A Flexible Bayesian Framework to Study Viral Trait Evolution

Jeudi 25 avril 2019 14:00-15:00 - Paul Bastide - KU Leuven

Résumé : During the course of an epidemic, many viral pathogens are known to
evolve rapidly, leaving an imprint of the pattern of spread in their
genomes. Uncovering the molecular footprint of this transmission process
is a key goal of phylodynamic inference. Less focus has been put on the
evolution of quantitative traits of viruses, such as geographical
location or virulence. The goal of Phylogenetic Comparative Methods is
to account for a shared evolutionary history among a set of
non-independent samples. Conditioning on such an history, the observed
traits can be seen as the result of a stochastic process running on the
branches of a phylogenetic tree. We propose a Bayesian inference
framework for the study of this flexible model. Using a MCMC based
method, it relies on the efficient sampling of the constrained
parameters of the model, and takes advantage of the tree structure for
fast likelihood computations. It encompasses a wide family of Gaussian
processes, allowing for fine-grained modelling of trait evolution of
various biological systems. We implemented this new approach in the
phylogenetic software BEAST, and applied it to the study of heritability
of virulence in HIV.

Notes de dernières minutes :

A Flexible Bayesian Framework to Study Viral Trait Evolution  Version PDF