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PSMIX: an R package for population structure inference via maximum likelihood method

BACKGROUND: Inference of population stratification and individual admixture from genetic markers is an integrative part of a study in diverse situations, such as association mapping and evolutionary studies. Bayesian methods have been proposed for population stratification and admixture inference us...

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Detalles Bibliográficos
Autores principales: Wu, Baolin, Liu, Nianjun, Zhao, Hongyu
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2006
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1550430/
https://www.ncbi.nlm.nih.gov/pubmed/16792813
http://dx.doi.org/10.1186/1471-2105-7-317
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author Wu, Baolin
Liu, Nianjun
Zhao, Hongyu
author_facet Wu, Baolin
Liu, Nianjun
Zhao, Hongyu
author_sort Wu, Baolin
collection PubMed
description BACKGROUND: Inference of population stratification and individual admixture from genetic markers is an integrative part of a study in diverse situations, such as association mapping and evolutionary studies. Bayesian methods have been proposed for population stratification and admixture inference using multilocus genotypes and widely used in practice. However, these Bayesian methods demand intensive computation resources and may run into convergence problem in Markov Chain Monte Carlo based posterior samplings. RESULTS: We have developed PSMIX, an R package based on maximum likelihood method using expectation-maximization algorithm, for inference of population stratification and individual admixture. CONCLUSION: Compared with software based on Bayesian methods (e.g., STRUCTURE), PSMIX has similar accuracy, but more efficient computations. PSMIX and its supplemental documents are freely available at .
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spelling pubmed-15504302006-08-18 PSMIX: an R package for population structure inference via maximum likelihood method Wu, Baolin Liu, Nianjun Zhao, Hongyu BMC Bioinformatics Software BACKGROUND: Inference of population stratification and individual admixture from genetic markers is an integrative part of a study in diverse situations, such as association mapping and evolutionary studies. Bayesian methods have been proposed for population stratification and admixture inference using multilocus genotypes and widely used in practice. However, these Bayesian methods demand intensive computation resources and may run into convergence problem in Markov Chain Monte Carlo based posterior samplings. RESULTS: We have developed PSMIX, an R package based on maximum likelihood method using expectation-maximization algorithm, for inference of population stratification and individual admixture. CONCLUSION: Compared with software based on Bayesian methods (e.g., STRUCTURE), PSMIX has similar accuracy, but more efficient computations. PSMIX and its supplemental documents are freely available at . BioMed Central 2006-06-22 /pmc/articles/PMC1550430/ /pubmed/16792813 http://dx.doi.org/10.1186/1471-2105-7-317 Text en Copyright © 2006 Wu et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Software
Wu, Baolin
Liu, Nianjun
Zhao, Hongyu
PSMIX: an R package for population structure inference via maximum likelihood method
title PSMIX: an R package for population structure inference via maximum likelihood method
title_full PSMIX: an R package for population structure inference via maximum likelihood method
title_fullStr PSMIX: an R package for population structure inference via maximum likelihood method
title_full_unstemmed PSMIX: an R package for population structure inference via maximum likelihood method
title_short PSMIX: an R package for population structure inference via maximum likelihood method
title_sort psmix: an r package for population structure inference via maximum likelihood method
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1550430/
https://www.ncbi.nlm.nih.gov/pubmed/16792813
http://dx.doi.org/10.1186/1471-2105-7-317
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