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Inference of structure in subdivided populations at low levels of genetic differentiation—the correlated allele frequencies model revisited
Author(s) -
G. Guillot
Publication year - 2008
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btn419
Subject(s) - inference , cluster analysis , markov chain monte carlo , population , bayesian inference , computer science , bayesian probability , statistics , data mining , machine learning , artificial intelligence , mathematics , demography , sociology
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.

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