Development of a panel of genome-wide ancestry informative markers to study admixture throughout the Americas

Most individuals throughout the Americas are admixed descendants of Native American, European, and African ancestors. Complex historical factors have resulted in varying proportions of ancestral contributions between individuals within and among ethnic groups. We developed a panel of 446 ancestry informative markers (AIMs) optimized to estimate ancestral proportions in individuals and populations throughout Latin America. We used genome-wide data from 953 individuals from diverse African, European, and Native American populations to select AIMs optimized for each of the three main continental populations that form the basis of modern Latin American populations. We selected markers on the basis of locus-specific branch length to be informative, well distributed throughout the genome, capable of being genotyped on widely available commercial platforms, and applicable throughout the Americas by minimizing within-continent heterogeneity. We then validated the panel in samples from four admixed populations by comparing ancestry estimates based on the AIMs panel to estimates based on genome-wide association study (GWAS) data. The panel provided balanced discriminatory power among the three ancestral populations and accurate estimates of individual ancestry proportions (R2 > 0.9 for ancestral components with significant between-subject variance). Finally, we genotyped samples from 18 populations from Latin America using the AIMs panel and estimated variability in ancestry within and between these populations. This panel and its reference genotype information will be useful resources to explore population history of admixture in Latin America and to correct for the potential effects of population stratification in admixed samples in the region.     

Integrative analysis of single nucleotide polymorphisms and gene expression efficiently distinguishes samples from closely related ethnic populations

ABSTRACT: BACKGROUND: Ancestry informative markers (AIMs) are a type of genetic marker that is informative for tracing the ancestral ethnicity of individuals. Application of AIMs has gained substantial attention in population genetics, forensic sciences, and medical genetics. Single nucleotide polymorphisms (SNPs), the materials of AIMs, are useful for classifying individuals from distinct continental origins but cannot discriminate individuals with subtle genetic differences from closely related ancestral lineages. Proof-of-principle studies have shown that gene expression (GE) also is a heritable human variation that exhibits differential intensity distributions among ethnic groups. GE supplies ethnic information supplemental to SNPs; this motivated us to integrate SNP and GE markers to construct AIM panels with a reduced number of required markers and provide high accuracy in ancestry inference. Few studies in the literature have considered GE in this aspect, and none have integrated SNP and GE markers to aid classification of samples from closely related ethnic populations. RESULTS: We integrated a forward variable selection procedure into flexible discriminant analysis to identify key SNP and/or GE markers with the highest cross-validation prediction accuracy. By analyzing genome-wide SNP and/or GE markers in 210 independent samples from four ethnic groups in the HapMap II Project, we found that average testing accuracies for a majority of classification analyses were quite high, except for SNP-only analyses that were performed to discern study samples containing individuals from two close Asian populations. The average testing accuracies ranged from 0.53 to 0.79 for SNP-only analyses and increased to around 0.90 when GE markers were integrated together with SNP markers for the classification of samples from closely related Asian populations. Compared to GE-only analyses, integrative analyses of SNP and GE markers showed comparable testing accuracies and a reduced number of selected markers in AIM panels. CONCLUSIONS: Integrative analysis of SNP and GE markers provides high-accuracy and/or cost-effective classification results for assigning samples from closely related or distantly related ancestral lineages to their original ancestral populations. User-friendly BIASLESS (Biomarkers Identification and Samples Subdivision) software was developed as an efficient tool for selecting key SNP and/or GE markers and then building models for sample subdivision. BIASLESS was programmed in R and R-GUI and is available online at http://www.stat.sinica.edu.tw/hsinchou/genetics/prediction/BIASLESS.htm.     

