Predictions of native American population structure using linguistic covariates in a hidden regression framework.

<h4>Background</h4>The mainland of the Americas is home to a remarkable diversity of languages, and the relationships between genes and languages have attracted considerable attention in the past. Here we investigate to which extent geography and languages can predict the genetic structu...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Flora Jay, Olivier François, Michael G B Blum
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c36216e668a34631aff868d32326f884
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:c36216e668a34631aff868d32326f884
record_format	dspace
spelling	oai:doaj.org-article:c36216e668a34631aff868d32326f8842021-11-18T06:59:25ZPredictions of native American population structure using linguistic covariates in a hidden regression framework.1932-620310.1371/journal.pone.0016227https://doaj.org/article/c36216e668a34631aff868d32326f8842011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21305006/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>The mainland of the Americas is home to a remarkable diversity of languages, and the relationships between genes and languages have attracted considerable attention in the past. Here we investigate to which extent geography and languages can predict the genetic structure of Native American populations.<h4>Methodology/principal findings</h4>Our approach is based on a Bayesian latent cluster regression model in which cluster membership is explained by geographic and linguistic covariates. After correcting for geographic effects, we find that the inclusion of linguistic information improves the prediction of individual membership to genetic clusters. We further compare the predictive power of Greenberg's and The Ethnologue classifications of Amerindian languages. We report that The Ethnologue classification provides a better genetic proxy than Greenberg's classification at the stock and at the group levels. Although high predictive values can be achieved from The Ethnologue classification, we nevertheless emphasize that Choco, Chibchan and Tupi linguistic families do not exhibit a univocal correspondence with genetic clusters.<h4>Conclusions/significance</h4>The Bayesian latent class regression model described here is efficient at predicting population genetic structure using geographic and linguistic information in Native American populations.Flora JayOlivier FrançoisMichael G B BlumPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 1, p e16227 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Flora Jay Olivier François Michael G B Blum Predictions of native American population structure using linguistic covariates in a hidden regression framework.
description	<h4>Background</h4>The mainland of the Americas is home to a remarkable diversity of languages, and the relationships between genes and languages have attracted considerable attention in the past. Here we investigate to which extent geography and languages can predict the genetic structure of Native American populations.<h4>Methodology/principal findings</h4>Our approach is based on a Bayesian latent cluster regression model in which cluster membership is explained by geographic and linguistic covariates. After correcting for geographic effects, we find that the inclusion of linguistic information improves the prediction of individual membership to genetic clusters. We further compare the predictive power of Greenberg's and The Ethnologue classifications of Amerindian languages. We report that The Ethnologue classification provides a better genetic proxy than Greenberg's classification at the stock and at the group levels. Although high predictive values can be achieved from The Ethnologue classification, we nevertheless emphasize that Choco, Chibchan and Tupi linguistic families do not exhibit a univocal correspondence with genetic clusters.<h4>Conclusions/significance</h4>The Bayesian latent class regression model described here is efficient at predicting population genetic structure using geographic and linguistic information in Native American populations.
format	article
author	Flora Jay Olivier François Michael G B Blum
author_facet	Flora Jay Olivier François Michael G B Blum
author_sort	Flora Jay
title	Predictions of native American population structure using linguistic covariates in a hidden regression framework.
title_short	Predictions of native American population structure using linguistic covariates in a hidden regression framework.
title_full	Predictions of native American population structure using linguistic covariates in a hidden regression framework.
title_fullStr	Predictions of native American population structure using linguistic covariates in a hidden regression framework.
title_full_unstemmed	Predictions of native American population structure using linguistic covariates in a hidden regression framework.
title_sort	predictions of native american population structure using linguistic covariates in a hidden regression framework.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/c36216e668a34631aff868d32326f884
work_keys_str_mv	AT florajay predictionsofnativeamericanpopulationstructureusinglinguisticcovariatesinahiddenregressionframework AT olivierfrancois predictionsofnativeamericanpopulationstructureusinglinguisticcovariatesinahiddenregressionframework AT michaelgbblum predictionsofnativeamericanpopulationstructureusinglinguisticcovariatesinahiddenregressionframework
_version_	1718424131927539712

Predictions of native American population structure using linguistic covariates in a hidden regression framework.

Ejemplares similares