ArticleEuropean journal of epidemiology2015
The impact of exposure-biased sampling designs on detection of gene-environment interactions in case-control studies with potential exposure misclassification.
Article in European journal of epidemiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Physical activity, polygenic risk score, and colorectal cancer risk.Cancer medicine · 2023Article
- Maternal verbal aggression in early infancy and child's internalizing symptoms: interaction by common oxytocin polymorphisms.European archives of psychiatry and clinical neuroscience · 2020Observational
- Genetics, adaptation to environmental changes and archaic admixture in the pathogenesis of diabetes mellitus in Indigenous Australians.Reviews in endocrine & metabolic disorders · 2019Review
- Observational
- Estimating Additive Interaction Effect in Stratified Two-Phase Case-Control Design.Human heredity · 2019Article
- Genetic modifiers of radon-induced lung cancer risk: a genome-wide interaction study in former uranium miners.International archives of occupational and environmental health · 2018Article
- A test for gene-environment interaction in the presence of measurement error in the environmental variable.Genetic epidemiology · 2018Article
- The Promise of Selecting Individuals from the Extremes of Exposure in the Analysis of Gene-Physical Activity Interactions.Human heredity · 2018Article
- Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases.American journal of epidemiology · 2017Article
- The Rotterdam Study: 2018 update on objectives, design and main results.European journal of epidemiology · 2017Article
- Opportunities and Challenges for Environmental Exposure Assessment in Population-Based Studies.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2017Review
- Exposure enriched outcome dependent designs for longitudinal studies of gene-environment interaction.Statistics in medicine · 2017Article
- Exposure Enriched Case-Control (EECC) Design for the Assessment of Gene-Environment Interaction.Genetic epidemiology · 2016Article
- Mass spectrometry in epidemiological studies: What are the key considerations?European journal of epidemiology · 2016Article
- Caution: work in progress : While the methodological "revolution" deserves in-depth study, clinical researchers and senior epidemiologists should not be disenfranchised.European journal of epidemiology · 2016Article
- Tests for Gene-Environment Interactions and Joint Effects With Exposure Misclassification.American journal of epidemiology · 2016Article
- Finding the missing gene-environment interactions.European journal of epidemiology · 2015Article
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5 authors.
Funding
Abstract
With limited funding and biological specimen availability, choosing an optimal sampling design to maximize power for detecting gene-by-environment (G-E) interactions is critical. Exposure-enriched sampling is often used to select subjects with rare exposures for genotyping to enhance power for tests of G-E effects. However, exposure misclassification (MC) combined with biased sampling can affect characteristics of tests for G-E interaction and joint tests for marginal association and G-E interaction. Here, we characterize the impact of exposure-biased sampling under conditions of perfect exposure information and exposure MC on properties of several methods for conducting inference. We assess the Type I error, power, bias, and mean squared error properties of case-only, case-control, and empirical Bayes methods for testing/estimating G-E interaction and a joint test for marginal G (or E) effect and G-E interaction across three biased sampling schemes. Properties are evaluated via empirical simulation studies. With perfect exposure information, exposure-enriched sampling schemes enhance power as compared to random selection of subjects irrespective of exposure prevalence but yield bias in estimation of the G-E interaction and marginal E parameters. Exposure MC modifies the relative performance of sampling designs when compared to the case of perfect exposure information. Those conducting G-E interaction studies should be aware of exposure MC properties and the prevalence of exposure when choosing an ideal sampling scheme and method for characterizing G-E interactions and joint effects.
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