The Structured Coalescent and Its Approximations

Abstract

Phylogeographic methods can help reveal the movement of genes between populations of organisms. This has been widely done to quantify pathogen movement between different host populations, the migration history of humans, and the geographic spread of languages or gene flow between species using the location or state of samples alongside sequence data. Phylogenies therefore offer insights into migration processes not available from classic epidemiological or occurrence data alone. Phylogeographic methods have however several known shortcomings. In particular, one of the most widely used methods treats migration the same as mutation, and therefore does not incorporate information about population demography. This may lead to severe biases in estimated migration rates for data sets where sampling is biased across populations. The structured coalescent on the other hand allows us to coherently model the migration and coalescent process, but current implementations struggle with complex data sets due to the need to infer ancestral migration histories. Thus, approximations to the structured coalescent, which integrate over all ancestral migration histories, have been developed. However, the validity and robustness of these approximations remain unclear. We present an exact numerical solution to the structured coalescent that does not require the inference of migration histories. Although this solution is computationally unfeasible for large data sets, it clarifies the assumptions of previously developed approximate methods and allows us to provide an improved approximation to the structured coalescent. We have implemented these methods in BEAST2, and we show how these methods compare under different scenarios.

BEAST 2.5: An Advanced Software Platform for Bayesian Evolutionary Analysis

“…Furthermore, the package needs to assume constant 318 population sizes through time for the different demes. These limitations have been 319 overcome by tracking ancestral states probabilistically using different 320 approximations [30,76], avoiding the need to sample ancestral states using MCMC. The 321 approximation originally proposed by [30] tracks state probabilities assuming that the 322 state of each lineage evolves completely independently of other lineages in the phylogeny.…”

nosoi: a stochastic agent-based transmission chain simulation framework in R

“…As such, it enables accurate testing of popular inference methods in both discrete and continuous phylogeography using either maximum-likelihood [27] or Bayesian inference [18,28,29], which are widely used in pathogen phylodynamics. In that regard, an interesting application of our proposed simulation framework could be to study the increasingly popular structured coalescent models [30][31][32], and to nosoi enables the simulation of real-life scenarios of viral outbreaks, and we provide several example scenarios to showcase its capabilities to generate a single transmission chain using different settings. An important aspect is that the resulting transmission tree, which describes the transmission events between infected hosts, differs from the phylogenetic tree, which describes the ancestral genetic relationships between pathogens sampled from these hosts.…”

Characterising the epidemic spread of Influenza A/H3N2 within a city through phylogenetics

“…Through 4 rapid evolution, influenza strains evade host immunity, allowing them to reinfect large 5 fractions of a population every year. In order to prevent infections, limited public health 6 resources have to be streamlined as efficiently as possible [1]. The planning of 7 interventions is dependent upon knowledge of the dynamics of epidemic spread of 8 influenza viruses in a city environment, which includes understanding the drivers of the 9 spread of seasonal influenza between individuals.…”

“…Such 18 a view on part of the influenza transmission chain allows to further quantify the 19 epidemiological dynamics which gave rise to the observed phylogenetic tree using 20 phylodynamic methods [2]. Phylogenetics and phylodynamics thus allows us to 21 elucidate past epidemiological dynamics [3,4] or to infer migration patterns [5,6].…”