Advances in Computational Methods for Phylogenetic Networks in the Presence of Hybridization

TL;DR

This paper reviews recent computational advances in inferring phylogenetic networks that incorporate hybridization and incomplete lineage sorting, highlighting methods, challenges, and future research directions.

Contribution

It provides a comprehensive survey of new computational methods for phylogenetic network inference, including parsimony, likelihood, Bayesian approaches, and statistical tests.

Findings

Significant progress in Bayesian network inference from sequence data.

Development of statistical tests like the D-statistic for hybridization hypotheses.

Identification of current limitations and future directions in the field.

Abstract

Phylogenetic networks extend phylogenetic trees to allow for modeling reticulate evolutionary processes such as hybridization. They take the shape of a rooted, directed, acyclic graph, and when parameterized with evolutionary parameters, such as divergence times and population sizes, they form a generative process of molecular sequence evolution. Early work on computational methods for phylogenetic network inference focused exclusively on reticulations and sought networks with the fewest number of reticulations to fit the data. As processes such as incomplete lineage sorting (ILS) could be at play concurrently with hybridization, work in the last decade has shifted to computational approaches for phylogenetic network inference in the presence of ILS. In such a short period, significant advances have been made on developing and implementing such computational approaches. In particular, parsimony, likelihood, and Bayesian methods have been devised for estimating phylogenetic networks and associated parameters using estimated gene trees as data. Use of those inference methods has been augmented with statistical tests for specific hypotheses of hybridization, like the D-statistic. Most recently, Bayesian approaches for inferring phylogenetic networks directly from sequence data were developed and implemented. In this chapter, we survey such advances and discuss model assumptions as well as methods' strengths and limitations. We also discuss parallel efforts in the population genetics community aimed at inferring similar structures. Finally, we highlight major directions for future research in this area.