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A Minimal yet Flexible Likelihood Framework to Assess Correlated Evolution.

Abdelkader Behdenna1,2,3, Maxime Godfroid2, Patrice Petot1,2

  • 1Institut de Systématique, Évolution, Biodiversité (ISYEB), Muséum National d'Histoire Naturelle, CNRS UMR 7205, Sorbonne Université, École Pratique des Hautes Études, Université des Antilles, 45 rue Buffon, 75005 Paris, France.

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Summary

This study introduces a new computational framework to analyze correlated evolution between traits. The method efficiently models trait interactions on phylogenetic trees, improving our understanding of evolutionary processes.

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Area of Science:

  • Evolutionary biology
  • Phylogenetics
  • Computational biology

Background:

  • Understanding evolutionary processes requires analyzing trait changes over time.
  • Characterizing correlated evolution, where multiple evolutionary processes interact, is crucial but computationally challenging with existing methods.

Purpose of the Study:

  • To develop a minimal likelihood framework for modeling the joint evolution of two discrete traits on a phylogenetic tree.
  • To provide a computationally efficient method for studying correlated evolution.

Main Methods:

  • A minimal likelihood framework was developed to model the joint evolution of two traits.
  • The framework uses a few parameters to tune mutation rates and their interdependencies.
  • The method was validated using simulations and 16S rRNA sequence data from enterobacteria.

Main Results:

  • The proposed framework efficiently characterizes the type and strength of correlated evolution.
  • It allows for testing independence, identifying interaction types, and estimating model parameters.
  • The method demonstrated good performance in parameter estimation and model selection, even with small datasets (<100 species).

Conclusions:

  • The new framework offers an efficient and powerful approach to study correlated evolution.
  • It advances the understanding of how multiple evolutionary processes interact.
  • The method is applicable to various discrete traits in evolutionary biology.