 Excess false positive rate caused by population stratification and disease rate heterogeneity in case-control association studiesZhaohai Li, Hong Zhang, Gang Zheng, Joseph L. Gastwirth and Mitchell H. Gail Computational Statistics & Data Analysis, 2009, vol. 53, issue 5, 1767-1781 Abstract: Case-control association studies using unrelated cases and controls may suffer from potential confounding due to population stratification. Bias and variance distortion caused by population stratification in the commonly used allele-based tests can considerably inflate the Type I error rate. It is shown that the bias vanishes in the absence of disease rate heterogeneity. If only population stratification exists, a proper estimate of the variance of the allele-based test statistic is developed. Using this estimated variance yields a valid Type I error. However, when the frequencies of the allele under study and the disease rates differ among the subpopulations, it is difficult to correct for this bias. Explicit expressions for the excess false positive rate (EFPR) of the test due to bias and variance distortion are derived. It turns out that the bias created when both population stratification and disease rate heterogeneity are present usually has a greater effect on the EFPR than variance distortion. Comprehensive simulation studies strongly support these results. Date: 2009 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:53:y:2009:i:5:p:1767-1781 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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