EFFICIENT BAYESIAN INFERENCE OF GENERAL GAUSSIAN MODELS ON LARGE PHYLOGENETIC TREES

• 成果类型: Article
• 署名作者: Bastide, Paul; Ho, Lam Si Tung; Baele, Guy; Lemey, Philippe; Suchard, Marc A.
• 署名单位: Universite de Montpellier; Centre National de la Recherche Scientifique (CNRS); Dalhousie University; KU Leuven; California State University System; California State University Los Angeles; California State University System; California State University Los Angeles; California State University System; California State University Los Angeles
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/20-AOAS1419
• 发表日期: 2021
• 页码: 971-997
• 关键词: trait evolution; likelihood-estimation; adaptive radiation; VIRAL LOAD; r package; rates; patterns; fossils; heritability; speciation

摘要

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.