Right ventricular (RV) structure and function influence the morbidity and mortality from coronary artery disease (CAD), dilated cardiomyopathy (DCM), pulmonary hypertension and heart failure. Little is known about the genetic basis of RV measurements. Here we perform genome-wide association analyses of four clinically relevant RV phenotypes (RV end-diastolic volume, RV end-systolic volume, RV stroke volume, RV ejection fraction) from cardiovascular magnetic resonance images, using a state-of-the-art deep learning algorithm in 29,506 UK Biobank participants. We identify 25 unique loci associated with at least one RV phenotype at P < 2.27 ×10−8, 17 of which are validated in a combined meta-analysis (n = 41,830). Several candidate genes overlap with Mendelian cardiomyopathy genes and are involved in cardiac muscle contraction and cellular adhesion. The RV polygenic risk scores (PRSs) are associated with DCM and CAD. The findings substantially advance our understanding of the genetic underpinning of RV measurements.
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Summary GWAS statistics are publicly available on the GWAS catalog portal (https://www.ebi.ac.uk/gwas/). The web links for the publicly available datasets used in the study are as follows: ANNOVAR (https://annovar.openbioinformatics.org/en/latest/user-guide/download/), PhenoScanner version 2 (http://www.phenoscanner.medschl.cam.ac.uk), GWAS catalog (https://www.ebi.ac.uk/gwas/docs/file-downloads), GTEx version 7 and version 8 eQTL results (https://gtexportal.org/home/datasets), CADD version 1.6 (https://cadd.gs.washington.edu/download) and RegulomeDB version 2 (https://regulomedb.org/regulome-search). All other data are contained in the article file and its supplementary information or are available upon request.
Publicly available software tools were used to perform genetic analyses. These tools include BOLT, MTAG, LDSC, probABEL, GWAMA, eCaviar, DEPICT, S-MultiXcan (https://github.com/hakyimlab/MetaXcan), MAGMA, FUMA, g:Profiler, g:Profiler and PheWAS R package. The algorithms for CMR image analysis are available in https://github.com/baiwenjia/ukbb_cardiac. For automated CMR image analysis, we used python v3.6 and tensorflow v1.9.0. Manual analysis of CMR studies was performed using cvi42 software (v5.1.1) (https://www.circlecvi.com).
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This research was conducted using the UK Biobank Resource under application 2964. We thank all UK Biobank participants and staff. N.A. recognizes the National Institute for Health Research Integrated Academic Training program, which supports his Academic Clinical Lectureship post, and also acknowledges support from the Wellcome Trust (Research Training Fellowship 203553/Z/16/Z). We acknowledge the British Heart Foundation for funding the manual analysis to create a CMR imaging reference standard for the UK Biobank imaging resource in 5,000 CMR scans (PG/14/89/31194; S.K.P., S.N. and S.E.P.). We also acknowledge support from the ‘SmartHeart’ Engineering and Physical Sciences Research Council program grant (EP/P001009/1; S.E.P.). The Oxford National Institute for Health Research Biomedical Research Centre and the Oxford British Heart Foundation Centre of Research Excellence supported this research (S.K.P. and S.N.). This work was part of the portfolio of translational research of the National Institute for Health Research Biomedical Research Centre at Barts and The London School of Medicine and Dentistry (N.A., P.B.M. and S.E.P.). This project was enabled through access to the Medical Research Council eMedLab Medical Bioinformatics infrastructure, supported by the Medical Research Council (grant MR/L016311/1; S.E.P.). The UK Biobank was established by the Wellcome Trust medical charity, the Medical Research Council, the Department of Health, the Scottish Government and the Northwest Regional Development Agency. It has also received funding from the Welsh Assembly Government and the British Heart Foundation. MESA and the MESA SHARe projects are conducted and supported by the National Heart, Lung, and Blood Institute in collaboration with MESA investigators. Support for MESA is provided by contracts 75N92020D00001, HHSN268201500003I, N01-HC-95159, 75N92020D00005, N01-HC-95160, 75N92020D00002, N01-HC-95161, 75N92020D00003, N01-HC-95162, 75N92020D00006, N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, UL1-TR-000040, UL1-TR-001079 UL1-TR-001420 from NHLBI and NIH. Funding for SHARe genotyping was provided by National Heart, Lung, and Blood Institute contract N02-HL-64278. Genotyping in MESA was performed at Affymetrix (Santa Clara, CA) and the Broad Institute of Harvard and MIT (Boston, MA) using the Affymetrix Genome-Wide Human SNP Array 6.0. The study was also supported in part by the National Center for Advancing Translational Sciences grant UL1TR001881 and National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center grant DK063491 to the Southern California Diabetes Endocrinology Research Center. RV phenotyping in MESA was funded by National Institutes of Health grants K24 HL103844 and R01 HL086719.
S.E.P. acts as a consultant for and is shareholder of Circle Cardiovascular Imaging (Calgary, Alberta, Canada). All other authors have no competing interests.
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Aung, N., Vargas, J.D., Yang, C. et al. Genome-wide association analysis reveals insights into the genetic architecture of right ventricular structure and function. Nat Genet 54, 783–791 (2022). https://doi.org/10.1038/s41588-022-01083-2