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Improved correction for population stratification in genome‐wide association studies by identifying hidden population structures
Author(s) -
Li Qizhai,
Yu Kai
Publication year - 2008
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.20296
Subject(s) - population stratification , genome wide association study , genetic association , population , biology , association (psychology) , computational biology , stratification (seeds) , evolutionary biology , genetics , demography , gene , single nucleotide polymorphism , psychology , genotype , sociology , seed dormancy , botany , germination , dormancy , psychotherapist
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. Genet. Epidemiol . 2007. Published 2007 Wiley‐Liss, Inc.