Identifiability of species network topologies from genomic sequences using the logDet distance

TL;DR

This paper demonstrates that logDet distances derived from genomic sequences can reliably recover species network topologies under certain models, enabling more efficient inference without analyzing individual gene trees.

Contribution

It shows that logDet distances retain sufficient information for network inference in the level-1 ultrametric case, simplifying the analysis process.

Findings

LogDet distances can recover network relationships under the Network Multispecies Coalescent model.

Inference does not require partitioning sequences by genes.

Results apply to the level-1 ultrametric case.

Abstract

Inference of network-like evolutionary relationships between species from genomic data must address the interwoven signals from both gene flow and incomplete lineage sorting. The heavy computational demands of standard approaches to this problem severely limit the size of datasets that may be analyzed, in both the number of species and the number of genetic loci. Here we provide a theoretical pointer to more efficient methods, by showing that logDet distances computed from genomic-scale sequences retain sufficient information to recover network relationships in the level-1 ultrametric case. This result is obtained under the Network Multispecies Coalescent model combined with a mixture of General Time-Reversible sequence evolution models across individual gene trees, but does not depend on partitioning sequences by genes. Thus under standard stochastic models statistically justifiable inference of network relationships from sequences can be accomplished without consideration of individual genes or gene trees.