An Efficient Bayesian Inference Framework for Coalescent-Based Nonparametric Phylodynamics

Phylodynamics focuses on the problem of reconstructing past population size dynamics from current genetic samples taken from the population of interest. This technique has been extensively used in many areas of biology, but is particularly useful for studying the spread of quickly evolving infectious diseases agents, e.g.,\ influenza virus. Phylodynamics inference uses a coalescent model that defines a probability density for the genealogy of randomly sampled individuals from the population. When we assume that such a genealogy is known, the coalescent model, equipped with a Gaussian process prior on population size trajectory, allows for nonparametric Bayesian estimation of population size dynamics. While this approach is quite powerful, large data sets collected during infectious disease surveillance challenge the state-of-the-art of Bayesian phylodynamics and demand computationally more efficient inference framework. To satisfy this demand, we provide a computationally efficient Bayesian inference framework based on Hamiltonian Monte Carlo for coalescent process models. Moreover, we show that by splitting the Hamiltonian function we can further improve the efficiency of this approach. Using several simulated and real datasets, we show that our method provides accurate estimates of population size dynamics and is substantially faster than alternative methods based on elliptical slice sampler and Metropolis-adjusted Langevin algorithm

Horseshoe-based Bayesian nonparametric estimation of effective population size trajectories

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.Comment: 36 pages, including supplementary informatio

Integrated nested laplace approximation for bayesian nonparametric phylodynamics

The goal of phylodynamics, an area on the intersection of phylogenetics and population genetics, is to reconstruct population size dynamics from genetic data. Recently, a series of nonparametric Bayesian methods have been proposed for such demographic reconstructions. These methods rely on prior specifications based on Gaussian processes and proceed by approximating the posterior distribution of population size trajectories via Markov chain Monte Carlo (MCMC) methods. In this paper, we adapt an integrated nested Laplace approximation (INLA), a recently proposed approximate Bayesian inference for latent Gaussian models, to the estimation of population size trajectories. We show that when a genealogy of sampled individuals can be reliably estimated from genetic data, INLA enjoys high accuracy and can replace MCMC entirely. We demonstrate significant computational efficiency over the state-of-the-art MCMC methods. We illustrate INLA-based population size inference using simulations and genealogies of hepatitis C and human influenza viruses

Integrated nested laplace approximation for bayesian nonparametric phylodynamics

The goal of phylodynamics, an area on the intersection of phylogenetics and population genetics, is to reconstruct population size dynamics from genetic data. Recently, a series of nonparametric Bayesian methods have been proposed for such demographic reconstructions. These methods rely on prior specifications based on Gaussian processes and proceed by approximating the posterior distribution of population size trajectories via Markov chain Monte Carlo (MCMC) methods. In this paper, we adapt an integrated nested Laplace approximation (INLA), a recently proposed approximate Bayesian inference for latent Gaussian models, to the estimation of population size trajectories. We show that when a genealogy of sampled individuals can be reliably estimated from genetic data, INLA enjoys high accuracy and can replace MCMC entirely. We demonstrate significant computational efficiency over the state-of-the-art MCMC methods. We illustrate INLA-based population size inference using simulations and genealogies of hepatitis C and human influenza viruses

Phylodynamic modelling of foot-and-mouth disease virus sequence data

The under-reporting of cases of infectious diseases is a substantial impediment to the control and management of infectious diseases in both epidemic and endemic contexts. Information about infectious disease dynamics can be recovered from sequence data using time-varying coalescent approaches, and phylodynamic models have been developed in order to reconstruct demographic changes of the numbers of infected hosts through time. In this study I have demonstrated the general concordance between empirically observed epidemiological incidence data and viral demography inferred through analysis of foot-and-mouth disease virus VP1 coding sequences belonging to the CATHAY topotype over large temporal and spatial scales. However a more precise and robust relationship between the effective population size