ArticleGenetic epidemiology2018
A test for gene-environment interaction in the presence of measurement error in the environmental variable.
Article in Genetic epidemiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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.
The trial behind it
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
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Differential and shared genetic effects on kidney function between diabetic and non-diabetic individuals.Communications biology · 2022Pooled it
- Leveraging polygenic risk scores to infer causal directions in genotype-by-environment interactions between complex traits.Human genetics · 2026Article
- How exposomic tools complement and enrich genomic research.Cell genomics · 2025Review
- Testing gene-environment interactions in the presence of confounders and mismeasured environmental exposures.G3 (Bethesda, Md.) · 2021Article
- Using Genetic Marginal Effects to Study Gene-Environment Interactions with GWAS Data.Behavior genetics · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The identification of gene-environment interactions in relation to risk of human diseases has been challenging. One difficulty has been that measurement error in the exposure can lead to massive reductions in the power of the test, as well as in bias toward the null in the interaction effect estimates. Leveraging previous work on linear discriminant analysis, we develop a new test of interaction between genetic variants and a continuous exposure that mitigates these detrimental impacts of exposure measurement error in ExG testing by reversing the role of exposure and the diseases status in the fitted model, thus transforming the analysis to standard linear regression. Through simulation studies, we show that the proposed approach is valid in the presence of classical exposure measurement error as well as when there is correlation between the exposure and the genetic variant. Simulations also demonstrated that the reverse test has greater power compared to logistic regression. Finally, we confirmed that our approach eliminates bias from exposure measurement error in estimation. Computing times are reduced by as much as fivefold in this new approach. For illustrative purposes, we applied the new approach to an ExGWAS study of interactions with alcohol and body mass index among 1,145 cases with invasive breast cancer and 1,142 controls from the Cancer Genetic Markers of Susceptibility study.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.