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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

MAST: Phylogenetic Inference with Mixtures Across Sites and Trees.

Thomas K F Wong¹, Caitlin Cherryh², Allen G Rodrigo³

¹School of Computing, Australian National University, Canberra, ACT 2601, Australia.

Systematic Biology

|February 29, 2024

Summary

This summary is machine-generated.

Phylogenetic studies can now use a new multi-tree mixture model (MAST) to analyze complex evolutionary histories. This method accurately infers evolutionary relationships and accounts for factors like incomplete lineage sorting and introgression.

Keywords:

Incomplete lineage sorting introgression mixture model multitree model phylogenetics

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Area of Science:

Computational Biology
Phylogenetics
Evolutionary Biology

Background:

Modern phylogenomic studies routinely analyze hundreds or thousands of loci.
Concatenation approaches assume a single evolutionary tree, but loci can have different histories due to incomplete lineage sorting (ILS), introgression, or horizontal gene transfer.
Recombination within loci can also violate the single tree assumption.

Purpose of the Study:

To introduce an implementation of a multi-tree mixture model called Mixtures Across Sites and Trees (MAST).
To extend prior mixture models by allowing estimation of weights for pre-specified bifurcating trees within a single alignment.
To provide a maximum-likelihood framework for analyzing concatenated alignments with complex evolutionary histories.

Main Methods:

Implemented the MAST model within a maximum-likelihood framework using the IQ-TREE software.
The MAST model allows each tree to have independent parameters including topology, branch lengths, substitution model, and rate heterogeneity.
Utilized simulations to test model accuracy and statistical inference capabilities for distinguishing single-tree from multi-tree scenarios.

Main Results:

Simulations demonstrated accurate recovery of model parameters, including branch lengths and tree weights, across diverse biologically realistic scenarios.
Standard statistical approaches could effectively reject single-tree models when data simulated under multiple trees, and vice versa.
Application to primate datasets revealed MAST's ability to detect ILS in Great Apes and introgression signals in macaques.

Conclusions:

The MAST model successfully identified the dominant tree topology in a Platyrrhine dataset where concatenated and gene tree approaches disagreed.
MAST provides a powerful tool for analyzing concatenated alignments using maximum likelihood while mitigating biases from assuming a single tree.
The model offers a flexible framework for future extensions to further enhance phylogenetic inference.