ArticleAmerican journal of epidemiology2017
Update on the State of the Science for Analytical Methods for Gene-Environment Interactions.
Article in American journal of epidemiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers, 1 of them a synthesis that pooled it.
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Who cites it
73 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Variance-quantitative trait loci enable systematic discovery of gene-environment interactions for cardiometabolic serum biomarkers.Nature communications · 2022Pooled it
- An Epidemiology-Constrained Framework for Mechanistic Hypothesis Restriction and Prioritization in Complex Disease.Journal of evaluation in clinical practice · 2026Article
- The geometry of G × E: How scaling and endogenous treatment effects shape interaction direction.PLoS genetics · 2026Article
- Mapping the Analytical Landscape of Gene-Diet Interactions in Epidemiology: From Classical Models to Causal and Multi-Omics Frameworks.Nutrients · 2026Review
- Pathway-specific polygenic scores substantially increase the discovery of gene-adiposity interactions impacting liver biomarkers.HGG advances · 2026Article
- Association of the triglyceride-glucose index with carotid intima-media thickness in type 2 diabetes: effect modification by age and albuminuria-a retrospective cross-sectional study.Frontiers in cardiovascular medicine · 2026Article
- Exploratory whole-exome analysis of low-density lipoprotein cholesterol and triglyceride response to Mediterranean-style dietary guidance: a focus on plasma lipoprotein pathways.Frontiers in nutrition · 2026Article
- Methods for modeling gene-environment interplay using polygenic risk scores.Statistical applications in genetics and molecular biology · 2026Review
- PIGEON: a statistical framework for estimating gene-environment interaction for polygenic traits.Nature human behaviour · 2025Article
- Pathway polygenic risk scores (pPRS) for the analysis of gene-environment interaction.PLoS genetics · 2025Article
- A large-scale genome-wide study of gene-sleep duration interactions for blood pressure in 811,405 individuals from diverse populations.Molecular psychiatry · 2025Article
- Pathway-specific polygenic scores substantially increase the discovery of gene-adiposity interactions impacting liver biomarkers.medRxiv : the preprint server for health sciences · 2025Article
- Efficient and accurate framework for genome-wide gene-environment interaction analysis in large-scale biobanks.Nature communications · 2025Article
- Decomposed interaction testing improves detection of genetic modifiers of the relationship of dietary omega-3 fatty acid intake and its plasma biomarkers with hsCRP in the UK Biobank.Genes & nutrition · 2025Article
- Identification of gene-sun exposure interactions of GWAS-identified variants in perceived facial aging progression.Frontiers in aging · 2025Article
- Characterization of Additive Gene-environment Interactions For Colorectal Cancer Risk.Epidemiology (Cambridge, Mass.) · 2025Article
- Refinement of a Published Gene-Physical Activity Interaction Impacting HDL-Cholesterol: Role of Sex and Lipoprotein Subfractions.Genetic epidemiology · 2025Article
- Article
- Gene-environment interactions in human health.Nature reviews. Genetics · 2024Review
- Joint modeling of gene-environment correlations and interactions using polygenic risk scores in case-control studies.American journal of epidemiology · 2024Article
13 more citing papers are in PubMed but not listed here.
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Abstract
The analysis of gene-environment interaction (G×E) may hold the key for further understanding the etiology of many complex traits. The current availability of high-volume genetic data, the wide range in types of environmental data that can be measured, and the formation of consortiums of multiple studies provide new opportunities to identify G×E but also new analytical challenges. In this article, we summarize several statistical approaches that can be used to test for G×E in a genome-wide association study. These include traditional models of G×E in a case-control or quantitative trait study as well as alternative approaches that can provide substantially greater power. The latest methods for analyzing G×E with gene sets and with data in a consortium setting are summarized, as are issues that arise due to the complexity of environmental data. We provide some speculation on why detecting G×E in a genome-wide association study has thus far been difficult. We conclude with a description of software programs that can be used to implement most of the methods described in the paper.
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