Abstract
Phylogenetic comparative methods correct for shared evolutionary history among a set of nonindependent organisms by modeling sample traits as arising from a diffusion process along the branches of a possibly unknown history. To incorporate such uncertainty, we present a scalable Bayesian inference framework under a general Gaussian trait evolution model that exploits Hamiltonian Monte Carlo (HMC). HMC enables efficient sampling of the constrained model parameters and takes advantage of the tree structure for fast likelihood and gradient computations, yielding algorithmic complexity linear in the number of observations. This approach encompasses a wide family of stochastic processes, including the general Ornstein–Uhlenbeck (OU) process, with possible missing data and measurement errors. We implement inference tools for a biologically relevant subset of all these models into the BEAST phylogenetic software package and develop model comparison through marginal likelihood estimation. We apply our approach to study the morphological evolution in the superfamily of Musteloidea (including weasels and allies) as well as the heritability of HIV virulence. This second problem furnishes a new measure of evolutionary heritability that demonstrates its utility through a targeted simulation study.
Highlights
We argue in Appendix D that, when the process is not a simple Brownian Motion (BM), the population variance is more appropriate, as the empirical variance might be impaired by confounding inter-group effects if the tips are expected to have different means under the trait evolution model, which is for instance the case for an OU model on a non-ultrametric tree
This is in line with the selection strength estimates, with a phylogenetic half-life of 0.27 (95% HPDI [0.1, 0.61]) for the GSVL, and 0.12 (95% HPDI [0.05, 0.26]) for the CD4 slope
The CD4 slope has a higher selection strength, so that the phylogenetic model allows for more individual variation, and the heritability, which is the relative importance of this phylogenetic model in the total variation, is mechanically higher
Summary
Pennell and Harmon, 2013, for a review). To account for correlation induced by this shared history, phylogenetic comparative methods (PCMs) have been developed for the analysis of quantitative traits These methods can be applied to a wide range of organisms and traits, to answer a large spectrum of biological questions on various evolutionary time frames, ranging from decades or even years in virology (Dudas et al, 2017) to millions of years in evolutionary biology (Aristide et al., 2016). The underlying biological processes at play often have complex dynamics, and are only measured imperfectly, with a variable amount of noise
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