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

Author

Listed:
  • Eran Elhaik

    (University of Sheffield
    Johns Hopkins University Bloomberg School of Public Health)

  • Tatiana Tatarinova

    (Keck School of Medicine and Children’s Hospital Los Angeles, University of Southern California)

  • Dmitri Chebotarev

    (T.T. Chang Genetic Resources Center, International Rice Research Institute)

  • Ignazio S. Piras

    (University of Cagliari)

  • Carla Maria Calò

    (University of Cagliari)

  • Antonella De Montis

    (Research Laboratories, bcs Biotech S.r.l.)

  • Manuela Atzori

    (Research Laboratories, bcs Biotech S.r.l.)

  • Monica Marini

    (Research Laboratories, bcs Biotech S.r.l.)

  • Sergio Tofanelli

    (University of Pisa)

  • Paolo Francalacci

    (University of Sassari)

  • Luca Pagani

    (The Wellcome Trust Sanger Institute)

  • Chris Tyler-Smith

    (The Wellcome Trust Sanger Institute)

  • Yali Xue

    (The Wellcome Trust Sanger Institute)

  • Francesco Cucca

    (University of Cagliari)

  • Theodore G. Schurr

    (University of Pennsylvania)

  • Jill B. Gaieski

    (University of Pennsylvania)

  • Carlalynne Melendez

    (University of Pennsylvania)

  • Miguel G. Vilar

    (University of Pennsylvania)

  • Amanda C. Owings

    (University of Pennsylvania)

  • Rocío Gómez

    (Cinvestav, San Pedro Zacatenco)

  • Ricardo Fujita

    (Instituto de Genética y Biología Molecular, University of San Martin de Porres)

  • Fabrício R. Santos

    (Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais)

  • David Comas

    (Institut de Biologia Evolutiva (CSIC-UPF), Departament de Ciences de la Salut i de la Vida, Universitat Pompeu Fabra)

  • Oleg Balanovsky

    (Vavilov Institute for General Genetics
    Research Centre for Medical Genetics)

  • Elena Balanovska

    (Research Centre for Medical Genetics)

  • Pierre Zalloua

    (The Lebanese American University)

  • Himla Soodyall

    (National Health Laboratory Service, Sandringham 2131)

  • Ramasamy Pitchappan

    (The Genographic Laboratory, School of Biological Sciences, Madurai Kamaraj University)

  • ArunKumar GaneshPrasad

    (The Genographic Laboratory, School of Biological Sciences, Madurai Kamaraj University)

  • Michael Hammer

    (University of Arizona)

  • Lisa Matisoo-Smith

    (University of Otago)

  • R. Spencer Wells

    (National Geographic Society)

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.

Suggested Citation

  • Eran Elhaik & Tatiana Tatarinova & Dmitri Chebotarev & Ignazio S. Piras & Carla Maria Calò & Antonella De Montis & Manuela Atzori & Monica Marini & Sergio Tofanelli & Paolo Francalacci & Luca Pagani &, 2014. "Geographic population structure analysis of worldwide human populations infers their biogeographical origins," Nature Communications, Nature, vol. 5(1), pages 1-13, May.
    • Handle: RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms4513
    DOI: 10.1038/ncomms4513
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