Abstract
Gene–environment interactions (G × E), the interplay of genetic variation with environmental factors, have a pivotal impact on human complex traits and diseases. Statistically, G × E can be assessed by determining the deviation from expectation of predictive models based solely on the phenotypic effects of genetics or environmental exposures. Despite the unprecedented, widespread and diverse use of G × E analytical frameworks, heterogeneity in their application and reporting hinders their applicability in public health. In this Review, we discuss study design considerations as well as G × E analytical frameworks to assess polygenic liability dependent on the environment, to identify specific genetic variants exhibiting G × E, and to characterize environmental context for these dynamics. We conclude with recommendations to address the most common challenges and pitfalls in the conceptualization, methodology and reporting of G × E studies, as well as future directions.
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Acknowledgements
The authors’ work was funded by the Combining advances in Genomics and Environmental science to accelerate Actionable Research and practice in ASD (GEARS) Network (R01ES034554). E.H.-L. and G.L.-W. received support from the National Institutes of Health (NIH) National Human Genome Research Institute (NHGRI) (R35HG011944-02). No funding agencies had a role in the study design, data collection and analysis, decision to publish or preparation of the manuscript. The views expressed are those of the authors and not necessarily those of any funder.
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E.H.-L., C.L.-A. and G.L.W. researched the literature. E.H.-L. and G.L.W. wrote the article. All authors contributed to discussions of the content and reviewed and/or edited the manuscript.
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Nature Reviews Genetics thanks Seunggeun Lee, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Related links
Comparative Toxicogenomics Database: https://ctdbase.org/
Drug–Gene Interaction Database: https://www.dgidb.org/
GWAS Catalog: https://www.ebi.ac.uk/gwas/
PGS Catalog: https://www.pgscatalog.org/
PhenX Toolkit: https://www.phenxtoolkit.org
Supplementary information
Glossary
- Biological or functional interaction
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The scenario whereby the exposures physically interact to produce an outcome.
- Broad-sense heritability
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(H2). The proportion of phenotypic variation attributed to genetic variation, including additive, dominance, epistasis and gene–environment interactions (G × E).
- G × E correlation
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(rGE). A situation when there are genetic differences in exposure to specific environments. rGEcan be classified as passive, active or evocative according to its source.
- G × E heritability
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(h2GE). The proportion of the phenotypic variability attributable to G × E.
- Genome-wide interaction studies
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(GWIS). Analytical approaches to investigate the modifying effect of an exposure variable on a genetic association.
- Heteroscedasticity
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A statistical phenomenon where the variability of the residuals (errors) in a regression model varies across different levels of the predictor variables.
- Imputation
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The statistical process of predicting missing genetic data based on observed data, typically using reference panels of known genetic variants.
- Instrumental variables
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Variables associated with an exposure that is not associated with the outcome through any other pathway.
- Mechanistic interactions
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Situations when there are individuals for whom the outcome would occur if both of two exposures were present but not if one or both of the exposures were absent.
- Mendelian randomization
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(MR). An analytical method that uses ‘instrumental variables’ for the predictor to determine the causal influences of a predictor on an outcome. MR is based on three key assumptions: instrumental variables are associated with the exposure; instrumental variables are not associated with any confounder; and instrumental variables do not exert effects on the phenotype not mediated by the exposure (also known as ‘horizontal pleiotropy’).
- Multiple testing
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Repeated statistical testing of hypotheses within a single study, increasing the likelihood of false positives. Adjustments such as Bonferroni correction help control the family-wise error rate.
- Polygenic score
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(PGS). A single value that aggregates the effect of variants across an individual’s germ-line genome to quantify the genetic predisposition to a trait. Usually calculated as a weighted sum of several trait-associated alleles (dosage), where the per-allele effect sizes (βi) are extracted from the genome-wide association study (GWAS) summary statistics of independent studies.
- Set-based analyses
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Analytical methods that aggregate G × E signals within a gene or genomic region.
- Statistical interactions
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Situations when the effect of a risk factor on an outcome varies across strata of another factor. An interaction on an additive scale occurs when the combined effect of the risk factors differs with regards to the sum of the individual effects, whereas an interaction on a multiplicative scales occurs when the combined effect of the risk factors differs with regards to the product of the individual effects.
- Variance quantitative trait locus
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(vQTL). Genetic variants associated with the variance of a continuous trait.
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Herrera-Luis, E., Benke, K., Volk, H. et al. Gene–environment interactions in human health. Nat Rev Genet (2024). https://doi.org/10.1038/s41576-024-00731-z
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DOI: https://doi.org/10.1038/s41576-024-00731-z
