Back

Multi-ancestry Multivariate Genome-Wide Analysis Highlights the Role of Common Genetic Variation in Cardiac Structure, Function, and Heart Failure-related Traits

Levin, M. G.; Tsao, N. L.; Bellomo, T. R.; Bone, W. P.; Aragam, K. G.; Yang, Y.; Morley, M. P.; Burke, M.; Judy, R. L.; Arany, Z.; Cappola, T. P.; Day, S. M.; Ellinor, P.; Margulies, K. B.; Voight, B. F.; Damrauer, S. M.

2021-08-05 cardiovascular medicine
10.1101/2021.08.03.21261508 medRxiv
Show abstract

Heart failure (HF) is a leading cause of cardiovascular morbidity and mortality, yet the contribution of common genetic variation to HF risk has not been fully elucidated, particularly in comparison to other common cardiometabolic traits. We conducted a multi-ancestry genome-wide association study (GWAS) meta-analysis of all-cause HF including up to 56,722 HF cases and 1,133,054 controls, identifying 4 novel loci. We then performed a multi-ancestry multivariate association study of HF and related cardiac imaging endophenotypes, identifying 71 conditionally-independent variants, including 16 novel loci. Secondary colocalization and transcriptome-wide association analyses identified known and novel candidate cardiomyopathy genes, which were validated in gene-expression profiling of failing and healthy human hearts. Colocalization, gene expression profiling, and Mendelian randomization provided convergent evidence for the roles of BCKDHA and circulating branch-chain amino acids in heart failure and cardiac structure. Finally, proteome-wide Mendelian randomization revealed 11 circulating proteins associated with HF or quantitative imaging traits. These analyses highlight similarities and differences among heart failure and associated cardiovascular imaging endophenotypes, implicate novel common genetic variation in the pathogenesis of HF, and identify circulating proteins that may represent novel cardiomyopathy treatment targets.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.