Abstract
This study was designed to determine the ancestral composition of a multi-ethnic sample collected for studies of drug addictions in New York City and Las Vegas, and to examine the reliability of self-identified ethnicity and three-generation family history data. Ancestry biographical scores for seven clusters corresponding to world major geographical regions were obtained using STRUCTURE, based on genotypes of 168 ancestry informative markers (AIMs), for a sample of 1,291 African Americans (AA), European Americans (EA), and Hispanic Americans (HA) along with data from 1,051 HGDP-CEPH ‘diversity panel’ as a reference. Self-identified ethnicity and family history data, obtained in an interview, were accurate in identifying the individual major ancestry in the AA and the EA samples (approximately 99% and 95%, respectively) but were not useful for the HA sample and could not predict the extent of admixture in any group. The mean proportions of the combined clusters corresponding to European and Middle Eastern populations in the AA sample, revealed by AIMs analysis, were 0.13. The HA subjects, predominantly Puerto Ricans, showed a highly variable hybrid contribution pattern of clusters corresponding to Europe (0.27), Middle East (0.27), Africa (0.20), and Central Asia (0.14). The effect of admixture on allele frequencies is demonstrated for two single-nucleotide polymorphisms (118A > G, 17 C > T) of the mu opioid receptor gene (OPRM1). This study reiterates the importance of AIMs in defining ancestry, especially in admixed populations.
Highlights
The well-established genetic differences between ancestral populations may have an effect on disease prevalence and outcomes, as well as on drug response [1]
Based on the HGDP sample, the seven factors correspond to the geographical regions of Africa, Europe, Middle East, Central Asia, Far East Asia, Oceania, and America, and they are named by the geographical regions hereafter, for simplicity
In this study, we have used a panel of 168 ancestry informative markers (AIMs) to estimate the ancestry composition of a multi-ethnic US sample collected for studies of drug addictions in New York City and Las Vegas
Summary
The well-established genetic differences between ancestral populations may have an effect on disease prevalence and outcomes, as well as on drug response [1]. The presence of subgroups that differ in allele frequencies is relevant to public health and has numerous clinical implications. Analysis of population structure using a clustering algorithm can distinguish between populations based on DNA polymorphisms [1]. Admixture occurs when a new hybrid population is formed from formerly isolated populations [2]. Estimating the proportions of different ancestries in admixed populations is especially important in case– control association studies, since spurious associations may occur due to population substructure [3,4].
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