[Show abstract][Hide abstract]ABSTRACT: Background:
Genetic ancestry is known to impact outcomes of genotype-phenotype studies that are designed to identify risk for common diseases in human populations. Failure to control for population stratification due to genetic ancestry can significantly confound results of disease association studies. Moreover, ancestry is a critical factor in assessing lifetime risk of disease, and can play an important role in optimizing treatment. As modern medicine moves towards using personal genetic information for clinical applications, it is important to determine genetic ancestry in an accurate, cost-effective and efficient manner. Self-identified race is a common method used to track and control for population stratification; however, social constructs of race are not necessarily informative for genetic applications. The use of ancestry informative markers (AIMs) is a more accurate method for determining genetic ancestry for the purposes of population stratification.
Here we introduce a novel panel of 36 microsatellite (MSAT) AIMs that determines continental admixture proportions. This panel, which we have named Continental Ancestry Informative Markers or CoAIMs, consists of MSAT AIMs that were chosen based upon their measure of genetic variance (F(st)), allele frequencies and their suitability for efficient genotyping. Genotype analysis using CoAIMs along with a Bayesian clustering method (STRUCTURE) is able to discern continental origins including Europe/Middle East (Caucasians), East Asia, Africa, Native America, and Oceania. In addition to determining continental ancestry for individuals without significant admixture, we applied CoAIMs to ascertain admixture proportions of individuals of self declared race.
CoAIMs can be used to efficiently and effectively determine continental admixture proportions in a sample set. The CoAIMs panel is a valuable resource for genetic researchers performing case-control genetic association studies, as it can control for the confounding effects of population stratification. The MSAT-based approach used here has potential for broad applicability as a cost effective tool toward determining admixture proportions.
[Show abstract][Hide abstract]ABSTRACT: PCA Plots From Analysis of the HGDP MSATs. (A) PC1 vs. PC3 and (B) PC2 vs. PC3 for the 78 MSAT marker set. (C) PC1 vs. PC3 and (D) PC2 vs. PC3 for the 48 MSAT marker set. (E) PC1 vs. PC3 and (F) PC2 vs. PC3 for the36 MSAT marker set. (G) PC1 vs. PC3 and (H) PC2 vs. PC3 for the 24 MSAT marker set. In all cases, the addition of the third PC does not allow for further separation of population cluster.
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[Show abstract][Hide abstract]ABSTRACT: PCA Plots of CoAIMs with the NIGMS and NHGRI Population Samples. Plot of PC1 vs. PC3 (A) and PC2 vs. PC3 (B) of the CoAIMs panel with the NIGMS and NHGRI population samples.
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[Show abstract][Hide abstract]ABSTRACT: PCA Plots of the Top Three PCs of the set of 19 MSATs (ABI identifiler and Coriell 6-plex). Plot of PC1 vs. PC3 (A) which captures 56.1% and 7.2% of the total variation of the data. Plot of PC2 vs. PC3 (B) which captures 22.9% and 7.2% of the total variation of the data.
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