Inferring Pareto-optimal reconciliations across multiple event costs under the duplication-loss-coalescence model

Area of Science:

• Computational Biology
• Evolutionary Biology
• Phylogenetics

Background:

• Gene tree-species tree incongruence is common in evolutionary studies.
• Maximum Parsimony Reconciliations (MPRs) are widely used but depend on difficult-to-estimate event costs.
• Existing models do not fully capture the complexity of evolutionary events, particularly in the duplication-loss-coalescence model.

Purpose of the Study:

• To develop a novel computational framework for analyzing evolutionary reconciliation events.
• To investigate the relationship between event costs and reconciliation outcomes in the duplication-loss-coalescence model.
• To provide a method applicable to a broader range of eukaryotic species.

Main Methods:

• Developed a fixed-parameter tractable algorithm for computing Pareto-optimal reconciliations.
• Implemented a method to record all reconciliation events and their frequencies.
• Applied the algorithm to a dataset of 16 fungal species.

Main Results:

• Characterized the complexity of the Maximum Parsimony Reconciliation (MPR) space across various event costs.
• Identified evolutionary events consistently supported across different cost parameters.
• Provided a systematic analysis of reconciliation event frequencies.

Conclusions:

• The study presents a new framework for understanding evolutionary reconciliation, integrating macro-evolutionary events and population effects.
• This approach offers broad applicability across diverse eukaryotic lineages.
• The findings enhance the study of gene tree-species tree incongruence by considering complex evolutionary models.