The large-sample asymptotic behaviour of quartet-based summary methods for species tree inference

Yao-Ban Chan (1) , Qiuyi Li (1) , Celine Scornavacca (2)

1 University of Melbourne
2 UMR ISEM - Institut des Sciences de l'Evolution de Montpellier

Summary methods seek to infer a species tree from a set of gene trees. A desirable property of such methods is that of statistical consistency; that is, the probability of inferring the wrong species tree (the error probability) tends to 0 as the number of input gene trees becomes large. A popular paradigm is to infer a species tree that agrees with the maximum number of quartets from the input set of gene trees; this has been proved to be statistically consistent under several models of gene evolution. In this paper, we study the asymptotic behaviour of the error probability of such methods in this limit, and show that it decays exponentially. For a 4-taxon species tree, we derive a closed form for the asymptotic behaviour in terms of the probability that the gene evolution process produces the correct topology. We also derive bounds for the sample complexity (the number of gene trees required to infer the true species tree with a given probability), which outperform existing bounds. We then extend our results to bounds for the asymptotic behaviour of the error probability for any species tree, and compare these to the true error probability for some model species trees using simulations.

Mots clés: species tree; asymptotic behaviour; sample complexity; multispecies coalescent

Domaines: Bio-informatique [q-bio.QM]

Yao-Ban Chan, Qiuyi Li, Celine Scornavacca. The large-sample asymptotic behaviour of quartet-based summary methods for species tree inference. Journal of Mathematical Biology, 2022, 85 (3), pp.1-22. ⟨10.1007/s00285-022-01786-4⟩. ⟨hal-03740193⟩