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PHRAPL: Phylogeographic Inference Using Approximate Likelihoods.

Nathon D Jackson1, Ariadna E Morales1, Bryan C Carstens2

  • 1Department of Ecology and Evolutionary Biology, University of Tennessee, 442 Hesler Biology Building, Knoxville, TN 37996, USA.

Systematic Biology
|February 17, 2017
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Summary

This study introduces PHRAPL, a new computational method for phylogeography that accurately identifies complex demographic histories and estimates population genetics parameters. PHRAPL reduces reliance on intuition for model selection in evolutionary studies.

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Area of Science:

  • Population Genetics
  • Evolutionary Biology
  • Computational Biology

Background:

  • Species' demographic histories are shaped by complex evolutionary processes influencing genetic diversity.
  • Current phylogeographic models often oversimplify demography, requiring researchers to rely on intuition for model selection.
  • Existing methods struggle to incorporate all possible evolutionary scenarios, limiting accurate inference.

Purpose of the Study:

  • To introduce PHRAPL, an approximate likelihood method that reduces reliance on intuition for phylogeographic model selection.
  • To enable the calculation of probabilities for complex demographic histories using gene trees.
  • To facilitate accurate parameter estimation for population genetics models.

Main Methods:

  • Developed an approximate likelihood approach (PHRAPL) for phylogeographic inference.
  • Utilized gene trees to calculate probabilities of various complex demographic histories.
  • Tested PHRAPL's performance in model selection and parameter estimation using simulated data.
  • Compared approximate likelihood results with standard analytical likelihood methods.

Main Results:

  • PHRAPL accurately identifies the most likely demographic model across diverse scenarios, especially with sufficient loci and deep divergence.
  • The method provides generally accurate parameter estimates for population size, gene flow, and coalescence times.
  • PHRAPL demonstrates strong performance in model selection, often identifying the true model in simulations.
  • Approximate likelihood results align well with standard analytical likelihood methods.

Conclusions:

  • PHRAPL is a valuable tool for phylogeographic model selection, enabling more extensive exploration of demographic model space.
  • The method facilitates parameter estimation for complex evolutionary models not easily handled by current approaches.
  • Accurate demographic modeling with PHRAPL enhances understanding of evolutionary processes shaping phylogeographic patterns.