title: Testing for genetic association in the presence of population stratification in genome-wide association studies.
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.
authors.affiliation: Department of Biostatistics, College of Public Health, The University of Iowa, Iowa City, Iowa 52242, USA. kai-wang@uiowa.edu
authors.forename: Kai
authors.initials: K
authors.lastname: Wang
mesh_terms: D056808:Biostatistics / Q000379:methods; D016022:Case-Control Studies; D003198:Computer Simulation; D005189:False Positive Reactions; D005828:Genetics, Population; D055106:Genome-Wide Association Study / Q000379:methods*; D023281:Genomics; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D017720:Molecular Epidemiology / Q000379:methods; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D025341:Principal Component Analysis; D013223:Statistics as Topic