| Title: | Estimation of non-additive genetic variance in human complex traits from a large sample of unrelated individuals |
| Journal: | American Journal of Human Genetics |
| Published: | 2 Apr 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/33811805/ |
| DOI: | https://doi.org/10.1016/j.ajhg.2021.02.014 |
| URL: | http://www.cell.com/article/S0002929721000562/pdf |
| Citations: | 82 (43 in last 2 years) as of 8 Aug 2024 |
Publication 4793
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Abstract
Non-additive genetic variance for complex traits is traditionally estimated from data on relatives. It is notoriously difficult to estimate without bias in non-laboratory species, including humans, because of possible confounding with environmental covariance among relatives. In principle, non-additive variance attributable to common DNA variants can be estimated from a random sample of unrelated individuals with genome-wide SNP data. Here, we jointly estimate the proportion of variance explained by additive (hSNP2), dominance (δSNP2) and additive-by-additive (ηSNP2) genetic variance in a single analysis model. We first show by simulations that our model leads to unbiased estimates and provide a new theory to predict standard errors estimated using either least-squares or maximum likelihood. We then apply the model to 70 complex traits using 254,679 unrelated individuals from the UK Biobank and 1.1 M genotyped and imputed SNPs. We found strong evidence for additive variance (average across traits h¯SNP2=0.208). In contrast, the average estimate of δ¯SNP2 across traits was 0.001, implying negligible dominance variance at causal variants tagged by common SNPs. The average epistatic variance η¯SNP2 across the traits was 0.055, not significantly different from zero because of the large sampling variance. Our results provide new evidence that genetic variance for complex traits is predominantly additive and that sample sizes of many millions of unrelated individuals are needed to estimate epistatic variance with sufficient precision.</p>
8 Authors
- Valentin Hivert
- Julia Sidorenko
- Florian Rohart
- Michael E Goddard
- Jian Yang
- Naomi R Wray
- Loic Yengo
- Peter M Visscher
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