Horseshoe-based Bayesian nonparametric estimation of effective population size trajectories

TL;DR

This paper introduces a novel Bayesian nonparametric method using horseshoe Markov random fields for flexible, locally-adaptive estimation of population size trajectories from genetic data, outperforming existing methods.

Contribution

The paper proposes a new horseshoe-based Bayesian nonparametric approach that improves local adaptivity and accuracy in reconstructing population dynamics from genetic sequences.

Findings

Reduced bias and increased precision in simulations

Better capture of abrupt changes in population size

Successful application to human hepatitis C and steppe bison data

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 recovering population size trajectories of unknown form use either change-point models or Gaussian process priors. 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 methods.