Examination of ancestry and ethnic affiliation using highly informative diallelic DNA markers: application to diverse and admixed populations and implications for clinical epidemiology and forensic medicine

We and others have identified several hundred ancestry informative markers (AIMs) with large allele frequency differences between different major ancestral groups. For this study, a panel of 199 widely distributed AIMs was used to examine a diverse set of 796 DNA samples including self-identified European Americans, West Africans, East Asians, Amerindians, African Americans, Mexicans, Mexican Americans, Puerto Ricans and South Asians. Analysis using a Bayesian clustering algorithm (STRUCTURE) showed grouping of individuals with similar ethnic identity without any identifier other than the AIMs genotyping and showed admixture proportions that clearly distinguished different individuals of mixed ancestry. Additional analyses showed that, for the majority of samples, the predicted ethnic identity corresponded with the self-identified ethnicity at high probability (P > 0.99). Overall, the study demonstrates that AIMs can provide a useful adjunct to forensic medicine, pharmacogenomics and disease studies in which major ancestry or ethnic affiliation might be linked to specific outcomes.Electronic Supplementary Material Supplementary material is available for this article at     

Comparison of three methods to estimate genetic ancestry and control for stratification in genetic association studies among admixed populations

Population stratification may confound the results of genetic association studies among unrelated individuals from admixed populations. Several methods have been proposed to estimate the ancestral information in admixed populations and used to adjust the population stratification in genetic association tests. We evaluate the performances of three different methods: maximum likelihood estimation, ADMIXMAP and Structure through various simulated data sets and real data from Latino subjects participating in a genetic study of asthma. All three methods provide similar information on the accuracy of ancestral estimates and control type I error rate at an approximately similar rate. The most important factor in determining accuracy of the ancestry estimate and in minimizing type I error rate is the number of markers used to estimate ancestry. We demonstrate that approximately 100 ancestry informative markers (AIMs) are required to obtain estimates of ancestry that correlate with correlation coefficients more than 0.9 with the true individual ancestral proportions. In addition, after accounting for the ancestry information in association tests, the excess of type I error rate is controlled at the 5% level when 100 markers are used to estimate ancestry. However, since the effect of admixture on the type I error rate worsens with sample size, the accuracy of ancestry estimates also needs to increase to make the appropriate correction. Using data from the Latino subjects, we also apply these methods to an association study between body mass index and 44 AIMs. These simulations are meant to provide some practical guidelines for investigators conducting association studies in admixed populations.     

Rapid Assessment of Genetic Ancestry in Populations of Unknown Origin by Genome-Wide Genotyping of Pooled Samples

As we move forward from the current generation of genome-wide association (GWA) studies, additional cohorts of different ancestries will be studied to increase power, fine map association signals, and generalize association results to additional populations. Knowledge of genetic ancestry as well as population substructure will become increasingly important for GWA studies in populations of unknown ancestry. Here we propose genotyping pooled DNA samples using genome-wide SNP arrays as a viable option to efficiently and inexpensively estimate admixture proportion and identify ancestry informative markers (AIMs) in populations of unknown origin. We constructed DNA pools from African American, Native Hawaiian, Latina, and Jamaican samples and genotyped them using the Affymetrix 6.0 array. Aided by individual genotype data from the African American cohort, we established quality control filters to remove poorly performing SNPs and estimated allele frequencies for the remaining SNPs in each panel. We then applied a regression-based method to estimate the proportion of admixture in each cohort using the allele frequencies estimated from pooling and populations from the International HapMap Consortium as reference panels, and identified AIMs unique to each population. In this study, we demonstrated that genotyping pooled DNA samples yields estimates of admixture proportion that are both consistent with our knowledge of population history and similar to those obtained by genotyping known AIMs. Furthermore, through validation by individual genotyping, we demonstrated that pooling is quite effective for identifying SNPs with large allele frequency differences (i.e., AIMs) and that these AIMs are able to differentiate two closely related populations (HapMap JPT and CHB).     

