Convergence-Divergence Models: Generalizations of Phylogenetic Trees Modeling Gene Flow Over Time

Area of Science:

• Evolutionary biology
• Phylogenetics
• Computational biology

Background:

• Phylogenetic trees model divergent evolution but struggle with gene flow processes like introgressive hybridization.
• Phylogenetic networks offer generalization but often assume instantaneous hybridization events.
• Convergence-divergence models provide a flexible framework by retaining a principal tree and allowing gene flow over time.

Purpose of the Study:

• To develop novel maximum likelihood algorithms for inferring N-taxon convergence-divergence models.
• To address limitations of existing phylogenetic models in representing complex evolutionary scenarios.
• To enable the study of processes like gene flow and replicated evolution within a unified framework.

Main Methods:

• Development of quartet-based maximum likelihood algorithms for inferring convergence-divergence models.
• Utilizing 4-taxon convergence-divergence models inferred via model selection for subsets of taxa.
• Algorithms designed to infer the principal tree, sets of converging taxa, and model parameters (root probabilities, edge lengths, convergence parameters).

Main Results:

• Successful inference of key aspects of N-taxon convergence-divergence models, including the principal tree and convergence parameters.
• Demonstration of accurate recovery of convergence-divergence models from simulated data.
• Algorithms are applicable to various data types, including multiple sequence alignments and gene presence/absence datasets.

Conclusions:

• The developed algorithms provide a robust computational framework for inferring complex evolutionary histories beyond simple tree models.
• Convergence-divergence models offer a more biologically realistic representation of evolutionary processes involving gene flow and convergence.
• This work advances phylogenetic inference by enabling the analysis of more nuanced evolutionary dynamics.