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Performance-based selection of likelihood models for phylogeny estimation.

Vladimir Minin1, Zaid Abdo, Paul Joyce

  • 1Department of Methematics, PO Box 441103, University of Idaho, Moscow, Idaho 83844-1103, USA.

Systematic Biology
|October 8, 2003
PubMed
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A new decision theory (DT) method improves phylogenetic model selection by penalizing overfitting and using branch-length error. This approach yields more accurate phylogenetic estimates than traditional likelihood-ratio tests (LRTs).

Area of Science:

  • Computational Biology
  • Phylogenetics
  • Evolutionary Biology

Background:

  • Model-based phylogenetic estimation relies on selecting and justifying evolutionary models.
  • Likelihood-ratio tests (LRTs) are commonly used for model justification, comparing nested reversible models.
  • However, the best-fitting model may not yield the most reliable phylogenetic estimates for real data.

Purpose of the Study:

  • To develop a novel decision theory (DT) approach for phylogenetic model selection.
  • To incorporate relative branch-length error as a performance measure within the DT framework.
  • To provide a method that penalizes overfitting and compares all models simultaneously.

Main Methods:

  • Developed a decision theory (DT) method based on the Bayesian information criterion.

Related Experiment Videos

  • Incorporated relative branch-length error as a key performance metric.
  • Evaluated the DT method on four real data sets and through simulations using codon-based models.

    • Main Results:

    • The DT method selected the same or simpler models compared to conventional LRTs on real data.
    • Simulations showed the DT method consistently selects simpler models than LRTs.
    • Simpler models selected by the DT method resulted in more accurate branch-length estimates (both relative and absolute error).

    Conclusions:

    • The novel DT method offers a more reliable approach to phylogenetic model selection.
    • This method provides more accurate phylogenetic estimates by selecting simpler, yet more appropriate, models.
    • The DT-ModSel program implements this new model selection strategy.