Maximum Likelihood Supertrees

TL;DR

This paper introduces a maximum-likelihood method for constructing supertrees that is statistically consistent under an exponential error model, outperforming the traditional MRP method, and extends this approach to gene trees considering incomplete lineage sorting.

Contribution

It presents a new ML supertree reconstruction method with proven statistical consistency and extends it to gene trees accounting for incomplete lineage sorting.

Findings

ML supertrees are statistically consistent under the exponential error model.

The proposed ML approach outperforms MRP, which can be inconsistent.

The method applies to gene trees, addressing incomplete lineage sorting.

Abstract

We analyse a maximum-likelihood approach for combining phylogenetic trees into a larger `supertree'. This is based on a simple exponential model of phylogenetic error, which ensures that ML supertrees have a simple combinatorial description (as a median tree, minimising a weighted sum of distances to the input trees). We show that this approach to ML supertree reconstruction is statistically consistent (it converges on the true species supertree as more input trees are combined), in contrast to the widely-used MRP method, which we show can be statistically inconsistent under the exponential error model. We also show that this statistical consistency extends to an ML approach for constructing species supertrees from gene trees. In this setting, incomplete lineage sorting (due to coalescence rates of homologous genes being lower than speciation rates) has been shown to lead to gene trees that are frequently different from species trees, and this can confound efforts to reconstruct the species phylogeny correctly.