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Ancestral inference on gene trees under selection.

Graham Coop1, Robert C Griffiths

  • 1Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK. coop@stats.ox.ac.uk

Theoretical Population Biology
|October 7, 2004
PubMed
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Quantifying natural selection strength is crucial for population genetics. This study introduces a likelihood method using coalescent models to infer selection at a single DNA site, applicable to various bi-allelic selection schemes.

Area of Science:

  • Population genetics
  • Evolutionary biology
  • Molecular evolution

Background:

  • Understanding natural selection's role in shaping genetic diversity is a fundamental question in population genetics.
  • Quantifying the strength of selection is essential for evolutionary studies.

Purpose of the Study:

  • To describe a full likelihood approach for inferring selection at a single site within linked neutral DNA sequences.
  • To provide a general method applicable to any bi-allelic selection scheme.

Main Methods:

  • Utilizes a coalescent model to simulate DNA sequence ancestry with a segregating selected site.
  • Employs the infinitely many-sites mutation model without back or parallel mutations.
  • Incorporates selection by modeling allelic class frequencies stochastically backward in time.

Related Experiment Videos

  • Uses a subdivided population model with time-varying population sizes.
  • Applies an importance sampling algorithm to explore coalescent tree space.

    • Main Results:

    • The described method allows for the inference of selection at a single site.
    • A perfect phylogeny (gene tree) can be constructed from mutation configurations.
    • The approach was successfully applied to a simulated dataset and published data.

    Conclusions:

    • The developed likelihood approach provides a robust framework for quantifying selection.
    • This method enhances our ability to study the evolutionary forces shaping genetic diversity.
    • The general applicability to bi-allelic selection schemes makes it a valuable tool for population geneticists.