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High-resolution species trees without concatenation.

Scott V Edwards1, Liang Liu, Dennis K Pearl

  • 1Department of Organismic and Evolutionary Biology, and Museum of Comparative Zoology, Harvard University, Cambridge, MA 02138, USA. sedwards@fas.harvard.edu

Proceedings of the National Academy of Sciences of the United States of America
|March 30, 2007
PubMed
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This study introduces a Bayesian phylogenetic model that accurately estimates species trees by accounting for gene tree variations. This new method improves phylogenetic resolution and reduces misleading results compared to traditional approaches.

Area of Science:

  • Evolutionary Biology
  • Phylogenetics
  • Computational Biology

Background:

  • Most phylogenetic models prioritize gene tree resolution over species tree estimation, despite the latter's primary biological interest.
  • Gene trees are expected to exhibit stochastic variation due to processes like the coalescent, even when originating from a single species history.
  • Existing methods often treat individual gene histories independently, potentially overlooking the overarching species tree.

Purpose of the Study:

  • To analyze a Bayesian model designed for estimating species trees that explicitly incorporates the expected stochastic variation of gene trees.
  • To evaluate the model's performance using a large-scale gene dataset from yeast and compare it with traditional concatenation methods.
  • To demonstrate the model's ability to resolve species trees accurately, even under conditions where concatenation may be misleading.

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Main Methods:

  • Development and application of a Bayesian phylogenetic model that accounts for the coalescent process and gene tree stochasticity.
  • Analysis of a 106-gene dataset from yeast to estimate the species tree.
  • Comparative analysis with the concatenation method, assessing the number of loci required and the accuracy of the resulting phylogeny.
  • Simulations were used to test the model's performance under varying conditions of gene tree/species tree congruence and number of loci.

Main Results:

  • The Bayesian model significantly reduces the number of gene trees when acknowledging a shared, unknown species tree, compared to independent locus analysis.
  • The model yields a concentrated posterior distribution for the yeast species tree, with a mode congruent with the concatenated gene tree.
  • The species tree was resolved with less than half the loci required by concatenation, and accurately even when gene tree/species tree congruence was as low as 10% with sufficient loci.

Conclusions:

  • The developed Bayesian model provides an alternative paradigm for phylogenomic data combination, prioritizing species history over individual gene tree idiosyncrasies.
  • This approach enhances the accuracy and resolution of species tree estimation, particularly with large numbers of unlinked loci.
  • The model can successfully resolve species trees even when gene tree discordance is high, offering a more robust phylogenetic inference method.