Graph hierarchies for phylogeography

Abstract

One contribution of 18 to a Discussion Meeting Issue 'Next-generation molecular and evolutionary epidemiology of infectious disease'. Bayesian phylogeographic methods simultaneously integrate geographical and evolutionary modelling, and have demonstrated value in assessing spatial spread patterns of measurably evolving organisms. We improve on existing phylogeographic methods by combining information from multiple phylogeographic datasets in a hierarchical setting. Consider N exchangeable datasets or strata consisting of viral sequences and locations, each evolving along its own phylogenetic tree and according to a conditionally independent geographical process. At the hierarchical level, a random graph summarizes the overall dispersion process by informing which migration rates between sampling locations are likely to be relevant in the strata. This approach provides an efficient and improved framework for analysing inherently hierarchical datasets. We first examine the evolutionary history of multiple serotypes of dengue virus in the Americas to showcase our method. Additionally, we explore an application to intrahost HIV evolution across multiple patients.

Distinct rates and patterns of spread of the major HIV-1 subtypes in Central and East Africa

“…Because we find a significant tendency for CEA sequences to cluster according to location of sampling ( P < 0.001; S5–S7 Tables), we next investigated the spatial patterns of virus spread using an approach that combines information from multiple sources. Specifically, we used a hierarchical discrete phylogeographic model that shares a migration graph across subtypes while allowing some variability in the migration patterns at the subtype level [26, 30]. Although inference of location of the root of subtype C in Mbuji-Mayi was robust to the inclusion of East African data, the same was not true for the subtype A1 and D phylogenies, because of a much larger sample of East African sequences compared to the number of DRC sequences available for analysis.…”

“…To quantify the contribution of virus movement across locations we used a hierarchical phylogeographic approach that estimates which migration rates between locations are likely to be relevant [30]. We find that on average 60% of the total number of estimated virus lineage movement events (38 of 64 links supported by Bayes factor, BF > 10) occurred within national borders (S8–S11 Tables).…”

“…To identify a subset of well-supported migration events amongst subtypes we use a Bayesian Stochastic Search variable selection procedure (BSSVS) with a hierarchical prior on location and country indicators (0–1) that allow CTMC rates to shrink to zero with some probability [30]. Posterior distributions of country and location clock rates were obtained using separate conditional reference priors for each subtype [109].…”

Understanding dengue virus evolution to support epidemic surveillance and counter-measure development

“…Alicock et al noted in their American-Caribbean spatial DENV analysis that Barbados, Brazil, Colombia, Puerto Rico, Suriname, Venezuela may be such putative hubs of transmission [149]. de Arujo et al noted that, within Brazil, the North and South East were hubs of DENV-3 dissemination and could be prioritized for surveillance[143] [153,154]…”

Virus dynamics and phyloanatomy: Merging population dynamic and phylogenetic approaches

“…Another study that Salemi and Rife cite in their review is Cybis et al., in which viral flow between plasma, CD4 + and CD8 + T cells is investigated. This study identifies potential migration paths by Bayes factor, but does not estimate migration rates between compartments.…”