30个祖先信息位点的筛选及应用

摘要：目的 筛选一组祖先信息SNPs位点（AIMs,Ancestry Informative Markers）,构建复合检测体系,用于东亚、欧洲和非洲人群遗传成分描述及个体种族来源推断。方法 以HapMap数据库9个人群的658份样本的分型数据为基础,从30个表型相关基因总共282个SNPs位点中筛选出30个AIMs位点,基于微测序-通用芯片技术构建复合检测体系,并建立人群等位基因频率数据库。使用这组位点分析HapMap数据库中658份人群样本,初步验证位点的区分效能;然后,使用研究构建的体系检验收集的5个人群194份无关个体的DNA样本。最后,通过Structure软件分析获取人群的成分构成以及个体的遗传成分,对个体样本进行种族来源推断。 结果 筛选的30个AIMs位点符合哈迪温伯格平衡（p>0.01）,位点之间没有连锁（r2<0.1）, 658份HapMap数据库样本和194份实验样本的祖先成分分析结果与已知结果完全一致。 结论 本文筛选并建立的30个AIMs位点复合检测体系,能够有效实现东亚、欧洲、非洲人群及混合人群的成分构成和个体遗传成分的分析,有效控制遗传连锁分析中由于人群分层现象带来的误差,也可以用于法医DNA检验中个体祖先来源推断。     

Reconstructing genetic ancestry blocks in admixed individuals

A chromosome in an individual of recently admixed ancestry resembles a mosaic of chromosomal segments, or ancestry blocks, each derived from a particular ancestral population. We consider the problem of inferring ancestry along the chromosomes in an admixed individual and thereby delineating the ancestry blocks. Using a simple population model, we infer gene-flow history in each individual. Compared with existing methods, which are based on a hidden Markov model, the Markov-hidden Markov model (MHMM) we propose has the advantage of accounting for the background linkage disequilibrium (LD) that exists in ancestral populations. When there are more than two ancestral groups, we allow each ancestral population to admix at a different time in history. We use simulations to illustrate the accuracy of the inferred ancestry as well as the importance of modeling the background LD; not accounting for background LD between markers may mislead us to false inferences about mixed ancestry in an indigenous population. The MHMM makes it possible to identify genomic blocks of a particular ancestry by use of any high-density single-nucleotide-polymorphism panel. One application of our method is to perform admixture mapping without genotyping special ancestry-informative-marker panels.     

A Panel of Ancestry Informative Markers for the Complex Five-Way Admixed South African Coloured Population

Admixture is a well known confounder in genetic association studies. If genome-wide data is not available, as would be the case for candidate gene studies, ancestry informative markers (AIMs) are required in order to adjust for admixture. The predominant population group in the Western Cape, South Africa, is the admixed group known as the South African Coloured (SAC). A small set of AIMs that is optimized to distinguish between the five source populations of this population (African San, African non-San, European, South Asian, and East Asian) will enable researchers to cost-effectively reduce false-positive findings resulting from ignoring admixture in genetic association studies of the population. Using genome-wide data to find SNPs with large allele frequency differences between the source populations of the SAC, as quantified by Rosenberg et. al''s -statistic, we developed a panel of AIMs by experimenting with various selection strategies. Subsets of different sizes were evaluated by measuring the correlation between ancestry proportions estimated by each AIM subset with ancestry proportions estimated using genome-wide data. We show that a panel of 96 AIMs can be used to assess ancestry proportions and to adjust for the confounding effect of the complex five-way admixture that occurred in the South African Coloured population.     

Genetic structure analyses and ancestral information inference of Chinese Kyrgyz group via a panel of 39 AIM-DIPs

Ancestry informative markers have extensive uses and advantages in inferring ancestral origins and estimating ancestral genetic information components of admixed populations. With the characteristics of highly cultural exchange and the admixed genetic structure of the Kyrgyz group, it is essential to enrich the genetic data of the Kyrgyz group. In this study, we used a self-developed ancestry informative marker-deletion/insertion polymorphic (AIM-DIP) panel to explore ancestral components of Chinese Kyrgyz group and population genetic relationships between the Kyrgyz group and reference populations. Results showed that all AIM-DIP loci were conformed to Hardy-Weinberg equilibrium. There were 36 AIM-DIP loci that contributed significantly to genetic information inference. Multiple statistical analyses revealed that Chinese Kyrgyz group had a closer genetic relationship with Chinese Uyghur group. The ancestral components of the Kyrgyz group, being mostly composed of genetic components of European and East Asian populations, were more similar to the ancestral components of Chinese Uyghur group.     

