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Cited by 136Genetic Associations and EpidemiologyGenetic and phenotypic traits in livestockGenetic Mapping and Diversity in Plants and Animals

Estimation of non-additive genetic variance in human complex traits from a large sample of unrelated individuals

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Hivert, Valentin, Sidorenko, Julia, Rohart, Florian, Goddard, Michael E., Yang, Jian, Wray, Naomi R., Yengo, Loic, Visscher, Peter M.·2021·The American Journal of Human Genetics
DOI 10.1016/j.ajhg.2021.02.014PMC PMC8206200

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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 (h SNP2), dominance (δ SNP2) and additive-by-additive (η SNP2) 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.

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  • The remaining reduction in predictive power is due to factors including epistasis, gene-environment interactions, and differences between populations in gene-environment correlations, assortative mating, and environmental variance.

    “The remaining reduction in predictive power is due to factors including epistasis (although epistatic variance is likely small13,15), gene-environment interactions, and differences between populations in gene-environment correlations, assortative mating, and environmental variance.”

    From Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals (2022) · cites this paper as [13]

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Authors
Hivert, V., Sidorenko, J., Rohart, F., Goddard, M. E., Yang, J., Wray, N. R., Yengo, L., & Visscher, P. M.
Affiliations
  • The University of Queensland (AU)
  • The University of Melbourne (AU)
Corresponding author
Visscher, Peter M.
ORCID
Venue
The American Journal of Human Genetics
Publisher
Elsevier BV
Published
2021-05
Volume
108
Issue
5
Pages
786-798
ISSN
0002-9297 (ISSN-L)
DOI
10.1016/j.ajhg.2021.02.014
PMC
PMC8206200
OpenAlex
W3147973671
Cited by
136
References
4
Citations by year
2026: 8 · 2025: 22 · 2024: 24 · 2023: 31 · 2022: 31 · 2021: 17 · 2020: 1
FWCI
14.56
Type
journal article
Language
en
Update notices
erratum (2021-05-06)
Open access
Licence
Locations
  • The American Journal of Human Genetics (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed (publishedVersion)
  • Minerva Access (University of Melbourne) (submittedVersion)
Funders
  • NHMRC (1173790, 1113400)
  • University of Queensland
  • Australian Research Council (FL180100072, FT180100186, 12505)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
Keywords
non-additive genetic variance, dominance variance, additive genetic variance, complex traits, unrelated individuals, heritability estimation, UK Biobank, sample size requirements
MeSH terms
Epistasis, Genetic, Female, Genotype, United Kingdom, Humans, Male, Models, Genetic, Phenotype, Reproducibility of Results, Biological Specimen Banks, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Datasets as Topic

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