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Related Experiment Videos

Does choice in model selection affect maximum likelihood analysis?

Jennifer Ripplinger1, Jack Sullivan

  • 1Bioinformatics and Computational Biology, University of Idaho, Moscow, Idaho 83844-3051, USA. jripplinger@vandals.uidaho.edu

Systematic Biology
|February 16, 2008
PubMed
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Choosing the right phylogenetic model is crucial for accurate evolutionary analysis. While different statistical methods yield varied models, the impact on evolutionary inferences is minimal, especially for well-supported findings.

Area of Science:

  • Phylogenetics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Accurate phylogenetic inference relies on statistically rigorous selection of nucleotide substitution models.
  • Several model selection methods exist for maximum likelihood (ML) analysis, but their empirical performance is not well-understood.

Purpose of the Study:

  • To investigate the impact of different model selection methods on ML phylogenetic estimation.
  • To evaluate effects on tree topology, bootstrap support, and hypothesis testing using empirical data.

Main Methods:

  • Analysis of 250 phylogenetic data sets from TreeBASE.
  • Comparison of model selection methods: hierarchical likelihood-ratio test (hLRT), Akaike information criterion (AIC), Bayesian information criterion (BIC), and decision theory (DT).

Related Experiment Videos

  • Evaluation of ML tree topology, bootstrap support, and hypothesis testing (SOWH, S-H tests) under selected models.

    • Main Results:

    • Different model selection methods identified distinct best-fit models for ~80% of data sets, with AIC favoring more complex models.
    • ML trees from different statistically supported models showed incongruent topologies in ~50% of cases, primarily due to poorly supported nodes.
    • Estimates from alternative statistically supported models were more similar to each other than to those from Kimura two-parameter (K2P) or maximum parsimony (MP) models.

    Conclusions:

    • Model selection significantly impacts optimal tree topology but has limited effect on robust evolutionary inferences.
    • Phylogenetic estimates using statistically supported models are more consistent than those using K2P or MP.
    • Employing any statistically-based model selection is preferable to omitting the process.