Improving the identification of antigenic sites in the H1N1 Influenza virus through accounting for the experimental structure in a sparse hierarchical Bayesian model

Davies, V. , Harvey, W. T., Reeve, R. and Husmeier, D. (2019) Improving the identification of antigenic sites in the H1N1 Influenza virus through accounting for the experimental structure in a sparse hierarchical Bayesian model. Journal of the Royal Statistical Society: Series C (Applied Statistics), (doi:10.1111/rssc.12338) (Early Online Publication)

Davies, V. , Harvey, W. T., Reeve, R. and Husmeier, D. (2019) Improving the identification of antigenic sites in the H1N1 Influenza virus through accounting for the experimental structure in a sparse hierarchical Bayesian model. Journal of the Royal Statistical Society: Series C (Applied Statistics), (doi:10.1111/rssc.12338) (Early Online Publication)

[img]
Preview
Text
175258.pdf - Published Version
Available under License Creative Commons Attribution.

840kB
[img]
Preview
Text
175258suppl.pdf - Supplemental Material

1MB

Abstract

Understanding how genetic changes allow emerging virus strains to escape the protection afforded by vaccination is vital for the maintenance of effective vaccines. We use structural and phylogenetic differences between pairs of virus strains to identify important antigenic sites on the surface of the influenza A(H1N1) virus through the prediction of haemagglutination inhibition (HI) titre: pairwise measures of the antigenic similarity of virus strains. We propose a sparse hierarchical Bayesian model that can deal with the pairwise structure and inherent experimental variability in the H1N1 data through the introduction of latent variables. The latent variables represent the underlying HI titre measurement of any given pair of virus strains and help to account for the fact that, for any HI titre measurement between the same pair of virus strains, the difference in the viral sequence remains the same. Through accurately representing the structure of the H1N1 data, the model can select virus sites which are antigenic, while its latent structure achieves the computational efficiency that is required to deal with large virus sequence data, as typically available for the influenza virus. In addition to the latent variable model, we also propose a new method, the block‐integrated widely applicable information criterion biWAIC, for selecting between competing models. We show how this enables us to select the random effects effectively when used with the model proposed and we apply both methods to an A(H1N1) data set.

Item Type:Articles
Status:Early Online Publication
Refereed:Yes
Glasgow Author(s) Enlighten ID:Reeve, Dr Richard and Davies, Mr Vincent and Husmeier, Professor Dirk and Harvey, Dr William
Authors: Davies, V., Harvey, W. T., Reeve, R., 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 Computing Science
College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Journal of the Royal Statistical Society: Series C (Applied Statistics)
Publisher:Wiley
ISSN:0035-9254
ISSN (Online):1467-9876
Published Online:03 February 2019
Copyright Holders:Copyright © 2019 The Authors
First Published:First published in Journal of the Royal Statistical Society: Series C (Applied Statistics) 2019
Publisher Policy:Reproduced under a Creative Commons License

University Staff: Request a correction | Enlighten Editors: Update this record

Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
3009820Exploiting Closed-Loop Aspects in Computationally and Data Intensive AnalyticsRoderick Murray-SmithEngineering and Physical Sciences Research Council (EPSRC)EP/R018634/1Computing Science
580944MRC Doctoral Training Grant 2011-2015Mary Beth KneafseyMedical Research Council (MRC)MR/J50032X/1 (AFS3)VPO VICE PRINCIPAL RESEARCH & ENTERPRISE
3026320Using a comparative One Health approach to investigate the structural basis of antigenic variation among human and avian influenza virusesJill PellMedical Research Council (MRC)MR/R024758/1Institute of Biodiversity, Animal Health and Comparative Medicine
632421An effective vaccination programme for the eradication of foot-and-mouth disease from IndiaRichard ReeveBiotechnology and Biological Sciences Research Council (BBSRC)BB/L004828/1 1805RI BIODIVERSITY ANIMAL HEALTH & COMPMED
730011Mathematical Theory and Biological Applications of DiversityRichard ReeveBiotechnology and Biological Sciences Research Council (BBSRC)BB/P004202/1RI BIODIVERSITY ANIMAL HEALTH & COMPMED