Incorporating compositional heterogeneity into Lie Markov models for phylogenetic inference

TL;DR

This paper introduces a non-stationary, non-reversible Lie Markov model for phylogenetics that can distinguish rooted trees, addressing limitations of traditional models that assume stationarity and reversibility.

Contribution

It develops a novel non-stationary, non-reversible Lie Markov model for phylogenetic inference, enabling root identification and better biological interpretation.

Findings

Model distinguishes rooted trees where traditional models cannot.

Bayesian inference with MCMC implemented for the new model.

Application shows improved root identification in biological data.

Abstract

Phylogenetics uses alignments of molecular sequence data to learn about evolutionary trees. Substitutions in sequences are modelled through a continuous-time Markov process, characterised by an instantaneous rate matrix, which standard models assume is time-reversible and stationary. These assumptions are biologically questionable and induce a likelihood function which is invariant to a tree's root position. This hampers inference because a tree's biological interpretation depends critically on where it is rooted. Relaxing both assumptions, we introduce a model whose likelihood can distinguish between rooted trees. The model is non-stationary, with step changes in the instantaneous rate matrix at each speciation event. Exploiting recent theoretical work, each rate matrix belongs to a non-reversible family of Lie Markov models. These models are closed under matrix multiplication, so our extension offers the conceptually appealing property that a tree and all its sub-trees could have arisen from the same family of non-stationary models. We adopt a Bayesian approach, describe an MCMC algorithm for posterior inference and provide software. The biological insight that our model can provide is illustrated through an analysis in which non-reversible but stationary, and non-stationary but reversible models cannot identify a plausible root.