Inferring time-dependent migration and coalescence patterns from genetic sequence and predictor data in structured populations

Open access
Date
2019-07Type
- Journal Article
Abstract
Population dynamics can be inferred from genetic sequence data by using phylodynamic methods. These methods typically quantify the dynamics in unstructured populations or assume migration rates and effective population sizes to be constant through time in structured populations. When considering rates to vary through time in structured populations, the number of parameters to infer increases rapidly and the available data might not be sufficient to inform these. Additionally, it is often of interest to know what predicts these parameters rather than knowing the parameters themselves. Here, we introduce a method to infer the predictors for time-varying migration rates and effective population sizes by using a generalized linear model (GLM) approach under the marginal approximation of the structured coalescent. Using simulations, we show that our approach is able to reliably infer the model parameters and its predictors from phylogenetic trees. Furthermore, when simulating trees under the structured coalescent, we show that our new approach outperforms the discrete trait GLM model. We then apply our framework to a previously described Ebola virus dataset, where we infer the parameters and its predictors from genome sequences while accounting for phylogenetic uncertainty. We infer weekly cases to be the strongest predictor for effective population size and geographic distance the strongest predictor for migration. This approach is implemented as part of the BEAST2 package MASCOT, which allows us to jointly infer population dynamics, i.e. the parameters and predictors, within structured populations, the phylogenetic tree, and evolutionary parameters. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000388307Publication status
publishedExternal links
Journal / series
Virus EvolutionVolume
Pages / Article No.
Publisher
Oxford University PressSubject
GLM; phylogeography; infectious disease; phylogenetics; BEASTOrganisational unit
09490 - Stadler, Tanja / Stadler, Tanja
Funding
166258 - System analysis of seasonal Influenza - virus transmission and evolution in the City of Basel (SNF)
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