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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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Testing for Independence between Evolutionary Processes.

Abdelkader Behdenna1, Joël Pothier2, Sophie S Abby3

  • 1UMR7138 "Evolution Paris-Seine" UPMC-CNRS Bât A, 4ème étage, porte 414, Case 5 7 quai Saint Bernard, 75252 Paris Cedex 05; Atelier de BioInformatique - MNHN Boite Courier 50 Batiment 139 - RdC 45 rue Buffon, 75005 Paris; Smile "Stochastic Models for the Inference of Life Evolution" Center for Interdisciplinary Research in Biology Collège de France 11, place Marcelin Berthelot 75231 Paris Cedex 05; kaderbehdenna@gmail.com.

Systematic Biology
|May 22, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical method to detect associations between evolutionary events on phylogenetic trees. The approach quantifies event co-occurrence across branches, improving our understanding of complex adaptations.

Keywords:
Coevolutionphylogenystatistical test.

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Area of Science:

  • Evolutionary Biology
  • Phylogenetics
  • Biostatistics

Background:

  • Evolutionary events on phylogenetic trees can indicate complex adaptive phenomena and epistasis.
  • Existing methods for detecting co-occurring events are limited in scope.
  • There is a need for methods that account for diverse relative positions of events on a tree.

Purpose of the Study:

  • To develop a novel statistical method for quantifying the association between two or more evolutionary events on a phylogenetic tree.
  • To provide a general approach applicable to any discrete character, including sequence substitutions and functional gains/losses.

Main Methods:

  • Proposed a statistical method to test for significant associations between evolutionary events along a phylogenetic tree.
  • Utilized a linear algebra representation to model the localization of events on the tree.
  • Computed the probability distribution of paired events under a null model of independent event occurrences.

Main Results:

  • The method can detect associations between events on the same branch and those ordered across different branches.
  • Simulations were used to assess the strengths and weaknesses of the proposed method.
  • The method was applied to study the loss of cell motility in intracellular pathogens.

Conclusions:

  • The developed method offers a robust statistical framework for analyzing the co-occurrence of evolutionary events on phylogenetic trees.
  • This approach enhances the understanding of complex adaptive processes and their underlying genetic or functional bases.
  • The findings provide valuable insights into evolutionary patterns, exemplified by the analysis of cell motility loss.