Robust estimation of local genetic ancestry in admixed populations using a nonparametric bayesian approach

We present a new haplotype-based approach for inferring local genetic ancestry of individuals in an admixed population. Most existing approaches for local ancestry estimation ignore the latent genetic relatedness between ancestral populations and treat them as independent. In this article, we exploit such information by building an inheritance model that describes both the ancestral populations and the admixed population jointly in a unified framework. Based on an assumption that the common hypothetical founder haplotypes give rise to both the ancestral and the admixed population haplotypes, we employ an infinite hidden Markov model to characterize each ancestral population and further extend it to generate the admixed population. Through an effective utilization of the population structural information under a principled nonparametric Bayesian framework, the resulting model is significantly less sensitive to the choice and the amount of training data for ancestral populations than state-of-the-art algorithms. We also improve the robustness under deviation from common modeling assumptions by incorporating population-specific scale parameters that allow variable recombination rates in different populations. Our method is applicable to an admixed population from an arbitrary number of ancestral populations and also performs competitively in terms of spurious ancestry proportions under a general multiway admixture assumption. We validate the proposed method by simulation under various admixing scenarios and present empirical analysis results from a worldwide-distributed dataset from the Human Genome Diversity Project.     

Ancestral informative marker selection and population structure visualization using sparse Laplacian eigenfunctions

Identification of a small panel of population structure informative markers can reduce genotyping cost and is useful in various applications, such as ancestry inference in association mapping, forensics and evolutionary theory in population genetics. Traditional methods to ascertain ancestral informative markers usually require the prior knowledge of individual ancestry and have difficulty for admixed populations. Recently Principal Components Analysis (PCA) has been employed with success to select SNPs which are highly correlated with top significant principal components (PCs) without use of individual ancestral information. The approach is also applicable to admixed populations. Here we propose a novel approach based on our recent result on summarizing population structure by graph laplacian eigenfunctions, which differs from PCA in that it is geometric and robust to outliers. Our approach also takes advantage of the priori sparseness of informative markers in the genome. Through simulation of a ring population and the real global population sample HGDP of 650K SNPs genotyped in 940 unrelated individuals, we validate the proposed algorithm at selecting most informative markers, a small fraction of which can recover the similar underlying population structure efficiently. Employing a standard Support Vector Machine (SVM) to predict individuals' continental memberships on HGDP dataset of seven continents, we demonstrate that the selected SNPs by our method are more informative but less redundant than those selected by PCA. Our algorithm is a promising tool in genome-wide association studies and population genetics, facilitating the selection of structure informative markers, efficient detection of population substructure and ancestral inference.     

Admixture analysis of a rural population of the state of Guerrero, Mexico

We studied 156 individuals of Native American descent from the city of Tlapa in the state of Guerrero in western Mexico. Most individuals' ethnicity was either Nahua, Mixtec, or Tlapanec, but self-identified Mestizos and individuals of mixed ethnicities were also included in the sample. We typed 24 autosomal, one Y-chromosome, and four mitochondrial ancestry-informative markers (AIMs) to estimate group and individual admixture proportions, and determine whether the admixture process involved directional gene flow between parental groups. When genetically defined (GD) Mestizos were excluded from the analysis, Native American ancestry represented approximately 98% of the population's gene pool, while European and West African ancestry represented approximately 1% each. Maternally inherited markers also showed an exceptionally high Native American contribution (98.5%), as did the paternally inherited marker, DYS199 (90.7%). We did not detect genetic structure in this population using these AIMs, which appears consistent with the homogeneity of the sample in terms of admixture proportions. The addition of GD Mestizos to the sample did not produce a considerable change in admixture estimates, but it had a major effect on population structure. These results show that the population of Tlapa in Guerrero, Mexico, has experienced little admixture with Europeans and/or West Africans. They also show that the impact of a small number of admixed individuals on an otherwise homogeneous population might have profound implications on subsequent ancestry/phenotype analysis and mapping strategies. We suggest that heterogeneity is a major characteristic of Mexican populations and, as a consequence, should not be disregarded when designing epidemiological studies of Mexican and Mexican American populations.     

