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Nat Commun. 2014 Apr 29;5:3513. doi: 10.1038/ncomms4513.

Geographic population structure analysis of worldwide human populations infers their biogeographical origins.

Author information

1
1] Department of Animal and Plant Sciences, University of Sheffield, Western Bank, Sheffield, S10 2TN, UK [2] Department of Mental Health, Johns Hopkins University Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, Maryland 21205, USA [3].
2
1] Department of Pediatrics, Keck School of Medicine and Children's Hospital Los Angeles, University of Southern California, 4650 Sunset Blvd, Los Angeles, California 90027, USA [2].
3
T.T. Chang Genetic Resources Center, International Rice Research Institute, Los Baños, Laguna , Philippines.
4
Department of Sciences of Life and Environment, University of Cagliari, SS 554, Monserrato 09042, Italy.
5
Research Laboratories, bcs Biotech S.r.l., Viale Monastir 112, Cagliari 09122, Italy.
6
Department of Biology, University of Pisa, Via Ghini 13, Pisa 56126, Italy.
7
Department of Science of Nature and Territory, University of Sassari, Località Piandanna 07100, Italy.
8
The Wellcome Trust Sanger Institute, Hinxton CB10 1SA, UK.
9
Department of Anthropology, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA.
10
Departamento de Toxicología, Cinvestav, San Pedro Zacatenco, CP 07360, Mexico.
11
Instituto de Genética y Biología Molecular, University of San Martin de Porres, Lima, Peru.
12
Departamento de Biologia Geral, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, CEP 31270-901, Brazil.
13
Institut de Biologia Evolutiva (CSIC-UPF), Departament de Ciences de la Salut i de la Vida, Universitat Pompeu Fabra, 08003 Barcelona, Spain.
14
1] Vavilov Institute for General Genetics: 119991, Moscow, Russia [2] Research Centre for Medical Genetics: 115478, Moscow, Russia.
15
Research Centre for Medical Genetics: 115478, Moscow, Russia.
16
The Lebanese American University, Chouran, Beirut 1102 2801, Lebanon.
17
National Health Laboratory Service, Sandringham 2131, Johannesburg, South Africa.
18
The Genographic Laboratory, School of Biological Sciences, Madurai Kamaraj University, Madurai 625 021, Tamil Nadu, India.
19
Department of ecology and evolutionary biology, University of Arizona, Tucson, Arizona 85721, USA.
20
Department of Anatomy, University of Otago, Dunedin 9054, New Zealand.
21
National Geographic Society, Washington, District of Columbia 20036, USA.

Abstract

The search for a method that utilizes biological information to predict humans' place of origin has occupied scientists for millennia. Over the past four decades, scientists have employed genetic data in an effort to achieve this goal but with limited success. While biogeographical algorithms using next-generation sequencing data have achieved an accuracy of 700 km in Europe, they were inaccurate elsewhere. Here we describe the Geographic Population Structure (GPS) algorithm and demonstrate its accuracy with three data sets using 40,000-130,000 SNPs. GPS placed 83% of worldwide individuals in their country of origin. Applied to over 200 Sardinians villagers, GPS placed a quarter of them in their villages and most of the rest within 50 km of their villages. GPS's accuracy and power to infer the biogeography of worldwide individuals down to their country or, in some cases, village, of origin, underscores the promise of admixture-based methods for biogeography and has ramifications for genetic ancestry testing.

PMID:
24781250
PMCID:
PMC4007635
DOI:
10.1038/ncomms4513
[Indexed for MEDLINE]
Free PMC Article

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