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Testing for genetic association in the presence of population stratification in genome-wide association studies
Journal article   Peer reviewed

Testing for genetic association in the presence of population stratification in genome-wide association studies

Kai Wang
Genetic epidemiology, Vol.33(7), pp.637-645
11/2009
DOI: 10.1002/gepi.20415
PMID: 19235185

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Abstract

Genome-wide case-control association study is gaining popularity, thanks to the rapid development of modern genotyping technology. In such studies, population stratification is a potential concern especially when the number of study subjects is large as it can lead to seriously inflated false-positive rates. Current methods addressing this issue are still not completely immune to excess false positives. A simple method that corrects for population stratification is proposed. This method modifies a test statistic such as the Armitage trend test by using an additive constant that measures the variation of the effect size confounded by population stratification across genomic control (GC) markers. As a result, the original statistic is deflated by a multiplying factor that is specific to the marker being tested for association. This deflating multiplying factor is guaranteed to be larger than 1. These properties are in contrast to the conventional GC method where the original statistic is deflated by a common factor regardless of the marker being tested and the deflation factor may turn out to be less than 1. The new method is introduced first for regular case-control design and then for other situations such as quantitative traits and the presence of covariates. Extensive simulation study indicates that this new method provides an appealing alternative for genetic association analysis in the presence of population stratification.
 Genetics, Population Genomics Humans Genotype Statistics as Topic Models, Statistical False Positive Reactions Molecular Epidemiology - methods Case-Control Studies Phenotype Biostatistics - methods Computer Simulation Models, Genetic Polymorphism, Single Nucleotide Genome-Wide Association Study - methods Principal Component Analysis

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