ArticleAmerican journal of epidemiology2012
Likelihood ratio test for detecting gene (G)-environment (E) interactions under an additive risk model exploiting G-E independence for case-control data.
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 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Association between NQO1 C609T polymorphism and bladder cancer susceptibility: a systemic review and meta-analysis.Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine · 2013Pooled it
- Methods for modeling gene-environment interplay using polygenic risk scores.Statistical applications in genetics and molecular biology · 2026Review
- High-Dimensional Gene-Environment Interaction Analysis.Annual review of statistics and its application · 2025Article
- Joint modeling of gene-environment correlations and interactions using polygenic risk scores in case-control studies.American journal of epidemiology · 2024Article
- Assessing Additive Interactions between Protective Factors Using Relative Risk Reduction Due to Interaction.Medicina (Kaunas, Lithuania) · 2024Article
- Obesity, Adiposity, and Risk of Symptomatic Gallstone Disease According to Genetic Susceptibility.Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2022Article
- A Robust Test for Additive Gene-Environment Interaction Under the Trend Effect of Genotype Using an Empirical Bayes-Type Shrinkage Estimator.American journal of epidemiology · 2021Article
- Smoking Modifies Pancreatic Cancer Risk Loci on 2q21.3.Cancer research · 2021Article
- A Likelihood Ratio Test for Gene-Environment Interaction Based on the Trend Effect of Genotype Under an Additive Risk Model Using the Gene-Environment Independence Assumption.American journal of epidemiology · 2021Article
- Interactions Between Genome-Wide Genetic Factors and Smoking Influencing Risk of Systemic Lupus Erythematosus.Arthritis & rheumatology (Hoboken, N.J.) · 2020Article
- A general approach to detect gene (G)-environment (E) additive interaction leveraging G-E independence in case-control studies.Statistics in medicine · 2019Article
- A comprehensive gene-environment interaction analysis in Ovarian Cancer using genome-wide significant common variants.International journal of cancer · 2019Article
- Estimating Additive Interaction Effect in Stratified Two-Phase Case-Control Design.Human heredity · 2019Article
- Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence.American journal of epidemiology · 2018Article
- Measuring differential treatment benefit across marker specific subgroups: The choice of outcome scale.Contemporary clinical trials · 2017Article
- Update on the State of the Science for Analytical Methods for Gene-Environment Interactions.American journal of epidemiology · 2017Article
- Augmented case-only designs for randomized clinical trials with failure time endpoints.Biometrics · 2016Article
- Modification of Occupational Exposures on Bladder Cancer Risk by Common Genetic Polymorphisms.Journal of the National Cancer Institute · 2015Article
- Prognostic and Predictive Values and Statistical Interactions in the Era of Targeted Treatment.Genetic epidemiology · 2015Article
- An exposure-weighted score test for genetic associations integrating environmental risk factors.Biometrics · 2015Article
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Authors and funding
8 authors.
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Abstract
There has been a long-standing controversy in epidemiology with regard to an appropriate risk scale for testing interactions between genes (G) and environmental exposure (E ). Although interaction tests based on the logistic model-which approximates the multiplicative risk for rare diseases-have been more widely applied because of its convenience in statistical modeling, interactions under additive risk models have been regarded as closer to true biologic interactions and more useful in intervention-related decision-making processes in public health. It has been well known that exploiting a natural assumption of G-E independence for the underlying population can dramatically increase statistical power for detecting multiplicative interactions in case-control studies. However, the implication of the independence assumption for tests for additive interaction has not been previously investigated. In this article, the authors develop a likelihood ratio test for detecting additive interactions for case-control studies that incorporates the G-E independence assumption. Numerical investigation of power suggests that incorporation of the independence assumption can enhance the efficiency of the test for additive interaction by 2- to 2.5-fold. The authors illustrate their method by applying it to data from a bladder cancer study.
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