Composite likelihood approaches to phylogenetic inference under the multispecies coalescent

Music City Center 

Species-level phylogenetic inference under the multispecies coalescent model remains challenging in the typical inference frameworks (e.g., the likelihood and Bayesian frameworks) due to the dimensionality of the space of both gene trees and species trees. Algebraic approaches intended to establish identifiability of species tree parameters have suggested computationally efficient inference procedures that have been widely used by empiricists and that have good theoretical properties, such as statistical consistency. However, such approaches are less powerful than approaches based on the full likelihood. In this talk, I will describe how the use of a composite likelihood approach enables computationally tractable statistical inference of the species-level phylogenetic relationships for genome-scale data. In particular, asymptotic properties of estimators obtained in the composite-likelihood framework will be derived, and the utility of the methods developed will be demonstrated with both simulated and empirical data.

Composite likelihood

Phylogenetics

Multispecies coalescent

pseudo likelihood

DNA sequences