Inferring phylogenetic networks with maximum pseudolikelihood under incomplete lineage sorting
Claudia Sol\'is-Lemus, C\'ecile An\'e

TL;DR
This paper introduces a fast, scalable statistical method using pseudolikelihood to infer phylogenetic networks from multi-locus data, effectively handling incomplete lineage sorting and horizontal gene transfer.
Contribution
It presents a novel pseudolikelihood framework for phylogenetic network inference that is computationally efficient and scalable, addressing limitations of existing methods.
Findings
The method is significantly faster than full likelihood approaches.
It maintains high accuracy in network inference.
Application to fish data revealed complex hybridization events.
Abstract
Phylogenetic networks are necessary to represent the tree of life expanded by edges to represent events such as horizontal gene transfers, hybridizations or gene flow. Not all species follow the paradigm of vertical inheritance of their genetic material. While a great deal of research has flourished into the inference of phylogenetic trees, statistical methods to infer phylogenetic networks are still limited and under development. The main disadvantage of existing methods is a lack of scalability. Here, we present a statistical method to infer phylogenetic networks from multi-locus genetic data in a pseudolikelihood framework. Our model accounts for incomplete lineage sorting through the coalescent model, and for horizontal inheritance of genes through reticulation nodes in the network. Computation of the pseudolikelihood is fast and simple, and it avoids the burdensome calculation of…
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