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MBE Advance Access published online on August 29, 2003
Molecular Biology and Evolution, doi:10.1093/molbev/msg215
Molecular Biology and Evolution © Society for Molecular Biology and Evolution 2003; all rights reserved
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Accepted July 6, 2003
© 2003 Society for Molecular Biology and Evolution

Original Articles

Inferring Evolutionary Rates Using Serially Sampled Sequences from Several Populations

Allen G. Rodrigo 1*, Matthew Goode 1, Roald Forsberg 2, Howard A. Ross 1, and Alexei Drummond 3

1 Computational and Evolutionary Biology Laboratory, School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand; Allan Wilson Centre for Molecular Ecology and Evolution, University of Auckland, Private Bag 92019, Auckland, New Zealand
2 Bioinformatics Research Center (BiRC), Department of Ecology and Genetics, University of Århus, Ny Munkegade, Bygn. 540, DK-8000 Århus C, Denmark
3 Computational and Evolutionary Biology Laboratory, School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand

* To whom correspondence should be addressed. E-mail: a.rodrigo{at}auckland.ac.nz.


  Abstract

The estimation of evolutionary rates from serially sampled sequences has recently been the focus of several studies. In this paper, we extend these analyses 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 non-responders 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 non-responder. An example dataset comprising HIV-1 partial envelope sequences from 6 patients on highly active antiretroviral therapy is analysed.

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


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