Distinguishing Migration From Isolation: A Markov Chain Monte Carlo Approach

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ID: 294529
2001
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Abstract
Abstract A Markov chain Monte Carlo method for estimating the relative effects of migration and isolation on genetic diversity in a pair of populations from DNA sequence data is developed and tested using simulations. The two populations are assumed to be descended from a panmictic ancestral population at some time in the past and may (or may not) after that be connected by migration. The use of a Markov chain Monte Carlo method allows the joint estimation of multiple demographic parameters in either a Bayesian or a likelihood framework. The parameters estimated include the migration rate for each population, the time since the two populations diverged from a common ancestral population, and the relative size of each of the two current populations and of the common ancestral population. The results show that even a single nonrecombining genetic locus can provide substantial power to test the hypothesis of no ongoing migration and/or to test models of symmetric migration between the two populations. The use of the method is illustrated in an application to mitochondrial DNA sequence data from a fish species: the threespine stickleback (Gasterosteus aculeatus).
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openalex_W1934545424 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Rasmus Nielsen, John Wakeley
Journal current genetics
Year 2001
DOI
10.1093/genetics/158.2.885
URL
https://academic.oup.com/genetics/article-pdf/158/2/885/35142240/genetics0885.pdf
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