Inference of structure in subdivided populations at low levels of genetic differentiation—the correlated allele frequencies model revisited
Abstract 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 dif...
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Online Access: | http://dx.doi.org/10.1093/bioinformatics/btn419 https://academic.oup.com/bioinformatics/article-pdf/24/19/2222/49049897/bioinformatics_24_19_2222.pdf |
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croxfordunivpr:10.1093/bioinformatics/btn419 2024-06-23T07:53:28+00:00 Inference of structure in subdivided populations at low levels of genetic differentiation—the correlated allele frequencies model revisited Guillot, Gilles 2008 http://dx.doi.org/10.1093/bioinformatics/btn419 https://academic.oup.com/bioinformatics/article-pdf/24/19/2222/49049897/bioinformatics_24_19_2222.pdf en eng Oxford University Press (OUP) Bioinformatics volume 24, issue 19, page 2222-2228 ISSN 1367-4811 1367-4803 journal-article 2008 croxfordunivpr https://doi.org/10.1093/bioinformatics/btn419 2024-06-11T04:17:28Z Abstract 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 Article in Journal/Newspaper Gulo gulo Oxford University Press Bioinformatics 24 19 2222 2228 |
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Oxford University Press |
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croxfordunivpr |
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English |
description |
Abstract 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 |
Article in Journal/Newspaper |
author |
Guillot, Gilles |
spellingShingle |
Guillot, Gilles Inference of structure in subdivided populations at low levels of genetic differentiation—the correlated allele frequencies model revisited |
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 (OUP) |
publishDate |
2008 |
url |
http://dx.doi.org/10.1093/bioinformatics/btn419 https://academic.oup.com/bioinformatics/article-pdf/24/19/2222/49049897/bioinformatics_24_19_2222.pdf |
genre |
Gulo gulo |
genre_facet |
Gulo gulo |
op_source |
Bioinformatics volume 24, issue 19, page 2222-2228 ISSN 1367-4811 1367-4803 |
op_doi |
https://doi.org/10.1093/bioinformatics/btn419 |
container_title |
Bioinformatics |
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24 |
container_issue |
19 |
container_start_page |
2222 |
op_container_end_page |
2228 |
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1802645148968943616 |