Deformed wing virus is a recent global epidemic in honeybees driven by Varroa mites

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Abstract

Deformed wing virus (DWV) and its vector, the mite Varroa destructor, are a major threat to the world's honeybees. Although the impact of Varroa on colony-level DWV epidemiology is evident, we have little understanding of wider DWV epidemiology and the role that Varroa has played in its global spread. A phylogeographic analysis shows that DWV is globally distributed in honeybees, having recently spread from a common source, the European honeybee Apis mellifera. DWV exhibits epidemic growth and transmission that is predominantly mediated by European and North American honeybee populations and driven by trade and movement of honeybee colonies. DWV is now an important reemerging pathogen of honeybees, which are undergoing a worldwide manmade epidemic fueled by the direct transmission route that the Varroa mite provides.

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Improving the accuracy of demographic and molecular clock model comparison while accommodating phylogenetic uncertainty.

Guy Baele, Philippe Lemey, Trevor Bedford … (2012)

Recent developments in marginal likelihood estimation for model selection in the field of Bayesian phylogenetics and molecular evolution have emphasized the poor performance of the harmonic mean estimator (HME). Although these studies have shown the merits of new approaches applied to standard normally distributed examples and small real-world data sets, not much is currently known concerning the performance and computational issues of these methods when fitting complex evolutionary and population genetic models to empirical real-world data sets. Further, these approaches have not yet seen widespread application in the field due to the lack of implementations of these computationally demanding techniques in commonly used phylogenetic packages. We here investigate the performance of some of these new marginal likelihood estimators, specifically, path sampling (PS) and stepping-stone (SS) sampling for comparing models of demographic change and relaxed molecular clocks, using synthetic data and real-world examples for which unexpected inferences were made using the HME. Given the drastically increased computational demands of PS and SS sampling, we also investigate a posterior simulation-based analogue of Akaike's information criterion (AIC) through Markov chain Monte Carlo (MCMC), a model comparison approach that shares with the HME the appealing feature of having a low computational overhead over the original MCMC analysis. We confirm that the HME systematically overestimates the marginal likelihood and fails to yield reliable model classification and show that the AICM performs better and may be a useful initial evaluation of model choice but that it is also, to a lesser degree, unreliable. We show that PS and SS sampling substantially outperform these estimators and adjust the conclusions made concerning previous analyses for the three real-world data sets that we reanalyzed. The methods used in this article are now available in BEAST, a powerful user-friendly software package to perform Bayesian evolutionary analyses.

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Smooth skyride through a rough skyline: Bayesian coalescent-based inference of population dynamics.

Vladimir N. Minin, Erik W Bloomquist, Marc Suchard (2008)

Kingman's coalescent process opens the door for estimation of population genetics model parameters from molecular sequences. One paramount parameter of interest is the effective population size. Temporal variation of this quantity characterizes the demographic history of a population. Because researchers are rarely able to choose a priori a deterministic model describing effective population size dynamics for data at hand, nonparametric curve-fitting methods based on multiple change-point (MCP) models have been developed. We propose an alternative to change-point modeling that exploits Gaussian Markov random fields to achieve temporal smoothing of the effective population size in a Bayesian framework. The main advantage of our approach is that, in contrast to MCP models, the explicit temporal smoothing does not require strong prior decisions. To approximate the posterior distribution of the population dynamics, we use efficient, fast mixing Markov chain Monte Carlo algorithms designed for highly structured Gaussian models. In a simulation study, we demonstrate that the proposed temporal smoothing method, named Bayesian skyride, successfully recovers "true" population size trajectories in all simulation scenarios and competes well with the MCP approaches without evoking strong prior assumptions. We apply our Bayesian skyride method to 2 real data sets. We analyze sequences of hepatitis C virus contemporaneously sampled in Egypt, reproducing all key known aspects of the viral population dynamics. Next, we estimate the demographic histories of human influenza A hemagglutinin sequences, serially sampled throughout 3 flu seasons.

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Deformed wing virus.

Joachim R. de Miranda, Elke Genersch (2010)

Deformed wing virus (DWV; Iflaviridae) is one of many viruses infecting honeybees and one of the most heavily investigated due to its close association with honeybee colony collapse induced by Varroadestructor. In the absence of V.destructor DWV infection does not result in visible symptoms or any apparent negative impact on host fitness. However, for reasons that are still not fully understood, the transmission of DWV by V.destructor to the developing pupae causes clinical symptoms, including pupal death and adult bees emerging with deformed wings, a bloated, shortened abdomen and discolouration. These bees are not viable and die soon after emergence. In this review we will summarize the historical and recent data on DWV and its relatives, covering the genetics, pathobiology, and transmission of this important viral honeybee pathogen, and discuss these within the wider theoretical concepts relating to the genetic variability and population structure of RNA viruses, the evolution of virulence and the development of disease symptoms. Copyright 2009 Elsevier Inc. All rights reserved.

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Journal

Title: Science

Abbreviated Title: Science

Publisher: American Association for the Advancement of Science (AAAS)

ISSN (Print): 0036-8075

ISSN (Electronic): 1095-9203

Publication date Created: February 04 2016

Publication date Created: February 05 2016

Publication date (Electronic): February 04 2016

Publication date (Print): February 05 2016

Volume: 351

Issue: 6273

Pages: 594-597

Article

DOI: 10.1126/science.aac9976

PubMed ID: 26912700

SO-VID: ec491fd0-c57a-492f-bd51-0574843b44c3

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Deformed wing virus is a recent global epidemic in honeybees driven by Varroa mites

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Most cited references 37

Improving the accuracy of demographic and molecular clock model comparison while accommodating phylogenetic uncertainty.

Smooth skyride through a rough skyline: Bayesian coalescent-based inference of population dynamics.

Deformed wing virus.

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