Mexican American ancestry-informative markers: examination of population structure and marker characteristics in European Americans, Mexican Americans, Amerindians and Asians

Markers with large differences in allele frequencies between ethnicities provide ancestry information that can be applied to genetic studies. We identified over 100 biallelic ancestry informative markers (AIMs) with large allele frequency differences between European Americans (EA) and Pima Amerindians from laboratory and database screens. For 35 of these markers, Mayan, Yavapai and Quechuan Amerindians were genotyped and compared with EA and Pima allele frequencies. Markers with large allele frequency differences between EA and one Amerindian tribe showed only small differences between the Amerindian tribes. Examination of structure in individuals demonstrated a clear separation of subjects of European from those of Amerindian ancestry, and similarity between individuals from disparate Amerindian populations. The AIMs demonstrated the variation in ancestral composition of individual Mexican Americans, providing evidence of applicability in admixture mapping and in controlling for structure in association tests. In addition, a high percentage of single-nucleotide polymorphisms (SNPs) selected on the basis of large frequency differences between EA and Asian populations had large allele frequency differences between EA and Amerindians, suggesting an efficient method for greatly expanding AIMs for use in admixture mapping/structure analysis in Mexican Americans. Together, these data provide additional support for the practical application of admixture mapping in the Mexican American population.Electronic Supplementary Material Supplementary material is available in the online version of this article at     

Comparing genetic ancestry and self-reported race/ethnicity in a multiethnic population in New York City

Self-reported race/ethnicity is frequently used in epidemiological studies to assess an individual’s background origin. However, in admixed populations such as Hispanic, self-reported race/ethnicity may not accurately represent them genetically because they are admixed with European, African and Native American ancestry. We estimated the proportions of genetic admixture in an ethnically diverse population of 396 mothers and 188 of their children with 35 ancestry informative markers (AIMs) using the STRUCTURE version 2.2 program. The majority of the markers showed significant deviation from Hardy-Weinberg equilibrium in our study population. In mothers self-identified as Black and White, the imputed ancestry proportions were 77.6% African and 75.1% European respectively, while the racial composition among self-identified Hispanics was 29.2% European, 26.0% African, and 44.8% Native American. We also investigated the utility of AIMs by showing the improved fitness of models in paraoxanase-1 genotype-phenotype associations after incorporating AIMs; however, the improvement was moderate at best. In summary, a minimal set of 35 AIMs is sufficient to detect population stratification and estimate the proportion of individual genetic admixture; however, the utility of these markers remains questionable.     

Statistical tests for admixture mapping with case-control and cases-only data

Admixture mapping is a promising new tool for discovering genes that contribute to complex traits. This mapping approach uses samples from recently admixed populations to detect susceptibility loci at which the risk alleles have different frequencies in the original contributing populations. Although the idea for admixture mapping has been around for more than a decade, the genomic tools are only now becoming available to make this a feasible and attractive option for complex-trait mapping. In this article, we describe new statistical methods for analyzing multipoint data from admixture-mapping studies to detect "ancestry association." The new test statistics do not assume a particular disease model; instead, they are based simply on the extent to which the sample's ancestry proportions at a locus deviate from the genome average. Our power calculations show that, for loci at which the underlying risk-allele frequencies are substantially different in the ancestral populations, the power of admixture mapping can be comparable to that of association mapping but with a far smaller number of markers. We also show that, although "ancestry informative markers" (AIMs) are superior to random single-nucleotide polymorphisms (SNPs), random SNPs can perform quite well when AIMs are not available. Hence, researchers who study admixed populations in which AIMs are not available can perform admixture mapping with the use of modestly higher densities of random markers. Software to perform the gene-mapping calculations, "MALDsoft," is freely available on the Pritchard Lab Web site.     

