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MBE Advance Access originally published online on February 22, 2006
Molecular Biology and Evolution 2006 23(5):988-996; doi:10.1093/molbev/msj111
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© The Author 2006. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org

Proceedings of the SMBE Tri-National Young Investigators' Workshop 2005

Coalescent-Based Estimation of Population Parameters When the Number of Demes Changes Over Time

Greg Ewing*,{dagger} and Allen Rodrigo*,{dagger}

* The Allan Wilson Centre for Molecular Ecology and Evolution, University of Auckland, Private Bag 920191, Auckland, New Zealand; and {dagger} The Bioinformatics Institute, University of Auckland, Private Bag 920191, Auckland, New Zealand

E-mail: a.rodrigo{at}auckland.ac.nz.

We expand a coalescent-based method that uses serially sampled genetic data from a subdivided population to incorporate changes to the number of demes and patterns of colonization. Often, when estimating population parameters or other parameters of interest from genetic data, the demographic structure and parameters are not constant over evolutionary time. In this paper, we develop a Bayesian Markov chain Monte Carlo method that allows for step changes in mutation, migration, and population sizes, as well as changing numbers of demes, where the times of these changes are also estimated. We show that in parameter ranges of interest, reliable estimates can often be obtained, including the historical times of parameter changes. However, posterior densities of migration rates can be quite diffuse and estimators somewhat biased, as reported by other authors.

Key Words: coalescent • migration • Markov chain Monte Carlo • Bayesian inference • serial samples • measurably evolving populations


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