Title: Locally-adaptive Bayesian nonparametric inference for phylodynamics,
Abstract: Phylodynamics is an area of population genetics that uses genetic sequence
data to estimate past population dynamics. Modern state-of-the-art Bayesian
nonparametric methods for phylodynamics use either change-point models or
Gaussian process priors to recover population size trajectories of unknown
form. Change-point models suffer from computational issues when the number of
change-points is unknown and needs to be estimated. Gaussian process-based
methods lack local adaptivity and cannot accurately recover trajectories that
exhibit features such as abrupt changes in trend or varying levels of
smoothness. We propose a novel, locally-adaptive approach to Bayesian
nonparametric phylodynamic inference that has the flexibility to accommodate a
large class of functional behaviors. Local adaptivity results from modeling the
log-transformed effective population size a priori as a horseshoe Markov random
field, a recently proposed statistical model that blends together the best
properties of the change-point and Gaussian process modeling paradigms. We use
simulated data to assess model performance, and find that our proposed method
results in reduced bias and increased precision when compared to contemporary
methods. We also use our models to reconstruct past changes in genetic
diversity of human hepatitis C virus in Egypt and to estimate population size
changes of ancient and modern steppe bison. These analyses show that our new
method captures features of the population size trajectories that were missed
by the state-of-the-art phylodynamic methods.