North African influences and potential bias in case-control association studies in the Spanish population

Background

Despite the limited genetic heterogeneity of Spanish populations, substantial evidences support that historical African influences have not affected them uniformly. Accounting for such population differences might be essential to reduce spurious results in association studies of genetic factors with disease. Using ancestry informative markers (AIMs), we aimed to measure the African influences in Spanish populations and to explore whether these might introduce statistical bias in population-based association studies.

Methodology/Principal Findings

We genotyped 93 AIMs in Spanish (from the Canary Islands and the Iberian Peninsula) and Northwest Africans, and conducted population and individual-based clustering analyses along with reference data from the HapMap, HGDP-CEPH, and other sources. We found significant differences for the Northwest African influence among Spanish populations from as low as ≈5% in Spanish from the Iberian Peninsula to as much as ≈17% in Canary Islanders, whereas the sub-Saharan African influence was negligible. Strikingly, the Northwest African ancestry showed a wide inter-individual variation in Canary Islanders ranging from 0% to 96%, reflecting the violent way the Islands were conquered and colonized by the Spanish in the XV century. As a consequence, a comparison of allele frequencies between Spanish samples from the Iberian Peninsula and the Canary Islands evidenced an excess of markers with significant differences. However, the inflation of p-values for the differences was adequately controlled by correcting for genetic ancestry estimates derived from a reduced number of AIMs.

Conclusions/Significance

Although the African influences estimated might be biased due to marker ascertainment, these results confirm that Northwest African genetic footprints are recognizable nowadays in the Spanish populations, particularly in Canary Islanders, and that the uneven African influences existing in these populations might increase the risk for false positives in association studies. Adjusting for population stratification assessed with a few dozen AIMs would be sufficient to control this effect.     

Development of two multiplex mini-sequencing panels of ancestry informative SNPs for studies in Latin Americans: an application to populations of the State of Minas Gerais (Brazil)

Admixture occurs when individuals from parental populations that have been isolated for hundreds of generations form a new hybrid population. Currently, interest in measuring biogeographic ancestry has spread from anthropology to forensic sciences, direct-to-consumers personal genomics, and civil rights issues of minorities, and it is critical for genetic epidemiology studies of admixed populations. Markers with highly differentiated frequencies among human populations are informative of ancestry and are called ancestry informative markers (AIMs). For tri-hybrid Latin American populations, ancestry information is required for Africans, Europeans and Native Americans. We developed two multiplex panels of AIMs (for 14 SNPs) to be genotyped by two mini-sequencing reactions, suitable for investigators of medium-small laboratories to estimate admixture of Latin American populations. We tested the performance of these AIMs by comparing results obtained with our 14 AIMs with those obtained using 108 AIMs genotyped in the same individuals, for which DNA samples is available for other investigators. We emphasize that this type of comparison should be made when new admixture/population structure panels are developed. At the population level, our 14 AIMs were useful to estimate European admixture, though they overestimated African admixture and underestimated Native American admixture. Combined with more AIMs, our panel could be used to infer individual admixture. We used our panel to infer the pattern of admixture in two urban populations (Montes Claros and Manhua?u) of the State of Minas Gerais (southeastern Brazil), obtaining a snapshot of their genetic structure in the context of their demographic history.     

