Inference of structure in subdivided populations at low levels of genetic differentiation--the correlated allele frequencies model revisited
Motivation: This article considers the problem of estimating population genetic subdivision from multilocus genotype data. A model is considered to make use of genotypes and possibly of spatial coordinates of sampled individuals. A particular attention is paid to the case of low genetic differentiat...
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fthighwire:oai:open-archive.highwire.org:bioinfo:24/19/2222 2023-05-15T16:32:20+02:00 Inference of structure in subdivided populations at low levels of genetic differentiation--the correlated allele frequencies model revisited Guillot, Gilles 2008-10-01 00:00:00.0 text/html http://bioinformatics.oxfordjournals.org/cgi/content/short/24/19/2222 https://doi.org/10.1093/bioinformatics/btn419 en eng Oxford University Press http://bioinformatics.oxfordjournals.org/cgi/content/short/24/19/2222 http://dx.doi.org/10.1093/bioinformatics/btn419 Copyright (C) 2008, Oxford University Press GENETICS AND POPULATION ANALYSIS TEXT 2008 fthighwire https://doi.org/10.1093/bioinformatics/btn419 2008-12-25T19:59:49Z Motivation: This article considers the problem of estimating population genetic subdivision from multilocus genotype data. A model is considered to make use of genotypes and possibly of spatial coordinates of sampled individuals. A particular attention is paid to the case of low genetic differentiation with the help of a previously described Bayesian clustering model where allele frequencies are assumed to be a priori correlated. Under this model, various problems of inference are considered, in particular the common and difficult, but still unaddressed, situation where the number of populations is unknown. Results: A Markov chain Monte Carlo algorithm and a new post-processing scheme are proposed. It is shown that they significantly improve the accuracy of previously existing algorithms in terms of estimated number of populations and estimated population membership. This is illustrated numerically with data simulated from the prior-likelihood model used in inference and also with data simulated from a Wright–Fisher model. Improvements are also illustrated on a real dataset of eighty-eight wolverines ( Gulo gulo ) genotyped at 10 microsatellites loci. The interest of the solutions presented here are not specific to any clustering model and are hence relevant to many settings in populations genetics where weakly differentiated populations are assumed or sought. Availability: The improvements implemented will be made available in version 3.0.0 of the R package Geneland. Informations on how to get and use the software are available from http://folk.uio.no/gillesg/Geneland.html . Supplementary information: http://folk.uio.no/gillesg/CFM/SuppMat.pdf Contact: gilles.guillot@bio.uio.no Text Gulo gulo HighWire Press (Stanford University) Bioinformatics 24 19 2222 2228 |
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HighWire Press (Stanford University) |
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English |
topic |
GENETICS AND POPULATION ANALYSIS |
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GENETICS AND POPULATION ANALYSIS Guillot, Gilles Inference of structure in subdivided populations at low levels of genetic differentiation--the correlated allele frequencies model revisited |
topic_facet |
GENETICS AND POPULATION ANALYSIS |
description |
Motivation: This article considers the problem of estimating population genetic subdivision from multilocus genotype data. A model is considered to make use of genotypes and possibly of spatial coordinates of sampled individuals. A particular attention is paid to the case of low genetic differentiation with the help of a previously described Bayesian clustering model where allele frequencies are assumed to be a priori correlated. Under this model, various problems of inference are considered, in particular the common and difficult, but still unaddressed, situation where the number of populations is unknown. Results: A Markov chain Monte Carlo algorithm and a new post-processing scheme are proposed. It is shown that they significantly improve the accuracy of previously existing algorithms in terms of estimated number of populations and estimated population membership. This is illustrated numerically with data simulated from the prior-likelihood model used in inference and also with data simulated from a Wright–Fisher model. Improvements are also illustrated on a real dataset of eighty-eight wolverines ( Gulo gulo ) genotyped at 10 microsatellites loci. The interest of the solutions presented here are not specific to any clustering model and are hence relevant to many settings in populations genetics where weakly differentiated populations are assumed or sought. Availability: The improvements implemented will be made available in version 3.0.0 of the R package Geneland. Informations on how to get and use the software are available from http://folk.uio.no/gillesg/Geneland.html . Supplementary information: http://folk.uio.no/gillesg/CFM/SuppMat.pdf Contact: gilles.guillot@bio.uio.no |
format |
Text |
author |
Guillot, Gilles |
author_facet |
Guillot, Gilles |
author_sort |
Guillot, Gilles |
title |
Inference of structure in subdivided populations at low levels of genetic differentiation--the correlated allele frequencies model revisited |
title_short |
Inference of structure in subdivided populations at low levels of genetic differentiation--the correlated allele frequencies model revisited |
title_full |
Inference of structure in subdivided populations at low levels of genetic differentiation--the correlated allele frequencies model revisited |
title_fullStr |
Inference of structure in subdivided populations at low levels of genetic differentiation--the correlated allele frequencies model revisited |
title_full_unstemmed |
Inference of structure in subdivided populations at low levels of genetic differentiation--the correlated allele frequencies model revisited |
title_sort |
inference of structure in subdivided populations at low levels of genetic differentiation--the correlated allele frequencies model revisited |
publisher |
Oxford University Press |
publishDate |
2008 |
url |
http://bioinformatics.oxfordjournals.org/cgi/content/short/24/19/2222 https://doi.org/10.1093/bioinformatics/btn419 |
genre |
Gulo gulo |
genre_facet |
Gulo gulo |
op_relation |
http://bioinformatics.oxfordjournals.org/cgi/content/short/24/19/2222 http://dx.doi.org/10.1093/bioinformatics/btn419 |
op_rights |
Copyright (C) 2008, Oxford University Press |
op_doi |
https://doi.org/10.1093/bioinformatics/btn419 |
container_title |
Bioinformatics |
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24 |
container_issue |
19 |
container_start_page |
2222 |
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2228 |
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1766022092938018816 |