Elhaik, E. orcid.org/0000-0003-4795-1084, Tatarinova, T., Chebotarev, D. et al. (56 more authors) (2014) Geographic population structure analysis of worldwide human populations infers their biogeographical origins. Nature Communications, 5 (3513 ). ISSN 2041-1723
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2014 Macmillan Publishers Limited. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/ |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Biosciences (Sheffield) > Department of Animal and Plant Sciences (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 27 Jul 2017 10:48 |
Last Modified: | 08 Oct 2019 10:03 |
Published Version: | https://doi.org/10.1038/ncomms4513 |
Status: | Published |
Publisher: | Nature Publishing Group |
Refereed: | Yes |
Identification Number: | 10.1038/ncomms4513 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:119426 |
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