Testing gene-environment interaction in large-scale case-control association studies: possible choices and comparisons.
paper
Cited
Public
Unavailable
- Authors
- Mukherjee, Bhramar; Ahn, Jaeil; Gruber, Stephen B; Chatterjee, Nilanjan
- Year
- 2012
- Journal
- American journal of epidemiology
- PMID
- 22199027
- DOI
- 10.1093/aje/kwr367
- PMCID
- PMC3286201
Several methods for screening gene-environment interaction have recently been proposed that address the issue of using gene-environment independence in a data-adaptive way. In this report, the authors present a comparative simulation study of power and type I error properties of 3 classes of procedures: 1) the standard 1-step case-control method; 2) the case-only method that requires an assumption of gene-environment independence for the underlying population; and 3) a variety of hybrid methods, including empirical-Bayes, 2-step, and model averaging, that aim at gaining power by exploiting the assumption of gene-environment independence and yet can protect against false positives when the independence assumption is violated. These studies suggest that, although the case-only method generally has maximum power, it has the potential to create substantial false positives in large-scale studies even when a small fraction of markers are associated with the exposure under study in the underlying population. All the hybrid methods perform well in protecting against such false positives and yet can retain substantial power advantages over standard case-control tests. The authors conclude that, for future genome-wide scans for gene-environment interactions, major power gain is possible by using alternatives to standard case-control analysis. Whether a case-only type scan or one of the hybrid methods should be used depends on the strength and direction of gene-environment interaction and association, the level of tolerance for false positives, and the nature of replication strategies.
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| Robust Bayesian variable selection for gene-environment interactions. | Ren J et al. | — | 2023 | → |
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| Smoking Modifies Pancreatic Cancer Risk Loci on 2q21.3. | Mocci E et al. | — | 2021 | → |
| Semiparametric Bayesian variable selection for gene-environment interactions. | Ren J et al. | — | 2020 | → |
| Evaluation of gene-environment interactions for colorectal cancer susceptibility loci using case-only and case-control designs. | Song N et al. | — | 2019 | → |
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| Dissecting gene-environment interactions: A penalized robust approach accounting for hierarchical structures. | Wu C et al. | — | 2018 | → |
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| Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence. | Liu G et al. | — | 2018 | → |
| Using imputed genotype data in the joint score tests for genetic association and gene-environment interactions in case-control studies. | Song M et al. | — | 2018 | → |
| Adult onset asthma and interaction between genes and active tobacco smoking: The GABRIEL consortium. | Vonk JM et al. | — | 2017 | → |
| Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases. | McAllister K et al. | — | 2017 | → |
| Informatics and Data Analytics to Support Exposome-Based Discovery for Public Health. | Manrai AK et al. | — | 2017 | → |
| Lessons Learned From Past Gene-Environment Interaction Successes. | Ritz BR et al. | — | 2017 | → |
| Semiparametric analysis of complex polygenic gene-environment interactions in case-control studies. | Stalder O et al. | — | 2017 | → |
| Set-Based Tests for the Gene-Environment Interaction in Longitudinal Studies. | He Z et al. | — | 2017 | → |
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| A perspective on interaction effects in genetic association studies. | Aschard H | — | 2016 | → |
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| A penalized robust semiparametric approach for gene-environment interactions. | Wu C et al. | — | 2015 | → |
| Candidate gene-environment interaction research: reflections and recommendations. | Dick DM et al. | — | 2015 | → |
| Deciphering Genome Environment Wide Interactions Using Exposed Subjects Only. | Zhao LP et al. | — | 2015 | → |
| Latent variable models for gene-environment interactions in longitudinal studies with multiple correlated exposures. | Tao Y et al. | — | 2015 | → |
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| Functional logistic regression approach to detecting gene by longitudinal environmental exposure interaction in a case-control study. | Wei P et al. | — | 2014 | → |
| Identification of new genetic susceptibility loci for breast cancer through consideration of gene-environment interactions. | Schoeps A et al. | — | 2014 | → |
| Is the gene-environment interaction paradigm relevant to genome-wide studies? The case of education and body mass index. | Boardman JD et al. | — | 2014 | → |
| Testing departure from additivity in Tukey's model using shrinkage: application to a longitudinal setting. | Ko YA et al. | — | 2014 | → |
| The role of environmental heterogeneity in meta-analysis of gene-environment interactions with quantitative traits. | Li S et al. | — | 2014 | → |
| A nonparametric test to detect quantitative trait loci where the phenotypic distribution differs by genotypes. | Aschard H et al. | — | 2013 | → |
| A novel method for identifying nonlinear gene-environment interactions in case-control association studies. | Wu C et al. | — | 2013 | → |
| A review of NCI's extramural grant portfolio: identifying opportunities for future research in genes and environment in cancer. | Ghazarian AA et al. | — | 2013 | → |
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| Current evidence for an inherited genetic basis of childhood acute lymphoblastic leukemia. | Urayama KY et al. | — | 2013 | → |
| Empirical hierarchical bayes approach to gene-environment interactions: development and application to genome-wide association studies of lung cancer in TRICL. | Sohns M et al. | — | 2013 | → |
| Environmental confounding in gene-environment interaction studies. | Vanderweele TJ et al. | — | 2013 | → |
| Finding novel genes by testing G × E interactions in a genome-wide association study. | Gauderman WJ et al. | — | 2013 | → |
| Gene-environment interactions in cancer epidemiology: a National Cancer Institute Think Tank report. | Hutter CM et al. | — | 2013 | → |
| Lack of association between STK39 and hypertension in the Chinese population. | Xu J et al. | — | 2013 | → |
| Network-guided sparse regression modeling for detection of gene-by-gene interactions. | Lu C et al. | — | 2013 | → |
| The case-only test for gene-environment interaction is not uniformly powerful: an empirical example. | Wu C et al. | — | 2013 | → |
| Challenges and opportunities in genome-wide environmental interaction (GWEI) studies. | Aschard H et al. | — | 2012 | → |
| Conventional case-cohort design and analysis for studies of interaction. | Cologne J et al. | — | 2012 | → |
| Gene-environment interactions in asthma and allergic diseases: challenges and perspectives. | Kauffmann F et al. | — | 2012 | → |
| Gene-environment interactions in the development of type 2 diabetes: recent progress and continuing challenges. | Cornelis MC et al. | — | 2012 | → |
| Genetic prediction of common diseases. Still no help for the clinical diabetologist! | Prudente S et al. | — | 2012 | → |
| Inclusion of gene-gene and gene-environment interactions unlikely to dramatically improve risk prediction for complex diseases. | Aschard H et al. | — | 2012 | → |
| Invited commentary: GE-Whiz! Ratcheting gene-environment studies up to the whole genome and the whole exposome. | Thomas DC et al. | — | 2012 | → |
| Next generation analytic tools for large scale genetic epidemiology studies of complex diseases. | Mechanic LE et al. | — | 2012 | → |
| Personal genetics, the European regulations maze and the way out. | Colaiacovo M et al. | — | 2012 | → |
| Powerful cocktail methods for detecting genome-wide gene-environment interaction. | Hsu L et al. | — | 2012 | → |
| Simultaneously testing for marginal genetic association and gene-environment interaction. | Dai JY et al. | — | 2012 | → |
| Two-stage testing procedures with independent filtering for genome-wide gene-environment interaction. | Dai JY et al. | — | 2012 | → |