Rooting Phylogenetic Trees Through Bayesian Models of DNA Sequence Evolution

• Activity Number: 555
• Type: Contributed
• Date/Time: Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Sarah Heaps*
• Companies: Newcastle University
• Keywords: Phylogenetics ; Bayesian hierarchical modelling ; Root inference

Abstract

Alignments of molecular sequence data for a group of species are used to learn about their phylogeny, an evolutionary tree which places these species as leaves and ancestors as internal nodes. Sequence evolution on each branch of the tree is generally modelled using a continuous time Markov process which is common to all branches. Typically, this process is assumed to be stationary and time-reversible. Whilst these assumptions offer mathematical simplification, the associated likelihood does not depend on the position of the root, an aspect fundamental to the biological interpretation of trees.

We propose two models which relax some of these assumptions and allow root inference. The first model is homogeneous across branches, but non-stationary and non-reversible. The second, more biologically motivated model, is non-stationary and allows step-changes in the Markov process parameters at each node of the tree. We formulate both models in a Bayesian framework with prior distributions constructed to provide shrinkage towards structured forms. The performances of the models are compared in analyses of alignments for which there is strong biological opinion about the rooted phylogeny.