808, Cardiomyopathy Genomes Project, G7396908598, Functional Interrogation of T2D-associated genes in human stem cell-derived models and mice, G7900632534, Integrative genomics of human heart failure, COVID and Translational Science supercomputer (CATS), G815100389, G8271222709, G9000014044, G9000171907, G9000180154, G9000230873, Chicago Center for Diabetes Translation Research, NRSA Training Core, CHIcago Center for Accelerating nextGen Omics, deep phenotyping, and data science in Heart Failure (CHICAGO-HF), MTSS1 in myocardial disease, Institutional Career Development Core
OpenAlex ID: W4308976268