A sparse hierarchical Bayesian model for detecting relevant antigenic sites in virus evolution

Davies, V., Reeve, R. , Harvey, W. T., Maree, F. F. and Husmeier, D. (2017) A sparse hierarchical Bayesian model for detecting relevant antigenic sites in virus evolution. Computational Statistics, 32(3), pp. 803-843. (doi:10.1007/s00180-017-0730-6)

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Abstract

Understanding how viruses offer protection against closely related emerging strains is vital for creating effective vaccines. For many viruses, multiple serotypes often co-circulate and testing large numbers of vaccines can be infeasible. Therefore the development of an in silico predictor of cross-protection between strains is important to help optimise vaccine choice. Here we present a sparse hierarchical Bayesian model for detecting relevant antigenic sites in virus evolution (SABRE) which can account for the experimental variability in the data and predict antigenic variability. The method uses spike and slab priors to identify sites in the viral protein which are important for the neutralisation of the virus. Using the SABRE method we are able to identify a number of key antigenic sites within several viruses, as well as providing estimates of significant changes in the evolutionary history of the serotypes. We show how our method outperforms alternative established methods; standard mixed effects models, the mixed effects LASSO, and the mixed effects elastic nets. We also propose novel proposal mechanisms for the Markov chain Monte Carlo simulations, which improve mixing and convergence over that of the established component-wise Gibbs sampler.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Reeve, Dr Richard and Davies, Mr Vincent and Husmeier, Professor Dirk and Harvey, Mr William
Authors: Davies, V., Reeve, R., Harvey, W. T., Maree, F. F., and Husmeier, D.
College/School:College of Medical Veterinary and Life Sciences > Institute of Biodiversity Animal Health and Comparative Medicine
College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Computational Statistics
Publisher:Springer
ISSN:0943-4062
ISSN (Online):1613-9658
Published Online:13 June 2017
Copyright Holders:Copyright © 2017 The Authors
First Published:First published in Computational Statistics 32(3):803-843
Publisher Policy:Reproduced under a Creative Commons License

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