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Bayesian long branch attraction bias and corrections.

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  • 1Department of Mathematics and Statistics, Dalhousie University, Halifax, Nova Scotia, canada B3H, 4R2 edward.susko@gmail.com.

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Bayesian phylogenetic methods can exhibit long branch attraction bias, especially with complex evolutionary trees. This study confirms this bias more broadly and introduces easily calculable corrections to improve tree inference accuracy.

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Area of Science:

  • Phylogenetics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Bayesian phylogenetic inference is widely used but can be susceptible to systematic errors.
  • The star-tree paradox highlights a specific type of error known as long branch attraction (LBA) bias in Bayesian methods.
  • Previous research identified LBA bias in simplified phylogenetic scenarios.

Purpose of the Study:

  • To investigate the prevalence and sources of long branch attraction bias in Bayesian phylogenetics.
  • To extend the analysis of LBA bias to more complex scenarios, including larger numbers of taxa and partially resolved trees.
  • To develop and validate methods for correcting topological biases in phylogenetic inference.

Main Methods:

  • Extension of previous theoretical work on the star-tree paradox to incorporate more taxa and partially resolved trees.
  • Analysis of Bayesian phylogenetic methods to identify and characterize sources of bias.
  • Development of novel methods for correcting topological biases.
  • Validation of correction methods through simulations.

Main Results:

  • Long branch attraction bias is confirmed to be a broader issue in Bayesian phylogenetics than previously established.
  • An additional, previously unidentified source of topological bias was discovered.
  • The developed correction methods are easily calculable using existing Bayesian software.
  • Simulations demonstrated the high effectiveness of the proposed corrections.

Conclusions:

  • Bayesian phylogenetic inference is prone to long branch attraction bias, particularly in complex datasets with numerous taxa or incomplete resolution.
  • New methods effectively correct for topological biases, enhancing the reliability of phylogenetic tree reconstruction.
  • These corrections offer a practical approach to improve phylogenetic accuracy and can be readily implemented with current Bayesian software.