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Inference of Multiple Mergers while Dating a Pathogen Phylogeny.

David Helekal1, Jere Koskela2, Xavier Didelot3

  • 1Centre for Doctoral Training in Mathematics for Real-World Systems, University of Warwick, Coventry CV47AL, UK.

Systematic Biology
|January 18, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to detect multiple merger events in pathogen phylogenetics, improving the accuracy of evolutionary dating. The approach enhances understanding of pathogen evolution and epidemiology.

Keywords:
Coalescent modelmultiple mergerspathogen phylogeneticsphylogenetic dating

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Area of Science:

  • Evolutionary biology
  • Computational biology
  • Epidemiology

Background:

  • Phylogenetic studies often assume single merger events, neglecting multiple mergers.
  • Multiple mergers are probable in small populations or with high reproductive variability.
  • Accurate phylogenetic dating is crucial for understanding pathogen evolution.

Purpose of the Study:

  • To develop a method for detecting multiple merger events in phylogenetic analysis.
  • To improve the accuracy of phylogenetic dating by incorporating multiple merger events.
  • To apply the new methodology to real pathogen datasets for evolutionary epidemiology.

Main Methods:

  • Utilized Lambda-coalescent theory as a modeling framework.
  • Employed Bayesian inference with Billera-Holmes-Vogtmann space embedding.
  • Developed a customized Markov Chain Monte Carlo (MCMC) sampling scheme.

Main Results:

  • Successfully inferred the presence and timing of multiple merger events in simulated datasets.
  • Demonstrated improved phylogenetic dating accuracy by accounting for multiple mergers.
  • Validated the approach on real-world data from Vibrio cholerae and Mycobacterium tuberculosis.

Conclusions:

  • The developed methodology effectively detects multiple merger events and enhances phylogenetic dating.
  • This approach offers significant improvements for pathogen evolutionary epidemiology.
  • A freely available R package (MMCTime) has been implemented for this methodology.