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A Practical Guide to Phylogenetics for Nonexperts

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A maximum pseudo-likelihood approach for phylogenetic networks.

Yun Yu, Luay Nakhleh

|October 10, 2015

Summary

This study introduces a computationally efficient pseudo-likelihood method for inferring species phylogenies, effectively handling incomplete lineage sorting (ILS) and hybridization. The method scales to larger datasets than previous approaches, offering a practical solution for complex evolutionary histories.

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Area of Science:

Phylogenetics
Computational Biology
Evolutionary Biology

Background:

Phylogenomic analyses increasingly require models accounting for incomplete lineage sorting (ILS) and hybridization.
A prior maximum likelihood approach showed promise but was computationally infeasible for large datasets.
Inferring species phylogenies under these complex evolutionary processes remains a significant challenge.

Purpose of the Study:

To develop a computationally efficient statistical method for inferring phylogenetic networks in the presence of ILS and hybridization.
To overcome the computational limitations of maximum likelihood methods for complex evolutionary scenarios.
To provide a scalable approach for phylogenetic inference using pseudo-likelihood.

Main Methods:

Introduced the concept of pseudo-likelihood for phylogenetic networks, inspired by rooted triples.
Combined pseudo-likelihood with a search heuristic for phylogenetic network inference.
Implemented the method in the open-source software PhyloNet.

Main Results:

The pseudo-likelihood method is computationally efficient and demonstrated strong performance on analyzed datasets.
The method successfully infers correct phylogenetic networks under specific conditions.
It provides a scalable alternative to maximum likelihood for inferring species phylogenies with ILS and hybridization.

Conclusions:

Maximum pseudo-likelihood offers a scalable solution for inferring species phylogenies with ILS and hybridization.
The method addresses computational intractability issues of full maximum likelihood.
While network inference can have non-unique solutions, this method provides a robust approach for phylogenetic reconstruction.