ArticleAmerican journal of epidemiology2012
Gene-environment interactions in genome-wide association studies: a comparative study of tests applied to empirical studies of type 2 diabetes.
Article in American journal of epidemiology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers, 4 of them syntheses that pooled it.
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Who cites it
75 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Linking Alzheimer's disease and type 2 diabetes: Novel shared susceptibility genes detected by cFDR approach.Journal of the neurological sciences · 2017Pooled it
- Interaction between genes and macronutrient intake on the risk of developing type 2 diabetes: systematic review and findings from European Prospective Investigation into Cancer (EPIC)-InterAct.The American journal of clinical nutrition · 2017Pooled it
- Post-GWAS gene-environment interplay in breast cancer: results from the Breast and Prostate Cancer Cohort Consortium and a meta-analysis on 79,000 women.Human molecular genetics · 2014Pooled it
- Genome-wide joint meta-analysis of SNP and SNP-by-smoking interaction identifies novel loci for pulmonary function.PLoS genetics · 2012Pooled it
- A trans-omics gene-smoking interaction study of lung cancer based on consortium data.American journal of respiratory and critical care medicine · 2026Article
- Robust Mixed Model Association Test for Gene-Environment Interactions.medRxiv : the preprint server for health sciences · 2025Article
- Characterizing the genetic architecture of drug response using gene-context interaction methods.Cell genomics · 2024Article
- Genetics of glucose homeostasis in pregnancy and postpartum.Diabetologia · 2024Article
- Admixture Mapping of Chronic Kidney Disease and Risk Factors in Hispanic/Latino Individuals From Central America Country of Origin.Circulation. Genomic and precision medicine · 2024Article
- Visit to visit transition in TXNIP gene methylation and the risk of type 2 diabetes mellitus: a nested case-control study.Journal of human genetics · 2024Article
- A Varying Coefficient Model to Jointly Test Genetic and Gene-Environment Interaction Effects.Behavior genetics · 2023Article
- Environmental pathways affecting gene expression (E.PAGE) as an R package to predict gene-environment associations.Scientific reports · 2022Article
- Citrus-Gene interaction and melanoma risk in the UK Biobank.International journal of cancer · 2022Article
- A Cross-validated Ensemble Approach to Robust Hypothesis Testing of Continuous Nonlinear Interactions: Application to Nutrition-Environment Studies.Journal of the American Statistical Association · 2022Article
- A hierarchical integrative group least absolute shrinkage and selection operator for analyzing environmental mixtures.Environmetrics · 2021Article
- Gene-Environment Interaction: A Variable Selection Perspective.Methods in molecular biology (Clifton, N.J.) · 2021Review
- Article
- Genetic and Non-genetic Determinants of Cardiovascular Disease in South Asians.Current diabetes reviews · 2021Review
- Efficient gene-environment interaction tests for large biobank-scale sequencing studies.Genetic epidemiology · 2020Article
- Evidence for gene-smoking interactions for hearing loss and deafness in Japanese American families.Hearing research · 2020Article
15 more citing papers are in PubMed but not listed here.
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7 authors.
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Abstract
The question of which statistical approach is the most effective for investigating gene-environment (G-E) interactions in the context of genome-wide association studies (GWAS) remains unresolved. By using 2 case-control GWAS (the Nurses' Health Study, 1976-2006, and the Health Professionals Follow-up Study, 1986-2006) of type 2 diabetes, the authors compared 5 tests for interactions: standard logistic regression-based case-control; case-only; semiparametric maximum-likelihood estimation of an empirical-Bayes shrinkage estimator; and 2-stage tests. The authors also compared 2 joint tests of genetic main effects and G-E interaction. Elevated body mass index was the exposure of interest and was modeled as a binary trait to avoid an inflated type I error rate that the authors observed when the main effect of continuous body mass index was misspecified. Although both the case-only and the semiparametric maximum-likelihood estimation approaches assume that the tested markers are independent of exposure in the general population, the authors did not observe any evidence of inflated type I error for these tests in their studies with 2,199 cases and 3,044 controls. Both joint tests detected markers with known marginal effects. Loci with the most significant G-E interactions using the standard, empirical-Bayes, and 2-stage tests were strongly correlated with the exposure among controls. Study findings suggest that methods exploiting G-E independence can be efficient and valid options for investigating G-E interactions in GWAS.
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