Efficient Bayesian inference under the structured coalescent

Abstract

Motivation: Population structure significantly affects evolutionary dynamics. Such structure may be due to spatial segregation, but may also reflect any other gene-flow-limiting aspect of a model. In combination with the structured coalescent, this fact can be used to inform phylogenetic tree reconstruction, as well as to infer parameters such as migration rates and subpopulation sizes from annotated sequence data. However, conducting Bayesian inference under the structured coalescent is impeded by the difficulty of constructing Markov Chain Monte Carlo (MCMC) sampling algorithms (samplers) capable of efficiently exploring the state space.Results: In this article, we present a new MCMC sampler capable of sampling from posterior distributions over structured trees: timed phylogenetic trees in which lineages are associated with the distinct subpopulation in which they lie. The sampler includes a set of MCMC proposal functions that offer significant mixing improvements over a previously published method. Furthermore, its implementation as a BEAST 2 package ensures maximum flexibility with respect to model and prior specification. We demonstrate the usefulness of this new sampler by using it to infer migration rates and effective population sizes of H3N2 influenza between New Zealand, New York and Hong Kong from publicly available hemagglutinin (HA) gene sequences under the structured coalescent.Availability and implementation: The sampler has been implemented as a publicly available BEAST 2 package that is distributed under version 3 of the GNU General Public License at http://compevol.github.io/MultiTypeTree.Contact: tgvaughan@gmail.comSupplementary information: Supplementary data are available at Bioinformatics online.

Persistent HIV-1 replication maintains the tissue reservoir during therapy

“…The model and its parameters were chosen after computing the posterior probability of several models to obtain the discriminatory Bayes factors. Because population structure, whether due to spatial segregation or limitations to gene flow, may affect evolutionary dynamics, we confirmed that the direction of flow was not due to oversampling of a particular environment, by running a two-deme Bayesian inference under a structured coalescent model with a HKY substitution model assuming a strict molecular clock 29 , which is less susceptible to sampling issues than our trait-based analysis. For completeness, we conducted a search for topologies and divergence times assuming a relaxed molecular clock as well.…”

“…These data, only revealed through temporarily and spatially resolved sampling, further support the conclusion that the pattern does not result from distinct populations of haplotypes being sampled from different compartments, but rather migration and colonization of haplotypes between lymphoid tissue and blood. A structured coalescent model 29 , less prone to potential bias in spatial inference estimates, shows higher migration rates from lymph node to blood (Extended Data Table 5), confirming that the direction of flow is not due to oversampling of a particular anatomic location that would have increased estimates of traffic into that location 30 .…”

The evolution within us

“…Following trends in infectious disease research [112,113], models could eventually integrate phenotypic information, such as the affinities and specificities of receptors for different epitopes. Another possible direction is to use structured coalescent approaches to infer rates of idiotype switching [114]. For now, there is a gap between repertoire analyses that are sequence-based and others that model the competitive dynamics and phenotypic evolution of clonal populations.…”

Virus dynamics and phyloanatomy: Merging population dynamic and phylogenetic approaches

“…They are widely used in epidemiology to quantify the epidemiological spread of viruses and to understand new epidemics in real‐time. This allows to locate the source of an epidemic, as well as parameters such as population size and basic reproductive number ( R 0 ), and migration rates between different locations . In phyloanatomy, these well‐established methods are applied to within‐host viral sequencing data to elucidate the importance of certain organs or cell types in the progress of an infection.…”