Dataset Information

HGDP and HapMap analysis by Ancestry Mapper reveals local and global population relationships.

ABSTRACT: Knowledge of human origins, migrations, and expansions is greatly enhanced by the availability of large datasets of genetic information from different populations and by the development of bioinformatic tools used to analyze the data. We present Ancestry Mapper, which we believe improves on existing methods, for the assignment of genetic ancestry to an individual and to study the relationships between local and global populations. The principle function of the method, named Ancestry Mapper, is to give each individual analyzed a genetic identifier, made up of just 51 genetic coordinates, that corresponds to its relationship to the HGDP reference population. As a consequence, the Ancestry Mapper Id (AMid) has intrinsic biological meaning and provides a tool to measure similarity between world populations. We applied Ancestry Mapper to a dataset comprised of the HGDP and HapMap data. The results show distinctions at the continental level, while simultaneously giving details at the population level. We clustered AMids of HGDP/HapMap and observe a recapitulation of human migrations: for a small number of clusters, individuals are grouped according to continental origins; for a larger number of clusters, regional and population distinctions are evident. Calculating distances between AMids allows us to infer ancestry. The number of coordinates is expandable, increasing the power of Ancestry Mapper. An R package called Ancestry Mapper is available to apply this method to any high density genomic data set.

SUBMITTER: Magalhaes TR

PROVIDER: S-EPMC3506643 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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HGDP and HapMap analysis by Ancestry Mapper reveals local and global population relationships.

Magalhães Tiago R TR Casey Jillian P JP Conroy Judith J Regan Regina R Fitzpatrick Darren J DJ Shah Naisha N Sobral João J Ennis Sean S

PloS one 20121126 11

Knowledge of human origins, migrations, and expansions is greatly enhanced by the availability of large datasets of genetic information from different populations and by the development of bioinformatic tools used to analyze the data. We present Ancestry Mapper, which we believe improves on existing methods, for the assignment of genetic ancestry to an individual and to study the relationships between local and global populations. The principle function of the method, named Ancestry Mapper, is t ...[more]

PMID: 23189146

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Project description:MotivationLocal ancestry analysis of genotype data from recently admixed populations (e.g. Latinos, African Americans) provides key insights into population history and disease genetics. Although methods for local ancestry inference have been extensively validated in simulations (under many unrealistic assumptions), no empirical study of local ancestry accuracy in Latinos exists to date. Hence, interpreting findings that rely on local ancestry in Latinos is challenging.ResultsHere, we use 489 nuclear families from the mainland USA, Puerto Rico and Mexico in conjunction with 3204 unrelated Latinos from the Multiethnic Cohort study to provide the first empirical characterization of local ancestry inference accuracy in Latinos. Our approach for identifying errors does not rely on simulations but on the observation that local ancestry in families follows Mendelian inheritance. We measure the rate of local ancestry assignments that lead to Mendelian inconsistencies in local ancestry in trios (MILANC), which provides a lower bound on errors in the local ancestry estimates. We show that MILANC rates observed in simulations underestimate the rate observed in real data, and that MILANC varies substantially across the genome. Second, across a wide range of methods, we observe that loci with large deviations in local ancestry also show enrichment in MILANC rates. Therefore, local ancestry estimates at such loci should be interpreted with caution. Finally, we reconstruct ancestral haplotype panels to be used as reference panels in local ancestry inference and show that ancestry inference is significantly improved by incoroprating these reference panels.Availability and implementationWe provide the reconstructed reference panels together with the maps of MILANC rates as a public resource for researchers analyzing local ancestry in Latinos at http://bogdanlab.pathology.ucla.eduSupplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

HGDP and HapMap analysis by Ancestry Mapper reveals local and global population relationships.

Publications

HGDP and HapMap analysis by Ancestry Mapper reveals local and global population relationships.

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