Inferência bayesiana da evolução da distribuição de um fenótipo em uma árvore filogenética

sexta-feira, setembro 09, 2016

Bayesian Inference of the Evolution of a Phenotype Distribution on a Phylogenetic Tree

M. Azim Ansari, Xavier Didelot

GENETICS September 1, 2016 vol. 204 no. 1 89-98; 

Fig. 1 - Illustration of the model. Changepoints occurred on three branches, which divided the tree into four sections (white, blue, green, and yellow), each of which has different probabilities of the first (black) and second (red) phenotypes.


The distribution of a phenotype on a phylogenetic tree is often a quantity of interest. Many phenotypes have imperfect heritability, so that a measurement of the phenotype for an individual can be thought of as a single realization from the phenotype distribution of that individual. If all individuals in a phylogeny had the same phenotype distribution, measured phenotypes would be randomly distributed on the tree leaves. This is, however, often not the case, implying that the phenotype distribution evolves over time. Here we propose a new model based on this principle of evolving phenotype distribution on the branches of a phylogeny, which is different from ancestral state reconstruction where the phenotype itself is assumed to evolve. We develop an efficient Bayesian inference method to estimate the parameters of our model and to test the evidence for changes in the phenotype distribution. We use multiple simulated data sets to show that our algorithm has good sensitivity and specificity properties. Since our method identifies branches on the tree on which the phenotype distribution has changed, it is able to break down a tree into components for which this distribution is unique and constant. We present two applications of our method, one investigating the association between HIV genetic variation and human leukocyte antigen and the other studying host range distribution in a lineage of Salmonella enterica, and we discuss many other potential applications.



The authors thank Remi Bardenet, Daniel Falush, Philip Maybank, and Gil McVean for their insightful discussions. This research was made possible by a James Martin fellowship (awarded to M.A.A.). X.D. received funding from the Biotechnology and Biological Sciences Research Council (BB/L023458/1) and the National Institute for Health Research (HPRU-2012-10080).


Communicating editor: M. A. Beaumont

Supplemental material is available online at 

Received April 18, 2016 Accepted July 7, 2016 

Copyright © 2016 Ansari and Didelot

Available freely online through the author-supported open access option.

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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