Polymorphism-Aware Species Trees with Advanced Mutation Models, Bootstrap, and Rate Heterogeneity.

Area of Science:

• Evolutionary biology
• Computational phylogenetics
• Population genetics

Background:

• Traditional phylogenetic methods often overlook population polymorphisms.
• Multispecies coalescent models are computationally intensive and limited to small datasets.
• Existing approaches struggle with scalability for large-scale genomic data.

Purpose of the Study:

• To introduce and enhance a polymorphism-aware phylogenetic model (PoMo) for efficient and accurate phylogenetic inference.
• To extend PoMo's capabilities for handling large population datasets and complex evolutionary scenarios.
• To provide a robust tool for analyzing genetic variation within and between species.

Main Methods:

• Developed PoMo, a phylogenetic model that directly infers species trees from allele frequency data, bypassing gene tree construction.
• Integrated PoMo into IQ-TREE, adding features for mutation model selection, rate variation inference, and branch support assessment.
• Utilized bmm-simulate for simulating sequence data under the PoMo model.

Main Results:

• Demonstrated PoMo's scalability and accuracy on a dataset of 100 species with 10 individuals each.
• Showcased PoMo's superior accuracy compared to standard substitution models on concatenated alignments.
• Confirmed PoMo's computational efficiency, performing inference almost as fast as standard methods.

Conclusions:

• PoMo offers a significant advancement in phylogenetic analysis by effectively incorporating population polymorphism data.
• The enhanced PoMo implementation provides a powerful and scalable solution for large-scale phylogenomic studies.
• PoMo is valuable for inferring evolutionary relationships when population-level genetic variation is considered.