| Title: | A method to estimate the contribution of rare coding variants to complex trait heritability |
| Journal: | Nature Communications |
| Published: | 9 Feb 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38336875/ |
| DOI: | https://doi.org/10.1038/s41467-024-45407-8 |
| URL: | https://www.nature.com/articles/s41467-024-45407-8.pdf |
| Citations: | 1 (1 in last 2 years) as of 8 Aug 2024 |
Publication 8760
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Abstract
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.</p>
10 Authors
- Nazia Pathan
- Wei Q. Deng
- Matteo Di Scipio
- Mohammad Khan
- Shihong Mao
- Robert W. Morton
- Ricky Lali
- Marie Pigeyre
- Michael R. Chong
- Guillaume Paré
1 Application
| Application ID | Title |
|---|---|
| 15255 | Identification of the shared biological and sociodemographic factors underlying cardiovascular disease and dementia risk |
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