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Many recent studies have sought to quantify the degree to which viral phenotypic characters (such as epidemiological risk group, geographic location, cell tropism, drug resistance state, etc.) are correlated with shared ancestry, as represented by a viral phylogenetic tree. Here, we present a new Bayesian Markov-Chain Monte Carlo approach to the investigation of such phylogeny-trait correlations. This method accounts for uncertainty arising from phylogenetic error and provides a statistical significance test of the null hypothesis that traits are associated randomly with phylogeny tips. We perform extensive simulations to explore and compare the behaviour of three statistics of phylogeny-trait correlation. Finally, we re-analyse two existing published data sets as case studies. Our framework aims to provide an improvement over existing methods for this problem.

Original publication

DOI

10.1016/j.meegid.2007.08.001

Type

Journal article

Journal

Infect Genet Evol

Publication Date

05/2008

Volume

8

Pages

239 - 246

Keywords

Algorithms, Computer Simulation, Data Interpretation, Statistical, Empirical Research, Phenotype, Phylogeny, Uncertainty, Virus Physiological Phenomena, Viruses