Exploration of the utility of ancestry informative markers for genetic association studies of African Americans with type 2 diabetes and end stage renal disease
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Admixture and population stratification are major concerns in genetic association studies. We wished to evaluate the impact of admixture using empirically derived data from genetic association studies of African Americans (AA) with type 2 diabetes (T2DM) and end-stage renal disease (ESRD). Seventy ancestry informative markers (AIMs) were genotyped in 577 AA with T2DM–ESRD, 596 AA controls, 44 Yoruba Nigerian (YRI) and 39 European American (EA) controls. Genotypic data and association results for eight T2DM candidate gene studies in our AA population were included. Ancestral estimates were calculated using FRAPPE, ADMIXMAP and STRUCTURE for all AA samples, using varying numbers of AIMs (25, 50, and 70). Ancestry estimates varied significantly across all three programs with the highest estimates obtained using STRUCTURE, followed by ADMIXMAP; while FRAPPE estimates were the lowest. FRAPPE estimates were similar using varying numbers of AIMs, while STRUCTURE estimates using 25 AIMs differed from estimates using 50 and 70 AIMs. Female T2DM-ESRD cases showed higher mean African proportions as compared to female controls, male cases, and male controls. Age showed a weak but significant correlation with individual ancestral estimates in AA cases (r 2 = 0.101; P = 0.019) and in the combined set (r 2 = 0.131; P = 3.57 × 10−5). The absolute difference between frequencies in parental populations, absolute δ, was correlated with admixture impact for dominant, additive, and recessive genotypic models of association. This study presents exploratory analyses of the impact of admixture on studies of AA with T2DM-ESRD and supports the use of ancestral proportions as a means of reducing confounding effects due to admixture.
We thank the individuals recruited as cases and controls for their participation, recruiters Joyce Byers and Mitzie Spainhour, technician Candace Gordon, programmer Matt Stiegert, and Mark Hansen and colleagues at Illumina Inc. This work was supported by grants DK066358, DK072550, DK070941, the Wake Forest University General Clinical Research Center M01 RR07122, and a Career Development Award from the American Diabetes Association (MMS).
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