ArticleBiometrics2018
Testing for gene-environment interaction under exposure misspecification.
Article in Biometrics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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
10 citing papers in PubMed.
- Functional Varying-Index Coefficients Model for Dynamic Synergistic Gene-Environment Interactions.Statistics in biosciences · 2026Article
- Identification of Pancreatic Cancer Germline Risk Variants With Effects That Are Modified by Smoking.JCO precision oncology · 2024Article
- A robust and adaptive framework for interaction testing in quantitative traits between multiple genetic loci and exposure variables.PLoS genetics · 2022Article
- Two-stage penalized regression screening to detect biomarker-treatment interactions in randomized clinical trials.Biometrics · 2022Article
- Genome-wide gene-air pollution interaction analysis of lung function in 300,000 individuals.Environment international · 2022Article
- Testing gene-environment interactions in the presence of confounders and mismeasured environmental exposures.G3 (Bethesda, Md.) · 2021Article
- GxEsum: a novel approach to estimate the phenotypic variance explained by genome-wide GxE interaction based on GWAS summary statistics for biobank-scale data.Genome biology · 2021Article
- Identification of novel loci associated with infant cognitive ability.Molecular psychiatry · 2020Article
- Identification of gene-environment interactions with marginal penalization.Genetic epidemiology · 2020Article
- Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Complex interplay between genetic and environmental factors characterizes the etiology of many diseases. Modeling gene-environment (GxE) interactions is often challenged by the unknown functional form of the environment term in the true data-generating mechanism. We study the impact of misspecification of the environmental exposure effect on inference for the GxE interaction term in linear and logistic regression models. We first examine the asymptotic bias of the GxE interaction regression coefficient, allowing for confounders as well as arbitrary misspecification of the exposure and confounder effects. For linear regression, we show that under gene-environment independence and some confounder-dependent conditions, when the environment effect is misspecified, the regression coefficient of the GxE interaction can be unbiased. However, inference on the GxE interaction is still often incorrect. In logistic regression, we show that the regression coefficient is generally biased if the genetic factor is associated with the outcome directly or indirectly. Further, we show that the standard robust sandwich variance estimator for the GxE interaction does not perform well in practical GxE studies, and we provide an alternative testing procedure that has better finite sample properties.
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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.