Species tree inference from genomic sequences using the log-det distance

TL;DR

This paper demonstrates that the log-det distance, combined with a distance-based method, can consistently infer species trees from genomic sequences even under complex mixture models, offering a fast and reliable approach.

Contribution

It extends the use of log-det distance to species tree inference under mixture models, providing a theoretically sound and computationally efficient method.

Findings

Log-det distance enables consistent species tree inference under mixture models.

The method handles data from multiple loci with different gene trees.

Distance-based inference with log-det is simple and fast.

Abstract

The log-det distance between two aligned DNA sequences was introduced as a tool for statistically consistent inference of a gene tree under simple non-mixture models of sequence evolution. Here we prove that the log-det distance, coupled with a distance-based tree construction method, also permits consistent inference of species trees under mixture models appropriate to aligned genomic-scale sequences data. Data may include sites from many genetic loci, which evolved on different gene trees due to incomplete lineage sorting on an ultrametric species tree, with different time-reversible substitution processes. The simplicity and speed of distance-based inference suggests log-det based methods should serve as benchmarks for judging more elaborate and computationally-intensive species trees inference methods.