ArticleAmerican journal of human genetics2010
Using principal components of genetic variation for robust and powerful detection of gene-gene interactions in case-control and case-only studies.
Article in American journal of human genetics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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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
25 citing papers in PubMed, 44 citations in OpenAlex.
- The case-only design is a powerful approach to detect interactions but should be used with caution.BMC genomics · 2025Article
- Genetic interactions effects for cancer disease identification using computational models: a review.Medical & biological engineering & computing · 2021Review
- A Fast and Accurate Method for Genome-wide Scale Phenome-wide G × E Analysis and Its Application to UK Biobank.American journal of human genetics · 2019Article
- Personalized medicine: Genetic risk prediction of drug response.Pharmacology & therapeutics · 2017Review
- Article
- The role of linkage disequilibrium in case-only studies of gene-environment interactions.Human genetics · 2015Article
- Variation in predictive ability of common genetic variants by established strata: the example of breast cancer and age.Epidemiology (Cambridge, Mass.) · 2015Article
- A kernel regression approach to gene-gene interaction detection for case-control studies.Genetic epidemiology · 2013Article
- Gene-environment interactions in genome-wide association studies: current approaches and new directions.Journal of child psychology and psychiatry, and allied disciplines · 2013Review
- Environmental confounding in gene-environment interaction studies.American journal of epidemiology · 2013Article
- A kernel of truth: statistical advances in polygenic variance component models for complex human pedigrees.Advances in genetics · 2013Article
- Likelihood ratio test for detecting gene (G)-environment (E) interactions under an additive risk model exploiting G-E independence for case-control data.American journal of epidemiology · 2012Article
- Challenges and opportunities in genome-wide environmental interaction (GWEI) studies.Human genetics · 2012Review
- Invited commentary: GE-Whiz! Ratcheting gene-environment studies up to the whole genome and the whole exposome.American journal of epidemiology · 2012Article
- Gene-environment interactions in genome-wide association studies: a comparative study of tests applied to empirical studies of type 2 diabetes.American journal of epidemiology · 2012Article
- Rare and common variants: twenty arguments.Nature reviews. Genetics · 2012Review
- Improved statistics for genome-wide interaction analysis.PLoS genetics · 2012Article
- A sibling-augmented case-only approach for assessing multiplicative gene-environment interactions.American journal of epidemiology · 2011Article
- Large-scale exploration of gene-gene interactions in prostate cancer using a multistage genome-wide association study.Cancer research · 2011Article
- Family-based gene-by-environment interaction studies: revelations and remedies.Epidemiology (Cambridge, Mass.) · 2011Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
Many popular methods for exploring gene-gene interactions, including the case-only approach, rely on the key assumption that physically distant loci are in linkage equilibrium in the underlying population. These methods utilize the presence of correlation between unlinked loci in a disease-enriched sample as evidence of interactions among the loci in the etiology of the disease. We use data from the CGEMS case-control genome-wide association study of breast cancer to demonstrate empirically that the case-only and related methods have the potential to create large-scale false positives because of the presence of population stratification (PS) that creates long-range linkage disequilibrium in the genome. We show that the bias can be removed by considering parametric and nonparametric methods that assume gene-gene independence between unlinked loci, not in the entire population, but only conditional on population substructure that can be uncovered based on the principal components of a suitably large panel of PS markers. Applications in the CGEMS study as well as simulated data show that the proposed methods are robust to the presence of population stratification and are yet much more powerful, relative to standard logistic regression methods that are also commonly used as robust alternatives to the case-only type methods.
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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.