Quartet-Based Inference Methods are Statistically Consistent Under the Unified Duplication-Loss-Coalescence Model

Abstract: The classic multispecies coalescent (MSC) model provides the means for theoretical justification of incomplete lineage sorting-aware species tree inference methods. A large body of work in phylogenetics is dedicated to the design of inference methods that are statistically consistent under MSC. One of such particularly popular methods is ASTRAL, a quartet-based species tree inference method. A few recent studies suggested that ASTRAL also performs well when given multi-locus gene trees in simulation studies. Further, Legried et al. recently demonstrated that ASTRAL is statistically consistent under the gene duplication and loss model (GDL). Note that GDL is prevalent in evolutionary histories and is a part of the powerful duplication-loss-coalescence evolutionary model (DLCoal) by Rasmussen and Kellis. In this work we prove that ASTRAL is statistically consistent under the general DLCoal model. Therefore, our result supports the empirical evidence from the simulation-based studies. More broadly, we prove that a randomly chosen quartet from a gene tree (with unique taxa) is more likely to agree with the respective species tree quartet than any of the two other quartets.