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Using the quantitative genetic threshold model for inferences between and within species.

Joseph Felsenstein1

  • 1Department of Genome Sciences, University of Washington, Box 357730, Seattle, WA 98195-7730, USA. joe@gs.washington.edu

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
|July 29, 2005
PubMed
Summary

Sewall Wright's threshold model can now be used for statistical inference in evolutionary studies thanks to Markov chain Monte Carlo (MCMC) methods. This approach offers advantages for modeling discrete traits and correlated trait evolution in systematics.

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Area of Science:

  • Evolutionary biology
  • Systematics
  • Quantitative genetics

Background:

  • Sewall Wright's threshold model is useful for discrete traits with underlying continuous variation.
  • Statistical inference with the threshold model has historically been challenging.
  • Markov chain Monte Carlo (MCMC) methods offer new possibilities for statistical inference.

Purpose of the Study:

  • To explore the application of the threshold model in morphological systematics for discrete traits.
  • To demonstrate the utility of MCMC methods for likelihood and Bayesian inference with the threshold model.
  • To highlight the advantages of the threshold model over 0/1 Markov process models.

Main Methods:

  • Introduction and detailed description of MCMC importance sampling methods.

Related Experiment Videos

  • Evaluation of likelihood ratios for the threshold model using MCMC.
  • Application of the threshold model to discrete, all-or-none traits in systematics.

    • Main Results:

    • MCMC methods enable efficient likelihood and Bayesian inference for the threshold model.
    • The threshold model accommodates polymorphism within species.
    • The threshold model allows for correlated trait evolution with fewer parameters than alternative models.

    Conclusions:

    • MCMC methods significantly enhance the statistical inference capabilities of Sewall Wright's threshold model.
    • The threshold model is a powerful tool for studying the evolution of discrete traits in morphological systematics.
    • This approach provides a more parsimonious way to model correlated trait evolution compared to 0/1 Markov process models.