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

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...

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Autores principales: Elhaik, Eran, Tatarinova, Tatiana, Chebotarev, Dmitri, Piras, Ignazio S., Maria Calò, Carla, De Montis, Antonella, Atzori, Manuela, Marini, Monica, Tofanelli, Sergio, Francalacci, Paolo, Pagani, Luca, Tyler-Smith, Chris, Xue, Yali, Cucca, Francesco, Schurr, Theodore G., Gaieski, Jill B., Melendez, Carlalynne, Vilar, Miguel G., Owings, Amanda C., Gómez, Rocío, Fujita, Ricardo, Santos, Fabrício R., Comas, David, Balanovsky, Oleg, Balanovska, Elena, Zalloua, Pierre, Soodyall, Himla, Pitchappan, Ramasamy, GaneshPrasad, ArunKumar, Hammer, Michael, Matisoo-Smith, Lisa, Wells, R. Spencer
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4007635/
https://www.ncbi.nlm.nih.gov/pubmed/24781250
http://dx.doi.org/10.1038/ncomms4513
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author Elhaik, Eran
Tatarinova, Tatiana
Chebotarev, Dmitri
Piras, Ignazio S.
Maria Calò, Carla
De Montis, Antonella
Atzori, Manuela
Marini, Monica
Tofanelli, Sergio
Francalacci, Paolo
Pagani, Luca
Tyler-Smith, Chris
Xue, Yali
Cucca, Francesco
Schurr, Theodore G.
Gaieski, Jill B.
Melendez, Carlalynne
Vilar, Miguel G.
Owings, Amanda C.
Gómez, Rocío
Fujita, Ricardo
Santos, Fabrício R.
Comas, David
Balanovsky, Oleg
Balanovska, Elena
Zalloua, Pierre
Soodyall, Himla
Pitchappan, Ramasamy
GaneshPrasad, ArunKumar
Hammer, Michael
Matisoo-Smith, Lisa
Wells, R. Spencer
author_facet Elhaik, Eran
Tatarinova, Tatiana
Chebotarev, Dmitri
Piras, Ignazio S.
Maria Calò, Carla
De Montis, Antonella
Atzori, Manuela
Marini, Monica
Tofanelli, Sergio
Francalacci, Paolo
Pagani, Luca
Tyler-Smith, Chris
Xue, Yali
Cucca, Francesco
Schurr, Theodore G.
Gaieski, Jill B.
Melendez, Carlalynne
Vilar, Miguel G.
Owings, Amanda C.
Gómez, Rocío
Fujita, Ricardo
Santos, Fabrício R.
Comas, David
Balanovsky, Oleg
Balanovska, Elena
Zalloua, Pierre
Soodyall, Himla
Pitchappan, Ramasamy
GaneshPrasad, ArunKumar
Hammer, Michael
Matisoo-Smith, Lisa
Wells, R. Spencer
author_sort Elhaik, Eran
collection PubMed
description 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.
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spelling pubmed-40076352014-05-13 Geographic population structure analysis of worldwide human populations infers their biogeographical origins Elhaik, Eran Tatarinova, Tatiana Chebotarev, Dmitri Piras, Ignazio S. Maria Calò, Carla De Montis, Antonella Atzori, Manuela Marini, Monica Tofanelli, Sergio Francalacci, Paolo Pagani, Luca Tyler-Smith, Chris Xue, Yali Cucca, Francesco Schurr, Theodore G. Gaieski, Jill B. Melendez, Carlalynne Vilar, Miguel G. Owings, Amanda C. Gómez, Rocío Fujita, Ricardo Santos, Fabrício R. Comas, David Balanovsky, Oleg Balanovska, Elena Zalloua, Pierre Soodyall, Himla Pitchappan, Ramasamy GaneshPrasad, ArunKumar Hammer, Michael Matisoo-Smith, Lisa Wells, R. Spencer Nat Commun Article 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. Nature Publishing Group 2014-04-29 /pmc/articles/PMC4007635/ /pubmed/24781250 http://dx.doi.org/10.1038/ncomms4513 Text en Copyright © 2014, Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved. http://creativecommons.org/licenses/by-nc-sa/3.0/ 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/
spellingShingle Article
Elhaik, Eran
Tatarinova, Tatiana
Chebotarev, Dmitri
Piras, Ignazio S.
Maria Calò, Carla
De Montis, Antonella
Atzori, Manuela
Marini, Monica
Tofanelli, Sergio
Francalacci, Paolo
Pagani, Luca
Tyler-Smith, Chris
Xue, Yali
Cucca, Francesco
Schurr, Theodore G.
Gaieski, Jill B.
Melendez, Carlalynne
Vilar, Miguel G.
Owings, Amanda C.
Gómez, Rocío
Fujita, Ricardo
Santos, Fabrício R.
Comas, David
Balanovsky, Oleg
Balanovska, Elena
Zalloua, Pierre
Soodyall, Himla
Pitchappan, Ramasamy
GaneshPrasad, ArunKumar
Hammer, Michael
Matisoo-Smith, Lisa
Wells, R. Spencer
Geographic population structure analysis of worldwide human populations infers their biogeographical origins
title Geographic population structure analysis of worldwide human populations infers their biogeographical origins
title_full Geographic population structure analysis of worldwide human populations infers their biogeographical origins
title_fullStr Geographic population structure analysis of worldwide human populations infers their biogeographical origins
title_full_unstemmed Geographic population structure analysis of worldwide human populations infers their biogeographical origins
title_short Geographic population structure analysis of worldwide human populations infers their biogeographical origins
title_sort geographic population structure analysis of worldwide human populations infers their biogeographical origins
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4007635/
https://www.ncbi.nlm.nih.gov/pubmed/24781250
http://dx.doi.org/10.1038/ncomms4513
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