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

Nazia Pathan, Wei Q. Deng, Matteo Di Scipio, Mohammad Khan, Shihong Mao, Robert W. Morton, Ricky Lali, Marie Pigeyre, Michael R. Chong and Guillaume Paré ()
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Nazia Pathan: Hamilton Health Sciences and McMaster University
Wei Q. Deng: St. Joseph’s Healthcare Hamilton
Matteo Di Scipio: Hamilton Health Sciences and McMaster University
Mohammad Khan: Hamilton Health Sciences and McMaster University
Shihong Mao: Hamilton Health Sciences and McMaster University
Robert W. Morton: Hamilton Health Sciences and McMaster University
Ricky Lali: Hamilton Health Sciences and McMaster University
Marie Pigeyre: Hamilton Health Sciences and McMaster University
Michael R. Chong: Hamilton Health Sciences and McMaster University
Guillaume Paré: Hamilton Health Sciences and McMaster University

Nature Communications, 2024, vol. 15, issue 1, 1-16

Abstract: Abstract It has been postulated that rare coding variants (RVs; MAF 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.

Date: 2024
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DOI: 10.1038/s41467-024-45407-8

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