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MBE Advance Access originally published online on February 9, 2005
Molecular Biology and Evolution 2005 22(5):1185-1192; doi:10.1093/molbev/msi103
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© The Author 2005. 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@oupjournals.org

Research Article

Bayesian Coalescent Inference of Past Population Dynamics from Molecular Sequences

A. J. Drummond, A. Rambaut, B. Shapiro and O. G. Pybus

Department of Zoology, University of Oxford, Oxford, United Kingdom

E-mail: alexei.drummond{at}zoo.ox.ac.uk.

We introduce the Bayesian skyline plot, a new method for estimating past population dynamics through time from a sample of molecular sequences without dependence on a prespecified parametric model of demographic history. We describe a Markov chain Monte Carlo sampling procedure that efficiently samples a variant of the generalized skyline plot, given sequence data, and combines these plots to generate a posterior distribution of effective population size through time. We apply the Bayesian skyline plot to simulated data sets and show that it correctly reconstructs demographic history under canonical scenarios. Finally, we compare the Bayesian skyline plot model to previous coalescent approaches by analyzing two real data sets (hepatitis C virus in Egypt and mitochondrial DNA of Beringian bison) that have been previously investigated using alternative coalescent methods. In the bison analysis, we detect a severe but previously unrecognized bottleneck, estimated to have occurred 10,000 radiocarbon years ago, which coincides with both the earliest undisputed record of large numbers of humans in Alaska and the megafaunal extinctions in North America at the beginning of the Holocene.

Key Words: coalescent inference • Markov chain Monte Carlo • demographic model selection • skyline plot • megafaunal extinctions • Holocene


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