Assessing phenotypic correlation through the multivariate phylogenetic latent liability model

TL;DR

This paper introduces a Bayesian multivariate phylogenetic latent liability model that assesses correlations among diverse phenotypic traits across evolutionary history, accounting for uncertainty and different data types.

Contribution

It presents a novel integrated model for analyzing correlated phenotypic traits of various data types while considering shared evolutionary history.

Findings

Applied to columbine flower morphology, revealing trait correlations.

Analyzed antibiotic resistance evolution in Salmonella.

Studied epitope evolution in influenza virus.

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.