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Updated: Jul 16, 2025

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Detecting Episodic Evolution through Bayesian Inference of Molecular Clock Models.

John H Tay1, Guy Baele2, Sebastian Duchene1

  • 1Peter Doherty Institute for Infection and Immunity, Department of Microbiology and Immunology, University of Melbourne, Melbourne, Victoria, Australia.

Molecular Biology and Evolution
|September 22, 2023
PubMed
Summary
This summary is machine-generated.

We developed a new Bayesian phylogenetic method to detect episodic evolution, a key factor in organismal evolution. This approach quantifies evolutionary rate increases, providing strong evidence for episodic evolution in SARS-CoV-2.

Keywords:
Bayesian model selectionMarkov chain Monte Carloeffect sizeepisodic evolutionmolecular clockprior sensitivityvirus molecular evolution

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Area of Science:

  • Evolutionary Biology
  • Phylogenetics
  • Molecular Evolution

Background:

  • Molecular evolutionary rate variation is crucial for understanding organismal evolution.
  • Molecular clock models are used to study evolutionary rates, with local clocks highlighting episodic evolution, as seen in SARS-CoV-2.
  • Assessing statistical evidence is vital for the reliability of molecular clock inferences.

Purpose of the Study:

  • To introduce a novel Bayesian phylogenetic approach for detecting episodic evolution.
  • To quantify the support for evolutionary rate increases using Bayes factors.
  • To compare this new method with existing model comparison techniques and an automated approach.

Main Methods:

  • Developed a Bayesian phylogenetic approach using Bayes factors to quantify support for evolutionary rate increases.
  • Conducted extensive simulations to evaluate the method's power and sensitivity.
  • Benchmarked the approach against formal model comparison using log marginal likelihood and a random local clock model.
  • Applied the method to a dataset of SARS-CoV-2 genomes.

Main Results:

  • The novel Bayesian method effectively quantifies support for episodic evolution.
  • Quantifying effect size showed higher sensitivity than formal model testing.
  • The random local clock approach demonstrated utility in detecting episodic evolution without a priori definition.
  • Analysis of SARS-CoV-2 genomes revealed "very strong" evidence for episodic evolution.

Conclusions:

  • The study provides practical guidelines and methods for detecting episodic evolution using Bayesian phylogenetics.
  • The findings underscore the importance of episodic evolution in viral evolution, exemplified by SARS-CoV-2.
  • The developed approach offers a sensitive and straightforward way to assess evolutionary rate shifts.