Genetics, Vol. 161, 1307-1320, July 2002, Copyright © 2002

Estimating Mutation Parameters, Population History and Genealogy Simultaneously From Temporally Spaced Sequence Data

Alexei J. Drummonda, Geoff K. Nichollsb, Allen G. Rodrigoa, and Wiremu Solomonc
a School of Biological Sciences, University of Auckland 1001, Auckland, New Zealand
b Department of Mathematics, University of Auckland 1001, Auckland, New Zealand
c Department of Statistics, University of Auckland 1001, Auckland, New Zealand

Corresponding author: Alexei J. Drummond, University of Oxford, South Parks Rd., Oxford, OX1 3PS, United Kingdom., alexei.drummond{at}zoology.oxford.ac.uk (E-mail)

Communicating editor: J. HEIN

Molecular sequences obtained at different sampling times from populations of rapidly evolving pathogens and from ancient subfossil and fossil sources are increasingly available with modern sequencing technology. Here, we present a Bayesian statistical inference approach to the joint estimation of mutation rate and population size that incorporates the uncertainty in the genealogy of such temporally spaced sequences by using Markov chain Monte Carlo (MCMC) integration. The Kingman coalescent model is used to describe the time structure of the ancestral tree. We recover information about the unknown true ancestral coalescent tree, population size, and the overall mutation rate from temporally spaced data, that is, from nucleotide sequences gathered at different times, from different individuals, in an evolving haploid population. We briefly discuss the methodological implications and show what can be inferred, in various practically relevant states of prior knowledge. We develop extensions for exponentially growing population size and joint estimation of substitution model parameters. We illustrate some of the important features of this approach on a genealogy of HIV-1 envelope (env) partial sequences.




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