ArticleFrontiers in genetics2014
A simulation study of gene-by-environment interactions in GWAS implies ample hidden effects.
Article in Frontiers in genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Ranking and characterization of established BMI and lipid associated loci as candidates for gene-environment interactions.PLoS genetics · 2017Pooled it
- BMI-genome interactions regulate global gene expression with emphasis in brain and gut.Cell genomics · 2026Article
- Choice of phenotype scale is critical in biobank-based G×E tests.bioRxiv : the preprint server for biology · 2026Article
- Dysfunctional VLDL metabolism in MASLD.npj metabolic health and disease · 2024Review
- Novel Functional Genomics Approaches Bridging Neuroscience and Psychiatry.Biological psychiatry global open science · 2023Review
- Testing the generalizability of ancestry-specific polygenic risk scores to predict prostate cancer in sub-Saharan Africa.Genome biology · 2022Article
- Canalization of the Polygenic Risk for Common Diseases and Traits in the UK Biobank Cohort.Molecular biology and evolution · 2022Article
- Suitability of GWAS as a Tool to Discover SNPs Associated with Tick Resistance in Cattle: A Review.Pathogens (Basel, Switzerland) · 2021Review
- Precision Medicine and Obesity.Gastroenterology clinics of North America · 2021 · on this mapReview
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- Article
- PhenotypeSimulator: A comprehensive framework for simulating multi-trait, multi-locus genotype to phenotype relationships.Bioinformatics (Oxford, England) · 2018Article
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- Replicability and Prediction: Lessons and Challenges from GWAS.Trends in genetics : TIG · 2018Review
- Gene-by-environment interactions in urban populations modulate risk phenotypes.Nature communications · 2018Article
- Excessive fuel availability amplifies the FTO-mediated obesity risk: results from the TUEF and Whitehall II studies.Scientific reports · 2017Article
- Genotypic variability based association identifies novel non-additive loci DHCR7 and IRF4 in sero-negative rheumatoid arthritis.Scientific reports · 2017Article
- Article
- Evidence for large-scale gene-by-smoking interaction effects on pulmonary function.International journal of epidemiology · 2017Article
- The importance of gene-environment interactions in human obesity.Clinical science (London, England : 1979) · 2016Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The switch to a modern lifestyle in recent decades has coincided with a rapid increase in prevalence of obesity and other diseases. These shifts in prevalence could be explained by the release of genetic susceptibility for disease in the form of gene-by-environment (GxE) interactions. Yet, the detection of interaction effects requires large sample sizes, little replication has been reported, and a few studies have demonstrated environmental effects only after summing the risk of GWAS alleles into genetic risk scores (GRSxE). We performed extensive simulations of a quantitative trait controlled by 2500 causal variants to inspect the feasibility to detect gene-by-environment interactions in the context of GWAS. The simulated individuals were assigned either to an ancestral or a modern setting that alters the phenotype by increasing the effect size by 1.05-2-fold at a varying fraction of perturbed SNPs (from 1 to 20%). We report two main results. First, for a wide range of realistic scenarios, highly significant GRSxE is detected despite the absence of individual genotype GxE evidence at the contributing loci. Second, an increase in phenotypic variance after environmental perturbation reduces the power to discover susceptibility variants by GWAS in mixed cohorts with individuals from both ancestral and modern environments. We conclude that a pervasive presence of gene-by-environment effects can remain hidden even though it contributes to the genetic architecture of complex traits.
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