Dissecting linkage disequilibrium in African-American genomes: roles of markers and individuals

Substantial increases of linkage disequilibrium (LD) both in magnitude and in range have been observed in recently admixed populations such as African-American (AfA). On the other hand, it has also been shown that LD in AfAs was very similar to that of African. In this study, we attempted to resolve these contradicting observations by conducting a systematic examination of the LD structure in AfAs by genotyping a sample of AfA individuals at 24,341 single nucleotide polymorphisms (SNPs) spanning almost the entire chromosome 21, with an average density of 1.5 kb/SNP. The overall LD in AfAs is similar to that in African populations and much less than that in European populations. Even when the ancestry-informative markers (AIMs) were used, extended LD in AfA was found to be limited to certain magnitude range (0.2 < or = r(2) < or = 0.8) and certain distance range, that is, between-marker distance more than 200 kb. Furthermore, the inclusion of AfA individuals with predominant African ancestry was found to reduce the overall magnitude of LD. Elevation of LD in the AfA population, compared with its parental populations, can only be observed at the markers with large allele frequency differences between 2 parental populations at limited scenario. AfA individuals of wholly African ancestry contribute little to the extended LD in the AfA population, and further genotyping or association analysis conducted using only admixed individuals may lead to higher statistical power and possibly reduced cost.     

A genomewide admixture mapping panel for Hispanic/Latino populations

Admixture mapping (AM) is a promising method for the identification of genetic risk factors for complex traits and diseases showing prevalence differences among populations. Efficient application of this method requires the use of a genomewide panel of ancestry-informative markers (AIMs) to infer the population of origin of chromosomal regions in admixed individuals. Genomewide AM panels with markers showing high frequency differences between West African and European populations are already available for disease-gene discovery in African Americans. However, no such a map is yet available for Hispanic/Latino populations, which are the result of two-way admixture between Native American and European populations or of three-way admixture of Native American, European, and West African populations. Here, we report a genomewide AM panel with 2,120 AIMs showing high frequency differences between Native American and European populations. The average intermarker genetic distance is ~1.7 cM. The panel was identified by genotyping, with the Affymetrix GeneChip Human Mapping 500K array, a population sample with European ancestry, a Mesoamerican sample comprising Maya and Nahua from Mexico, and a South American sample comprising Aymara/Quechua from Bolivia and Quechua from Peru. The main criteria for marker selection were both high information content for Native American/European ancestry (measured as the standardized variance of the allele frequencies, also known as "f value") and small frequency differences between the Mesoamerican and South American samples. This genomewide AM panel will make it possible to apply AM approaches in many admixed populations throughout the Americas.     

Skin pigmentation,biogeographical ancestry and admixture mapping

Ancestry informative markers (AIMs) are genetic loci showing alleles with large frequency differences between populations. AIMs can be used to estimate biogeographical ancestry at the level of the population, subgroup (e.g. cases and controls) and individual. Ancestry estimates at both the subgroup and individual level can be directly instructive regarding the genetics of the phenotypes that differ qualitatively or in frequency between populations. These estimates can provide a compelling foundation for the use of admixture mapping (AM) methods to identify the genes underlying these traits. We present details of a panel of 34 AIMs and demonstrate how such studies can proceed, by using skin pigmentation as a model phenotype. We have genotyped these markers in two population samples with primarily African ancestry, viz. African Americans from Washington D.C. and an African Caribbean sample from Britain, and in a sample of European Americans from Pennsylvania. In the two African population samples, we observed significant correlations between estimates of individual ancestry and skin pigmentation as measured by reflectometry (R(2)=0.21, P<0.0001 for the African-American sample and R(2)=0.16, P<0.0001 for the British African-Caribbean sample). These correlations confirm the validity of the ancestry estimates and also indicate the high level of population structure related to admixture, a level that characterizes these populations and that is detectable by using other tests to identify genetic structure. We have also applied two methods of admixture mapping to test for the effects of three candidate genes (TYR, OCA2, MC1R) on pigmentation. We show that TYR and OCA2 have measurable effects on skin pigmentation differences between the west African and west European parental populations. This work indicates that it is possible to estimate the individual ancestry of a person based on DNA analysis with a reasonable number of well-defined genetic markers. The implications and applications of ancestry estimates in biomedical research are discussed.