Nature GeneticsGenetic & Genomic MedicinePeer-reviewed journal article
Genome-wide association analysis reveals insights into the molecular aetiology underlying dilated cardiomyopathy
Sean Lee ZhengiD, Albert HenryiD, Douglas Ewan CannieiD, Michael LeeiD, David Miller, Kathryn A. McGurkiD, Isabelle Bond, Xiao Yun XuiD, Hanane G. IssaiD, Catherine FrancisiD, Antonio de MarvaoiD, Pantazis I. TheotokisiD, Rachel J. BuchaniD, Doug SpeediD, Erik AbneriD, Lance Adams, Krishna G. AragamiD, Johan ÄrnlöviD, Anna Axelsson RajaiD, Joshua BackmaniD, John Arun Baksi, Paul J.R. BartoniD, Kiran J. Biddinger, Eric H. BoersmaiD, Jeffrey BrandimartoiD, , Patrick T. EllinoriD
Abstract
Dilated cardiomyopathy (DCM) is a clinical disorder characterised by reduced contractility of the heart muscle that is not explained by coronary artery disease or abnormal haemodynamic loading. Although Mendelian disease is well described, clinical testing yields a genetic cause in a minority of patients. The role of complex inheritance is emerging, however the common genetic architecture is relatively unexplored. To improve our understanding of the genetic basis of DCM, we perform a genome-wide association study (GWAS) meta-analysis comprising 14,255 DCM cases and 1,199,156 controls, and a multi-trait GWAS incorporating correlated cardiac magnetic resonance imaging traits of 36,203 participants. We identify 80 genetic susceptibility loci and prioritize 61 putative effector genes for DCM by synthesizing evidence from 8 gene prioritization strategies. Rare variant association testing identifies genes associated with DCM, including MAP3K7, NEDD4L, and SSPN. Through integration with single-nuclei transcriptomics from 52 end-stage DCM patients and 18 controls, we identify cellular states, biological pathways, and intercellular communications driving DCM pathogenesis. Finally, we demonstrate that a polygenic score predicts DCM in the general population and modulates the penetrance of rare pathogenic and likely pathogenic variants in DCM-causing genes. Our findings may inform the design of novel clinical genetic testing strategies incorporating polygenic background and the genes and pathways identified may inform the development of targeted therapeutics.
- organism: human
- assay: bulk RNA-seq
- research focus: GWAS / association mapping
- research focus: fine-mapping
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 68
- FWCI
- 20.35
- Field percentile
- top 1%
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