A Bayesian Approach to Phylogenetic Networks

  • Rosalba Radice

Student thesis: Doctoral ThesisPhD


Traditional phylogenetic inference assumes that the history of a set of taxa can be explained by a tree. This assumption is often violated as some biological entities can exchange genetic material giving rise to non-treelike events often called reticulations. Failure to consider these events might result in incorrectly inferred phylogenies, and further consequences, for example stagnant and less targeted drug development. Phylogenetic networks provide a flexible tool which allow us to model the evolutionary history of a set of organisms in the presence of reticulation events. In recent years, a number of methods addressing phylogenetic network reconstruction and evaluation have been introduced. One of suchmethods has been proposed byMoret et al. (2004). They defined a phylogenetic network as a directed acyclic graph obtained by positing a set of edges between pairs of the branches of an underlying tree to model reticulation events. Recently, two works by Jin et al. (2006), and Snir and Tuller (2009), respectively, using this definition of phylogenetic network, have appeared. Both works demonstrate the potential of using maximum likelihood estimation for phylogenetic network reconstruction. We propose a Bayesian approach to the estimation of phylogenetic network parameters. We allow for different phylogenies to be inferred at different parts of our DNA alignment in the presence of reticulation events, at the species level, by using the idea that a phylogenetic network can be naturally decomposed into trees. A Markov chainMonte Carlo algorithmis provided for posterior computation of the phylogenetic network parameters. Also a more general algorithm is proposed which allows the data to dictate how many phylogenies are required to explain the data. This can be achieved by using stochastic search variable selection. Both algorithms are tested on simulated data and also demonstrated on the ribosomal protein gene rps11 data from five flowering plants. The proposed approach can be applied to a wide variety of problems which aim at exploring the possibility of reticulation events in the history of a set of taxa.
Date of Award1 Jan 2011
Original languageEnglish
Awarding Institution
  • University of Bath
SupervisorEdward Feil (Supervisor)


  • phylogenetic networks
  • Bayesian Statitics
  • Markov chain Monte Carlo

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