Improved correction for population stratification in genome-wide association studies by identifying hidden population structures.
Hidden population substructure can cause population stratification and lead to false-positive findings in population-based genome-wide association (GWA) studies. Given a large panel of markers scanned in a GWA study, it becomes increasingly feasible to uncover the hidden population substructure within the study sample based on measured genotypes across the genome. Recognizing that population substructure can be displayed as clustered and/or continuous patterns of genetic variation, we propose a method that aims at the detection and correction of the confounding effect resulting from both patterns of population substructure. The proposed method is an extension of the EIGENSTRAT method (Price et al. [2006] Nat Genet 38:904-909). This approach is computationally feasible and easily applied to large-scale GWA studies. We show through simulation studies that, compared with the EIGENSTRAT method, the new method requires a smaller number of markers and yields a more appropriate correction for population stratification.
Li
Qizhai
Q
Division of Cancer Epidemiology and Genetics, NCI, NIH, Bethesda, Maryland.
Yu
Kai
K
D016000:Cluster Analysis; D003198:Computer Simulation; D030541:Databases, Genetic; D005819:Genetic Markers; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D005828:Genetics, Population; D015894:Genome, Human / Q000235:genetics*; D006801:Humans; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide
D005819:Genetic Markers