Open Access
June 2015 Assessing phenotypic correlation through the multivariate phylogenetic latent liability model
Gabriela B. Cybis, Janet S. Sinsheimer, Trevor Bedford, Alison E. Mather, Philippe Lemey, Marc A. Suchard
Ann. Appl. Stat. 9(2): 969-991 (June 2015). DOI: 10.1214/15-AOAS821


Understanding which phenotypic traits are consistently correlated throughout evolution is a highly pertinent problem in modern evolutionary biology. Here, we propose a multivariate phylogenetic latent liability model for assessing the correlation between multiple types of data, while simultaneously controlling for their unknown shared evolutionary history informed through molecular sequences. The latent formulation enables us to consider in a single model combinations of continuous traits, discrete binary traits and discrete traits with multiple ordered and unordered states. Previous approaches have entertained a single data type generally along a fixed history, precluding estimation of correlation between traits and ignoring uncertainty in the history. We implement our model in a Bayesian phylogenetic framework, and discuss inference techniques for hypothesis testing. Finally, we showcase the method through applications to columbine flower morphology, antibiotic resistance in Salmonella and epitope evolution in influenza.


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Gabriela B. Cybis. Janet S. Sinsheimer. Trevor Bedford. Alison E. Mather. Philippe Lemey. Marc A. Suchard. "Assessing phenotypic correlation through the multivariate phylogenetic latent liability model." Ann. Appl. Stat. 9 (2) 969 - 991, June 2015.


Received: 1 June 2014; Revised: 1 January 2015; Published: June 2015
First available in Project Euclid: 20 July 2015

zbMATH: 06499939
MathSciNet: MR3371344
Digital Object Identifier: 10.1214/15-AOAS821

Keywords: Bayesian phylogenetics , Evolution , genotype-phenotype correlation , threshold model

Rights: Copyright © 2015 Institute of Mathematical Statistics

Vol.9 • No. 2 • June 2015
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