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Estimating ancestral proportions in a multi-ethnic US sample: implications for studies of admixed populations.

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.

SUBMITTER: Levran O

PROVIDER: S-EPMC3437566 | biostudies-literature | 2012 Jul

REPOSITORIES: biostudies-literature

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Estimating ancestral proportions in a multi-ethnic US sample: implications for studies of admixed populations.

Levran Orna O Awolesi Olaoluwakitan O Shen Pei-Hong PH Adelson Miriam M Kreek Mary Jeanne MJ

Human genomics 20120705

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), ...[more]

PMID: 23244743

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Project description:BackgroundAdmixed populations arise when two or more previously isolated populations interbreed. A powerful approach to addressing the genetic complexity in admixed populations is to infer ancestry. Ancestry inference including the proportion of an individual's genome coming from each population and its ancestral origin along the chromosome of an admixed population requires the use of ancestry informative markers (AIMs) from reference ancestral populations. AIMs exhibit substantial differences in allele frequency between ancestral populations. Given the huge amount of human genetic variation data available from diverse populations, a computationally feasible and cost-effective approach is becoming increasingly important to extract or filter AIMs with the maximum information content for ancestry inference, admixture mapping, forensic applications, and detecting genomic regions that have been under recent selection.ResultsTo address this gap, we present MI-MAAP, an easy-to-use web-based bioinformatics tool designed to prioritize informative markers for multi-ancestry admixed populations by utilizing feature selection methods and multiple genomics resources including 1000 Genomes Project and Human Genome Diversity Project. Specifically, this tool implements a novel allele frequency-based feature selection algorithm, Lancaster Estimator of Independence (LEI), as well as other genotype-based methods such as Principal Component Analysis (PCA), Support Vector Machine (SVM), and Random Forest (RF). We demonstrated that MI-MAAP is a useful tool in prioritizing informative markers and accurately classifying ancestral populations. LEI is an efficient feature selection strategy to retrieve ancestry informative variants with different allele frequency/selection pressure among (or between) ancestries without requiring computationally expensive individual-level genotype data.ConclusionsMI-MAAP has a user-friendly interface which provides researchers an easy and fast way to filter and identify AIMs. MI-MAAP can be accessed at https://research.cchmc.org/mershalab/MI-MAAP/login/.

Project description:Abstinence rates among smokers attempting to quit remain low despite the wide availability and accessibility of pharmacological smoking cessation treatments. In addition, the prevalence of cessation attempts and abstinence differs by individual-level social factors such as race and ethnicity. Clinical treatment of nicotine dependence also continues to be challenged by individual-level variability in effectiveness to promote abstinence. The use of tailored smoking cessation strategies that incorporate information on individual-level social and genetic factors hold promise, although additional pharmacogenomic knowledge is still needed. In particular, genetic variants associated with pharmacological responses to smoking cessation treatment have generally been conducted in populations with participants that self-identify as White race or who are determined to be of European genetic ancestry. These results may not adequately capture the variability across all smokers as a result of understudied differences in allele frequencies across genetic ancestry populations. This suggests that much of the current pharmacogenetic study results for smoking cessation may not apply to all populations. Therefore, clinical application of pharmacogenetic results may exacerbate health inequities by racial and ethnic groups. This scoping review examines the extent to which racial, ethnic, and ancestral groups that experience differences in smoking rates and smoking cessation are represented in the existing body of published pharmacogenetic studies of smoking cessation. We will summarize results by race, ethnicity, and ancestry across pharmacological treatments and study designs. We will also explore current opportunities and challenges in conducting pharmacogenomic research on smoking cessation that encourages greater participant diversity, including practical barriers to clinical utilization of pharmacological smoking cessation treatment and clinical implementation of pharmacogenetic knowledge.

Project description:It is important to study the genetics of complex traits in diverse populations. Here, we introduce covariate-adjusted linkage disequilibrium (LD) score regression (cov-LDSC), a method to estimate SNP-heritability (${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}})$ and its enrichment in homogenous and admixed populations with summary statistics and in-sample LD estimates. In-sample LD can be estimated from a subset of the genome-wide association studies samples, allowing our method to be applied efficiently to very large cohorts. In simulations, we show that unadjusted LDSC underestimates ${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}}$ by 10-60% in admixed populations; in contrast, cov-LDSC is robustly accurate. We apply cov-LDSC to genotyping data from 8124 individuals, mostly of admixed ancestry, from the Slim Initiative in Genomic Medicine for the Americas study, and to approximately 161 000 Latino-ancestry individuals, 47 000 African American-ancestry individuals and 135 000 European-ancestry individuals, as classified by 23andMe. We estimate ${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}}$ and detect heritability enrichment in three quantitative and five dichotomous phenotypes, making this, to our knowledge, the most comprehensive heritability-based analysis of admixed individuals to date. Most traits have high concordance of ${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}}$ and consistent tissue-specific heritability enrichment among different populations. However, for age at menarche, we observe population-specific heritability estimates of ${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}}$. We observe consistent patterns of tissue-specific heritability enrichment across populations; for example, in the limbic system for BMI, the per-standardized-annotation effect size $ \tau $* is 0.16 ± 0.04, 0.28 ± 0.11 and 0.18 ± 0.03 in the Latino-, African American- and European-ancestry populations, respectively. Our approach is a powerful way to analyze genetic data for complex traits from admixed populations.

Dataset Information

Estimating ancestral proportions in a multi-ethnic US sample: implications for studies of admixed populations.

Publications

Estimating ancestral proportions in a multi-ethnic US sample: implications for studies of admixed populations.

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