ArticleBMC genetics2010
Robust tests for matched case-control genetic association studies.
Article in BMC genetics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Identifying rare and common disease associated variants in genomic data using Parkinson's disease as a model.Journal of biomedical science · 2014Article
- A unifying framework for robust association testing, estimation, and genetic model selection using the generalized linear model.European journal of human genetics : EJHG · 2013Article
- Robust joint analysis with data fusion in two-stage quantitative trait genome-wide association studies.Computational and mathematical methods in medicine · 2013Article
- Further support to the uncoupling-to-survive theory: the genetic variation of human UCP genes is associated with longevity.PloS one · 2011Article
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2 authors.
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Abstract
backgroundThe Cochran-Armitage trend test (CATT) is powerful in detecting association between a susceptible marker and a disease. This test, however, may suffer from a substantial loss of power when the underlying genetic model is unknown and incorrectly specified. Thus, it is useful to derive tests obtaining the plausible power against all common genetic models. For this purpose, the genetic model selection (GMS) and genetic model exclusion (GME) methods were proposed recently. Simulation results showed that GMS and GME can obtain the plausible power against three common genetic models while the overall type I error is well controlled.
resultsAlthough GMS and GME are powerful statistically, they could be seriously affected by known confounding factors such as gender, age and race. Therefore, in this paper, via comparing the difference of Hardy-Weinberg disequilibrium coefficients between the cases and the controls within each sub-population, we propose the stratified genetic model selection (SGMS) and exclusion (SGME) methods which could eliminate the effect of confounding factors by adopting a matching framework. Our goal in this paper is to investigate the robustness of the proposed statistics and compare them with other commonly used efficiency robust tests such as MAX3 and χ2 with 2 degrees of freedom (df) test in matched case-control association designs through simulation studies.
conclusionSimulation results showed that if the mean genetic effect of the heterozygous genotype is between those of the two homozygous genotypes, then the proposed tests and MAX3 are preferred. Otherwise, χ2 with 2 df test may be used. To illustrate the robust procedures, the proposed tests are applied to a real matched pair case-control etiologic study of sarcoidosis.
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