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

Journal Article

TitleHGDP and HapMap analysis by Ancestry Mapper reveals local and global population relationships.
Publication TypeJournal Article
AuthorsMagalhaes, TR, Casey, JP, Conroy, J, Regan, R, Fitzpatrick, DJ, Shah, N, Sobral, J, Ennis, S
Year of Publication2012
JournalPLoS One
Date Published2012
KeywordsCluster Analysis, Computational Biology, Evolution, Molecular, Genetics, Population, HapMap Project, Human Genome Project, Human Migration, Humans, Internet, Polymorphism, Single Nucleotide, Population Groups

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.

Alternate JournalPLoS ONE
PubMed ID23189146
PubMed Central IDPMC3506643
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