title: A mixed model reduces spurious genetic associations produced by population stratification in genome-wide association studies.
abstract: Population stratification can produce spurious genetic associations in genome-wide association studies (GWASs). Mixed model methodology has been regarded useful for correcting population stratification. This study explored statistical power and false discovery rate (FDR) with the data simulated for dichotomous traits. Empirical FDRs and powers were estimated using fixed models with and without genomic control and using mixed models with and without reflecting loci linked to the candidate marker in genetic relationships. Population stratification with admixture degree ranged from 1% to 10% resulted in inflated FDRs from the fixed model analysis without genomic control and decreased power from the fixed model analysis with genomic control (P<0.05). Meanwhile, population stratification could not change FDR and power estimates from the mixed model analyses (P>0.05). We suggest that the mixed model methodology was useful to reduce spurious genetic associations produced by population stratification in GWAS, even with a high degree of admixture (10%).
authors: Department of Bioinformatics and Life Science, Soongsil University, Seoul 156-743, Republic of Korea.
authors: Jimin
authors: J
authors: Shin
authors: Department of Bioinformatics and Life Science, Soongsil University, Seoul 156-743, Republic of Korea. Electronic address: clee@ssu.ac.kr.
authors: Chaeyoung
authors: C
authors: Lee
mesh_terms: D003198:Computer Simulation; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D044382:Population Groups / Q000235:genetics
keywords: False discovery; Genomic control; Mixed model; Population stratification; Statistical power