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Depression has well-established influences from genetic and environmental risk factors. This has led to the diathesis-stress theory, which assumes a multiplicative gene-by-environment interaction (GxE) effect on risk. Recently, Colodro-Conde et al. empirically tested this theory, using the polygenic risk score for major depressive disorder (PRS, genes) and stressful life events (SLE, environment) effects on depressive symptoms, identifying significant GxE effects with an additive contribution to liability. We have tested the diathesis-stress theory on an independent sample of 4919 individuals. We identified nominally significant positive GxE effects in the full cohort (R = 0.08%, p = 0.049) and in women (R = 0.19%, p = 0.017), but not in men (R = 0.15%, p = 0.07). GxE effects were nominally significant, but only in women, when SLE were split into those in which the respondent plays an active or passive role (R = 0.15%, p = 0.038; R = 0.16%, p = 0.033, respectively). High PRS increased the risk of depression in participants reporting high numbers of SLE (p = 2.86 × 10). However, in those participants who reported no recent SLE, a higher PRS appeared to increase the risk of depressive symptoms in men (β = 0.082, p = 0.016) but had a protective effect in women (β = -0.061, p = 0.037). This difference was nominally significant (p = 0.017). Our study reinforces the evidence of additional risk in the aetiology of depression due to GxE effects. However, larger sample sizes are required to robustly validate these findings.
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http://dx.doi.org/10.1038/s41398-018-0356-7 | DOI Listing |
Stat Med
September 2025
Department of Mathematics, Indian Institute of Technology Hyderabad, Kandi, India.
Gene-environment (GxE) interactions crucially contribute to complex phenotypes. The statistical power of a GxE interaction study is limited mainly due to weak GxE interaction effect sizes. Joint tests of the main genetic and GxE effects for a univariate phenotype were proposed to utilize the individually weak GxE effects to improve the discovery of associated genetic loci.
View Article and Find Full Text PDFGenes (Basel)
July 2025
Center for Craniofacial and Dental Genetics, Department of Oral and Craniofacial Sciences, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA.
Background/objectives: Maternal exposures are known to influence the risk of isolated cleft lip with or without cleft palate (CL/P)-a common and highly heritable birth defect with a multifactorial etiology.
Methods: To identify new risk loci, we conducted a genome-wide gene-environment interaction (GEI) analysis of CL/P with maternal smoking and vitamin use in Filipinos ( = 540, = 260). Since GEI analyses are typically low in power and the results can be difficult to interpret, we applied multiple testing frameworks to evaluate potential GEI effects: a one degree-of-freedom (1df) GxE test, the 3df joint test, and the two-step EDGE approach.
PLoS Genet
August 2025
Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
In genome-wide association studies (GWAS), it is often desirable to test for interactions, such as gene-environment (G x E) or gene-gene (G x G) interactions, between single-nucleotide polymorphisms (SNPs, G's) and environmental variables (E's). However, directly accounting for interaction is often infeasible, because the interacting variable is latent or the computational burden is too large. For quantitative traits (Y) that are approximately normally distributed, it has been shown that indirect testing on GxE can be done by testing for heteroskedasticity of Y between genotypes.
View Article and Find Full Text PDFGenetics
August 2025
Department of Genetics, Cell Biology, & Development, University of Minnesota Twin Cities, Minneapolis, MN 55455, USA.
In genotype-by-environment interactions (GxE), the effect of a genetic variant on a trait depends on the environment. GxE influences numerous organismal traits. However, we have limited understanding of how GxE shapes molecular processes.
View Article and Find Full Text PDFPlant Methods
August 2025
Plant Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food (ILVO), Melle, Belgium.
Integrating genotype-by-Environment (GxE) interactions into genomic prediction models has been demonstrated to enhance the accuracy of predictions for crops exposed to unfavourable environmental conditions. However, despite the increasing complexity of machine learning models in genomic prediction, no model or approach has been found to be overall superior in comparison to a classical genomic best linear unbiased prediction (GBLUP) model. In this paper, we compared two GBLUP models (Linear Mixed Effects model and Bayesian GBLUP) with two machine learning models (Random Forest and Extreme Gradient Boosting) on the EUCLEG soybean genotype set phenotyped in Belgium and Serbia.
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