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Inferring evolutionary rates using serially sampled sequences from several populations.

Allen G Rodrigo1, Matthew Goode, Roald Forsberg

  • 1Computational and Evolutionary Biology Laboratory, School of Biological Sciences, University of Auckland, Auckland, New Zealand. a.rodrigo@auckland.ac.nz

Molecular Biology and Evolution
|September 2, 2003
PubMed
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This study introduces methods to estimate viral evolutionary rates and identify nonresponders during therapy. It helps understand treatment effectiveness and viral evolution in populations.

Area of Science:

  • Evolutionary biology
  • Virology
  • Statistical genetics

Background:

  • Estimating evolutionary rates from serially sampled sequences is crucial for understanding pathogen dynamics.
  • Previous studies focused on single populations, limiting joint analysis of multiple evolving groups.
  • Viral evolution during therapy can be complex, with some individuals showing continued replication (nonresponders).

Purpose of the Study:

  • To extend evolutionary rate estimation to multiple, simultaneously sampled populations.
  • To develop methods for jointly estimating the substitution rate (omega) and the proportion of nonresponders (p).
  • To create statistical tests for classifying individuals as responders or nonresponders.

Main Methods:

  • Developed two likelihood-based procedures for joint estimation of p and omega.

Related Experiment Videos

  • Utilized empirical Bayes' tests for responder/nonresponder classification.
  • Applied methods to HIV-1 envelope gene sequences from patients undergoing therapy.

    • Main Results:

    • Successfully estimated joint substitution rates and nonresponder proportions from serially sampled viral sequences.
    • Demonstrated the utility of empirical Bayes' tests in classifying responders and nonresponders.
    • Analyzed HIV-1 data, providing insights into viral evolution under therapy.

    Conclusions:

    • The developed methods allow for robust estimation of evolutionary rates in multiple populations.
    • The approach effectively identifies individuals with ongoing viral evolution despite treatment.
    • This work has implications for understanding treatment dynamics and viral adaptation in clinical settings.