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

Hivert, Valentin; Sidorenko, Julia; Rohart, Florian; Goddard, Michael E.; Yang, Jian; Wray, Naomi R.; Yengo, Loïc; Visscher, Peter M.

doi:10.1016/j.ajhg.2021.02.014

Cited by 101 publications

(105 citation statements)

References 50 publications

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“…For example, the rs11020521 and rs11020522 haplotype pair exhibited an interaction with sign reversal where each variant individually has no effect or is weakly positive, while the joint haplotype is much more significant in the opposite direction (Figure 4H). These results support other synthetic, eQTL and complex trait studies that have identified non-additive regulatory effects ( 20, 34–36 ) and find that 14.7% of significant haplotype effects (only 0.91% of all tests) have evidence of non-additivity.…”

Section: Resultssupporting

confidence: 89%

Multiple Causal Variants Underlie Genetic Associations in Humans

DeGorter

Gloudemans

Greenwald

et al. 2021

Preprint

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The majority of associations between genetic variation and human traits and diseases are non-coding and in strong linkage disequilibrium (LD) with surrounding genetic variation. In these cases, a single causal variant is often assumed to underlie the association, however no systematic assessment of the number of causal variants has been performed. In this study, we applied a massively parallel reporter assay (MPRA) in lymphoblastoid cells to functionally evaluate 49,256 allelic pairs, representing 30,893 genetic variants in high, local linkage disequilibrium for 744 independent cis-expression quantitative trait loci (eQTL) and assessed each for colocalization across 114 traits. We identified 3,536 allele-independent regulatory regions containing 907 allele-specific regulatory variants, and found that 17.3% of eQTL contained more than one significant allelic effect. We show that detected regulatory variants are highly and specifically enriched for activating chromatin structures and allelic transcription factor binding, for which ETS-domain family members are a large driver. Integration of MPRA profiles with eQTL/complex trait colocalizations identified causal variant sets for associations with blood cell measurements, Multiple Sclerosis, Irritable Bowel Disease, and Crohns Disease. These results demonstrate that a sizable number of association signals are manifest through multiple, tightly-linked causal variants requiring high-throughput functional assays for fine-mapping.

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Section: Resultssupporting

confidence: 89%

Multiple Causal Variants Underlie Genetic Associations in Humans

DeGorter

Gloudemans

Greenwald

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…However, the increase in power is still expected to be substantial from additive components that are usually considered major in a genetic architecture, with effect sizes highly similar across populations ( Wojcik et al, 2019 ). Additionally, currently the contribution from epistasis or G × E components to most trait variability is estimated to be relatively small ( Wang et al, 2019 ; Dahl et al, 2020 ; Hivert et al, 2021 ). For variants with heterogeneous effect sizes per ancestry, other local ancestry-aware regression methods could potentially improve the power of detection in admixed populations ( Atkinson et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Admixed Populations Improve Power for Variant Discovery and Portability in Genome-Wide Association Studies

et al. 2021

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Genome-wide association studies (GWAS) are primarily conducted in single-ancestry settings. The low transferability of results has limited our understanding of human genetic architecture across a range of complex traits. In contrast to homogeneous populations, admixed populations provide an opportunity to capture genetic architecture contributed from multiple source populations and thus improve statistical power. Here, we provide a mechanistic simulation framework to investigate the statistical power and transferability of GWAS under directional polygenic selection or varying divergence. We focus on a two-way admixed population and show that GWAS in admixed populations can be enriched for power in discovery by up to 2-fold compared to the ancestral populations under similar sample size. Moreover, higher accuracy of cross-population polygenic score estimates is also observed if variants and weights are trained in the admixed group rather than in the ancestral groups. Common variant associations are also more likely to replicate if first discovered in the admixed group and then transferred to an ancestral population, than the other way around (across 50 iterations with 1,000 causal SNPs, training on 10,000 individuals, testing on 1,000 in each population, p = 3.78e-6, 6.19e-101, ∼0 for FST = 0.2, 0.5, 0.8, respectively). While some of these FST values may appear extreme, we demonstrate that they are found across the entire phenome in the GWAS catalog. This framework demonstrates that investigation of admixed populations harbors significant advantages over GWAS in single-ancestry cohorts for uncovering the genetic architecture of traits and will improve downstream applications such as personalized medicine across diverse populations.

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“…An error term is sometimes included in Equations 1 and 2 or is otherwise incorporated into the V E term.6 In humans, V D and V I do not appear to influence phenotypic variance to a great degree for most quantitative traits(Hivert et al 2020), meaning that h 2 and H 2 estimates should generally converge.…”

mentioning

confidence: 99%

The Meaning of “Cause” in Genetics

Lynch

2021

Cold Spring Harb Perspect Med

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Causation has multiple distinct meanings in genetics. One reason for this is meaning slippage between two concepts of the gene: Mendelian and molecular. Another reason is that a variety of genetic methods address different kinds of causal relationships. Some genetic studies address causes of traits in individuals, which can only be assessed when single genes follow predictable inheritance patterns that reliably cause a trait. A second sense concerns the causes of trait differences within a population. Whereas some single genes can be said to cause population-level differences, most often these claims concern the effects of many genes. Polygenic traits can be understood using heritability estimates, which estimate the relative influences of genetic and environmental differences to trait differences within a population. Attempts to understand the molecular mechanisms underlying polygenic traits have been developed, although causal inference based on these results remains controversial. Genetic variation has also recently been leveraged as a randomizing factor to identify environmental causes of trait differences. This technique-Mendelian randomization-offers some solutions to traditional epidemiological challenges, although it is limited to the study of environments with known genetic influences.Additionally, the way that people interpret genetic information is influenced by biases and aspects of our cognition. This is true for both laypeople and professionals who are educated about genetics. (For a review, see Lynch et al. 2019.

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

Cited by 101 publications

References 50 publications

Multiple Causal Variants Underlie Genetic Associations in Humans

Multiple Causal Variants Underlie Genetic Associations in Humans

Admixed Populations Improve Power for Variant Discovery and Portability in Genome-Wide Association Studies

The Meaning of “Cause” in Genetics

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