In:
Science, American Association for the Advancement of Science (AAAS), Vol. 379, No. 6639 ( 2023-03-31), p. 1341-1348
Abstract:
In statistical genetics, dominance is a deviation from an additive genetic effect on a trait and is well documented in model organisms, particularly in the context of measures of “fitness,” and in plant and animal breeding. In humans, however, evidence of nonadditive genetic effects on complex, polygenic traits is sparse. We looked for evidence of nonadditive effects in more than 1000 phenotypes in the UK Biobank population cohort ( N = 361,194). To test for “dominance heritability,” or the aggregate contribution of nonadditive genetic effects to trait variance genome-wide, we introduce dominance linkage disequilibrium (LD) score regression (d-LDSC). Our method builds upon existing software to include nonadditive effects site by site. RATIONALE Identifying nonadditive genetic effects on traits allows us to better understand their underlying biology. Although nonadditive effects are commonly tested in Mendelian disorders, they are rarely tested in human complex traits. Population biobanks allow us to explore questions of genetic architecture at scale and to detect small effect sizes. We sought to test for evidence of nonadditive effects in the UK Biobank. We also investigated whether the less-correlated nature of dominance associations among sites at a locus relative to their additive counterparts can help pinpoint causal variants. RESULTS We identified 183 phenotype-locus pairs at genome-wide significance ( P 〈 4.7 × 10 −11 ). We replicated known associations for phenotypes with dominant and recessive patterns of inheritance, for example, hair color at the MC1R locus. Qualitatively, we observed stronger nonadditive effects in instances where additive effects are large or the underlying genetic architecture is concentrated in a few loci. The power to detect nonadditive loci was low: We estimate that around a 20- to 30-fold increase in sample size is necessary to capture evidence of dominance effects similar to that observed at additive loci. Applying LDSC and d-LDSC to 1060 traits, we confirmed strong evidence of additive heritability (700 traits, P 〈 4.7 × 10 −5 ). Despite analyzing a much larger collection of traits with increased power over existing studies, we found little evidence of dominance heritability. We introduced dominance fine-mapping to pinpoint causal variants in the presence of a dominance signal. Gains in fine-mapping resolution due to the rapid decay of dominance LD compared with additive LD are generally outweighed by weaker association signals. CONCLUSION We evaluated the contribution of nonadditive genetic effects on trait variation across 1060 traits in the UK Biobank. We identified a modest number of loci and confirmed that heritability explained by dominance is small, in line with previous analyses. Our results support the robustness of the additive model when modeling human complex traits, consistent with the view that most common variants induce small perturbations of continuous latent biological processes aggregated by a mean-field approximation. Furthermore, the additive model typically captures much of the trait variance at a population level, even under classical dominant or recessive patterns. We estimate that for most complex traits, minimum sample sizes of millions are required to detect nonadditive effects at the same strength of association as those reported for additive effects. Summary of the analysis of nonadditive common variant effects site by site and in aggregate across the human genome for more than 1000 traits. The unique recoding of allele counts (up to a linear rescaling) captures any residual genetic association signal at a variant after accounting for the additive effect, known as the dominance deviation (top left). This “dominance encoding” varies with allele frequency, as shown by the color legend. The dominance encoding is generally distinct from biological recessive-dominant architecture, which includes an additive contribution of allelic dosage (top center). The correlation structure between variants under the dominance encoding decays as the square of the additive correlation between variants, as shown by the color legend (top right). The areas under the blue and red curves are average additive and dominance LD scores, respectively. Aggregate Manhattan plots of nonadditive marginal effect sizes assess the dominance deviation across the genome for 1060 traits (bottom left). The y axis is on the −log 10 scale up to 30, after which it switches to a −log 10 [−log 10 ( P )] scale to aid presentation. The genome-wide significance threshold ( P 〈 4.7 × 10 −11 ) is displayed in orange. Additive and dominance effect-size estimates coupled with additive and dominance LD scores were used to estimate the additive and dominance contribution to phenotypic variance, known as additive and dominance SNP heritability (bottom right). Additive and dominance SNP heritability estimates across these traits are displayed in blue and red, respectively. The dashed gray lines display the mean estimates across traits.
Type of Medium:
Online Resource
ISSN:
0036-8075
,
1095-9203
DOI:
10.1126/science.abn8455
Language:
English
Publisher:
American Association for the Advancement of Science (AAAS)
Publication Date:
2023
detail.hit.zdb_id:
128410-1
detail.hit.zdb_id:
2066996-3
detail.hit.zdb_id:
2060783-0
SSG:
11
Permalink
