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

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)969-991
Number of pages23
JournalAnnals of Applied Statistics
Volume9
Issue number2
DOIs
Publication statusPublished - 1 Jun 2015

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