Author:

Keywords:

Science & Technology, Life Sciences & Biomedicine, Physical Sciences, Mathematical & Computational Biology, Public, Environmental & Occupational Health, Medical Informatics, Medicine, Research & Experimental, Statistics & Probability, Research & Experimental Medicine, Mathematics, Bayesian nonparametric mixture models, phylodynamics, antigenic cartography, HUMAN-IMMUNODEFICIENCY-VIRUS, T-CELL RESPONSES, RHESUS-MONKEYS, MOSAIC VACCINES, HIV-1 VACCINE, LYMPHOCYTE RESPONSES, IMMUNE-RESPONSES, BREADTH, EPITOPE, DNA, Antigens, Viral, Bayes Theorem, Biostatistics, Cluster Analysis, Evolution, Molecular, Humans, Influenza A Virus, H1N1 Subtype, Influenza, Human, Likelihood Functions, Models, Genetic, Models, Immunological, Molecular Epidemiology, Phylogeny, Statistics, Nonparametric, Stochastic Processes, 0104 Statistics, 1117 Public Health and Health Services, 4202 Epidemiology, 4905 Statistics

Abstract:

Influenza is responsible for up to 500,000 deaths every year, and antigenic variability represents much of its epidemiological burden. To visualize antigenic differences across many viral strains, antigenic cartography methods use multidimensional scaling on binding assay data to map influenza antigenicity onto a low-dimensional space. Analysis of such assay data ideally leads to natural clustering of influenza strains of similar antigenicity that correlate with sequence evolution. To understand the dynamics of these antigenic groups, we present a framework that jointly models genetic and antigenic evolution by combining multidimensional scaling of binding assay data, Bayesian phylogenetic machinery and nonparametric clustering methods. We propose a phylogenetic Chinese restaurant process that extends the current process to incorporate the phylogenetic dependency structure between strains in the modeling of antigenic clusters. With this method, we are able to use the genetic information to better understand the evolution of antigenicity throughout epidemics, as shown in applications of this model to H1N1 influenza. Copyright © 2017 John Wiley & Sons, Ltd.