A method to estimate the contribution of rare coding variants to complex trait heritability

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作者
Nazia Pathan
Wei Q. Deng
Matteo Di Scipio
Mohammad Khan
Shihong Mao
Robert W. Morton
Ricky Lali
Marie Pigeyre
Michael R. Chong
Guillaume Paré
机构
[1] Hamilton Health Sciences and McMaster University,Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute
[2] Michael G. DeGroote School of Medicine,Department of Pathology and Molecular Medicine, McMaster University
[3] St. Joseph’s Healthcare Hamilton,Peter Boris Centre for Addictions Research
[4] McMaster University,Department of Psychiatry and Behavioural Neurosciences
[5] McMaster University,Department of Medicine, Faculty of Health Sciences
[6] McMaster University,Department of Health Research Methods, Evidence, and Impact
[7] Vascular and Stroke Research Institute,Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac
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摘要
It has been postulated that rare coding variants (RVs; MAF < 0.01) contribute to the “missing” heritability of complex traits. We developed a framework, the Rare variant heritability (RARity) estimator, to assess RV heritability (h2RV) without assuming a particular genetic architecture. We applied RARity to 31 complex traits in the UK Biobank (n = 167,348) and showed that gene-level RV aggregation suffers from 79% (95% CI: 68-93%) loss of h2RV. Using unaggregated variants, 27 traits had h2RV > 5%, with height having the highest h2RV at 21.9% (95% CI: 19.0-24.8%). The total heritability, including common and rare variants, recovered pedigree-based estimates for 11 traits. RARity can estimate gene-level h2RV, enabling the assessment of gene-level characteristics and revealing 11, previously unreported, gene-phenotype relationships. Finally, we demonstrated that in silico pathogenicity prediction (variant-level) and gene-level annotations do not generally enrich for RVs that over-contribute to complex trait variance, and thus, innovative methods are needed to predict RV functionality.
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