Exploiting Gene-Environment Interaction to Detect Genetic Associations
Abstract
Complex disease by definition results from the interplay of genetic and environmental factors. However, it is currently unclear how gene-environment interaction can best be used to locate complex disease susceptibility loci, particularly in the context of studies where between 1,000 and 1,000,000 markers are scanned for association with disease. We present a joint test of marginal association and gene-environment interaction for case-control data. We compare the power and sample size requirements of this joint test to other analyses: the marginal test of genetic association, the standard test for gene-environment interaction based on logistic regression, and the case-only test for interaction that exploits gene-environment independence. Although for many penetrance models the joint test of genetic marginal effect and interaction is not the most powerful, it is nearly optimal across all penetrance models we considered. In particular, it generally has better power than the marginal test when the genetic effect is restricted to exposed subjects and much better power than the tests of gene-environment interaction when the genetic effect is not restricted to a particular exposure level. This makes the joint test an attractive tool for large-scale association scans where the true gene-environment interaction model is unknown.
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Logistic regression techniques used to identify interactions in gene-environment studies can produce both false-positive and false-negative findings.
“The use of logistic regression techniques to identify interactions in GxE studies is widespread, but it is important to note that this technique can lead to both false-positive and false-negative findings”
From Gene–environment interactions: early life stress and risk for depressive and anxiety disorders (2011) · cites this paper as Kraft et al. 2007
Artifactual interactions can occur from subtle changes in the definition and scaling of variables.
“Yet another concern when testing for interaction effects is the potential for artifactual interactions that can occur secondary to subtle changes in definition and scaling of the variables”
From Gene–environment interactions: early life stress and risk for depressive and anxiety disorders (2011) · cites this paper as Kraft et al. 2007
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“Yet another concern when testing for interaction effects is the potential for artifactual interactions that can occur secondary to subtle changes in definition and scaling of the variables (Jinks and Fulker 1970; Kraft et al. 2007; Mather and Jinks 1982; Moffit et al. 2006; Neale and Cardon 1992).”
BackgroundMethodHamza, T. H., Chen, H., Hill-Burns, E. M., Rhodes, S. L., Montimurro, J., Kay, D. M., Tenesa, A., Kusel, V. I., Sheehan, P., Eaaswarkhanth, M., et al. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1002237 · Cited by 218 · see citing works
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Self-citationBhattacharjee, S., Wang, Z., Ciampa, J., Kraft, P., Chanock, S., Yu, K., & Chatterjee, N. (2010) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2010.01.026 · Cited by 44 · see citing works
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- Authors
- Kraft, P., Yen, Y.-C., Stram, D. O., Morrison, J., & Gauderman, W. J.
- Affiliations
- Harvard University (US)
- University of Southern California (US)
- Venue
- Human Heredity
- Publisher
- S. Karger AG
- Published
- 2007
- Volume
- 63
- Issue
- 2
- Pages
- 111-119
- ISSN
- 0001-5652 (ISSN-L)
- 1423-0062
- OpenAlex
- W2093453252
- Cited by
- 439
- References
- 0
- Citations by year
- 2026: 9 · 2025: 10 · 2024: 25 · 2023: 18 · 2022: 12 · 2021: 33 · 2020: 24 · 2019: 18 · 2018: 23 · 2017: 26 · 2016: 26 · 2015: 18 · 2014: 43 · 2013: 34 · 2012: 25
- FWCI
- 12.37
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Human Heredity (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Cancer Institute (R01CA052862, P01 CA08796, R01 CA52862, U01CA098233)
- National Institute of Environmental Health Sciences (U01ES015090, P01 ES07048)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Health, Environment, Cognitive Aging
- Keywords
- gene-environment interaction, genetic association testing, complex diseases, case-control data, statistical power, sample size requirements, genetic susceptibility loci
- MeSH terms
- Environment, Gene Frequency, Genes, Humans, Models, Genetic, Sensitivity and Specificity, Likelihood Functions, Case-Control Studies, Sample Size, Penetrance, Genetic Predisposition to Disease, Genomics, Genetic Diseases, Inborn
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