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Recombination estimation under complex evolutionary models with the coalescent composite-likelihood method.

Antonio Carvajal-Rodríguez1, Keith A Crandall, David Posada

  • 1Department of Microbiology and Molecular Biology, Brigham Young University, Provo, Utah, USA. ac549@email.byu.edu

Molecular Biology and Evolution
|February 3, 2006
PubMed
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The composite-likelihood estimator (CLE) robustly estimates population recombination rates but can underestimate them with complex demographic histories. More sophisticated models improve accuracy in these challenging scenarios.

Area of Science:

  • Population genetics
  • Molecular evolution
  • Bioinformatics

Background:

  • The composite-likelihood estimator (CLE) is a standard method for estimating population recombination rates.
  • It assumes a simple mutation model and is generally robust to minor model misspecifications.
  • Complex evolutionary scenarios, such as those in rapidly evolving pathogens, challenge these assumptions.

Purpose of the Study:

  • To evaluate the performance of the CLE and likelihood permutation test (LPT) under complex evolutionary models.
  • To assess the impact of relaxed assumptions and more complex models on recombination rate estimation.
  • To provide guidance for robust application of recombination rate estimation methods.

Main Methods:

  • Simulations under various models: general time reversible (GTR) substitution, rate heterogeneity (Gamma), positive selection, population growth, structure, and noncontemporaneous sampling.

Related Experiment Videos

  • Evaluation of a modified CLE allowing for a four-allele, GTR + Gamma model.
  • Analysis of real genetic sequence data.

    • Main Results:

    • The CLE is robust to severe misspecifications of the substitution model.
    • The CLE underestimates recombination rates under exponential growth, population mixture, selection, or noncontemporaneous sampling.
    • More complex models offer slight performance improvements, particularly for the LPT, in challenging scenarios.

    Conclusions:

    • The CLE is reliable for simple population genetic models but requires caution with complex demographic histories.
    • Employing more complex models can enhance the accuracy of recombination rate estimation in challenging evolutionary contexts.
    • This study offers insights for more robust applications of recombination rate estimation in population genetics.