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MBE Advance Access originally published online on August 29, 2003
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Mol. Biol. Evol. 20(12):2010-2018. 2003
DOI: 10.1093/molbev/msg215
© 2003 by the Society for Molecular Biology and Evolution. ISSN: 0737-4038

Inferring Evolutionary Rates Using Serially Sampled Sequences from Several Populations

Allen G. Rodrigo*,{dagger},, Matthew Goode*,{dagger}, Roald Forsberg{ddagger}, Howard A. Ross*,{dagger} and Alexei Drummond*,1

* Computational and Evolutionary Biology Laboratory, School of Biological Sciences, and the
{dagger} Allan Wilson Centre for Molecular Ecology and Evolution, University of Auckland, Auckland, New Zealand
{ddagger} Bioinformatics Research Center, Department of Ecology and Genetics, University of rhus, rhus, Denmark

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

The estimation of evolutionary rates from serially sampled sequences has recently been the focus of several studies. In this paper, we extend these analyzes to allow the estimation of a joint rate of substitution, {omega}, from several evolving populations from which serial samples are drawn. In the case of viruses evolving in different hosts, therapy may halt replication and therefore the accumulation of substitutions in the population. In such cases, it may be that only a proportion, p, of subjects are nonresponders who have viral populations that continue to evolve. We develop two likelihood-based procedures to jointly estimate p and {omega}, and empirical Bayes' tests of whether an individual should be classified as a responder or nonresponder. An example data set comprising HIV-1 partial envelope sequences from six patients on highly active antiretroviral therapy is analyzed.

Key Words: serial samples • substitution rate • subtree likelihood • whole-tree likelihood • maximum likelihood


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