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

TL;DR

This paper introduces a sparse hierarchical Bayesian model that incorporates structural information and experimental variability to improve the identification of antigenic sites in H1N1 influenza, enhancing vaccine design insights.

Contribution

The study presents a novel Bayesian model with latent variables for better antigenic site detection and introduces biWAIC for model selection in influenza data analysis.

Findings

Accurately identified antigenic sites in H1N1 influenza.

Enhanced model selection using biWAIC.

Efficient handling of large virus sequence datasets.

Abstract

Understanding how genetic changes allow emerging virus strains to escape the protection afforded by vaccination is vital for the maintenance of effective vaccines. In the current work, 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) assay, 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 assay measurement of any given pair of virus strains and help account for the fact that for any HI assay 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 is able to select virus sites which are antigenic, while its latent structure achieves the computational efficiency 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, block integrated Widely Applicable Information Criterion (biWAIC), for selecting between competing models. We show how this allows us to effectively select the random effects when used with the proposed model and apply both methods to an A(H1N1) dataset.