Bayesian Analysis of Genetic Differentiation Between Populations

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ID: 301794
2003
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Abstract
Abstract We introduce a Bayesian method for estimating hidden population substructure using multilocus molecular markers and geographical information provided by the sampling design. The joint posterior distribution of the substructure and allele frequencies of the respective populations is available in an analytical form when the number of populations is small, whereas an approximation based on a Markov chain Monte Carlo simulation approach can be obtained for a moderate or large number of populations. Using the joint posterior distribution, posteriors can also be derived for any evolutionary population parameters, such as the traditional fixation indices. A major advantage compared to most earlier methods is that the number of populations is treated here as an unknown parameter. What is traditionally considered as two genetically distinct populations, either recently founded or connected by considerable gene flow, is here considered as one panmictic population with a certain probability based on marker data and prior information. Analyses of previously published data on the Moroccan argan tree (Argania spinosa) and of simulated data sets suggest that our method is capable of estimating a population substructure, while not artificially enforcing a substructure when it does not exist. The software (BAPS) used for the computations is freely available from http://www.rni.helsinki.fi/~mjs.
Reference Key
openalex_W1886810706 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Jukka Corander, Patrik Waldmann, Mikko J. Sillanpää
Journal current genetics
Year 2003
DOI
10.1093/genetics/163.1.367
URL
https://academic.oup.com/genetics/article-pdf/163/1/367/37256790/genetics0367.pdf
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