A Phylogenetic Regression Model for Studying Trait Evolution on Network

Abstract

A phylogenetic regression model that incorporates the network structure allowing the reticulation event to study trait evolution is proposed. The parameter estimation is achieved through the maximum likelihood approach, where an algorithm is developed by taking a phylogenetic network in eNewick format as the input to build up the variance–covariance matrix. The model is applied to study the common sunflower, Helianthus annuus, by investigating its traits used to respond to drought conditions. Results show that our model provides acceptable estimates of the parameters, where most of the traits analyzed were found to have a significant correlation with drought tolerance.

Keywords

• regression model
• phylogenetic comparative analysis
• variance–covariance matrix
• reticulate evolution
• Brownian motion