Literature DB >> 21184275

Investigating Hardy-Weinberg equilibrium in case-control or cohort studies or meta-analysis.

Andreas Ziegler1, Kristel Van Steen, Stefan Wellek.   

Abstract

Yu et al. (Breast Cancer Res Treat 117:675-677, 2009) recently stated that testing for deviation from Hardy-Weinberg equilibrium (HWE) is necessary to identify systematic genotyping errors in case-control studies. They criticized a meta-analytic study for the deviation from HWE in the case group of one study. The aim of this article is twofold. First, we derive recommendations on how to test for deviations from HWE in different study designs. Second, we develop a meta-analytic framework for assessing compatibility with HWE or measuring deviation from HWE. The authors sketch the possible reasons behind deviation from HWE and provide guidelines for proper investigation of HWE deviations in different study designs. The authors argue that the standard HWE χ² lack of fit test is logically flawed and provide a logically unflawed approach for measuring deviation from HWE using confidence intervals. The proposed method is applicable to meta-analyses of both case-control or cohort association studies. The proposed approach is illustrated using the meta-analysis criticized by Yu et al. Heterogeneity between studies can be assessed. The critique of Yu et al. to the article of Frank et al. (Breast Cancer Res Treat 111:139-144, 2008) can be refuted. Even more, validity of HWE can be proven for the pooled control sample. The authors advocate the use of a confidence interval-based approach to assess HWE. The latter should only be investigated in control populations. In multicenter studies or meta-analysis, deviation from HWE should be analyzed using a meta-analytic approach.

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Mesh:

Year:  2010        PMID: 21184275     DOI: 10.1007/s10549-010-1295-z

Source DB:  PubMed          Journal:  Breast Cancer Res Treat        ISSN: 0167-6806            Impact factor:   4.872


  31 in total

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10.  Study of the collagen type VI alpha 3 (COL6A3) gene in Parkinson's disease.

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Journal:  BMC Neurol       Date:  2021-05-08       Impact factor: 2.474

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