Bayesian nonparametric clustering in phylogenetics: modeling antigenic evolution in influenza. 2018

Gabriela B Cybis, and Janet S Sinsheimer, and Trevor Bedford, and Andrew Rambaut, and Philippe Lemey, and Marc A Suchard
Department of Statistics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil.

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.

UI MeSH Term Description Entries
D007251 Influenza, Human An acute viral infection in humans involving the respiratory tract. It is marked by inflammation of the NASAL MUCOSA; the PHARYNX; and conjunctiva, and by headache and severe, often generalized, myalgia. Grippe,Human Flu,Human Influenza,Influenza in Humans,Influenza,Flu, Human,Human Influenzas,Influenza in Human,Influenzas,Influenzas, Human
D008957 Models, Genetic Theoretical representations that simulate the behavior or activity of genetic processes or phenomena. They include the use of mathematical equations, computers, and other electronic equipment. Genetic Models,Genetic Model,Model, Genetic
D010802 Phylogeny The relationships of groups of organisms as reflected by their genetic makeup. Community Phylogenetics,Molecular Phylogenetics,Phylogenetic Analyses,Phylogenetic Analysis,Phylogenetic Clustering,Phylogenetic Comparative Analysis,Phylogenetic Comparative Methods,Phylogenetic Distance,Phylogenetic Generalized Least Squares,Phylogenetic Groups,Phylogenetic Incongruence,Phylogenetic Inference,Phylogenetic Networks,Phylogenetic Reconstruction,Phylogenetic Relatedness,Phylogenetic Relationships,Phylogenetic Signal,Phylogenetic Structure,Phylogenetic Tree,Phylogenetic Trees,Phylogenomics,Analyse, Phylogenetic,Analysis, Phylogenetic,Analysis, Phylogenetic Comparative,Clustering, Phylogenetic,Community Phylogenetic,Comparative Analysis, Phylogenetic,Comparative Method, Phylogenetic,Distance, Phylogenetic,Group, Phylogenetic,Incongruence, Phylogenetic,Inference, Phylogenetic,Method, Phylogenetic Comparative,Molecular Phylogenetic,Network, Phylogenetic,Phylogenetic Analyse,Phylogenetic Clusterings,Phylogenetic Comparative Analyses,Phylogenetic Comparative Method,Phylogenetic Distances,Phylogenetic Group,Phylogenetic Incongruences,Phylogenetic Inferences,Phylogenetic Network,Phylogenetic Reconstructions,Phylogenetic Relatednesses,Phylogenetic Relationship,Phylogenetic Signals,Phylogenetic Structures,Phylogenetic, Community,Phylogenetic, Molecular,Phylogenies,Phylogenomic,Reconstruction, Phylogenetic,Relatedness, Phylogenetic,Relationship, Phylogenetic,Signal, Phylogenetic,Structure, Phylogenetic,Tree, Phylogenetic
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000956 Antigens, Viral Substances elaborated by viruses that have antigenic activity. Viral Antigen,Viral Antigens,Antigen, Viral
D001499 Bayes Theorem A theorem in probability theory named for Thomas Bayes (1702-1761). In epidemiology, it is used to obtain the probability of disease in a group of people with some characteristic on the basis of the overall rate of that disease and of the likelihood of that characteristic in healthy and diseased individuals. The most familiar application is in clinical decision analysis where it is used for estimating the probability of a particular diagnosis given the appearance of some symptoms or test result. Bayesian Analysis,Bayesian Estimation,Bayesian Forecast,Bayesian Method,Bayesian Prediction,Analysis, Bayesian,Bayesian Approach,Approach, Bayesian,Approachs, Bayesian,Bayesian Approachs,Estimation, Bayesian,Forecast, Bayesian,Method, Bayesian,Prediction, Bayesian,Theorem, Bayes
D013269 Stochastic Processes Processes that incorporate some element of randomness, used particularly to refer to a time series of random variables. Process, Stochastic,Stochastic Process,Processes, Stochastic
D016000 Cluster Analysis A set of statistical methods used to group variables or observations into strongly inter-related subgroups. In epidemiology, it may be used to analyze a closely grouped series of events or cases of disease or other health-related phenomenon with well-defined distribution patterns in relation to time or place or both. Clustering,Analyses, Cluster,Analysis, Cluster,Cluster Analyses,Clusterings
D016013 Likelihood Functions Functions constructed from a statistical model and a set of observed data which give the probability of that data for various values of the unknown model parameters. Those parameter values that maximize the probability are the maximum likelihood estimates of the parameters. Likelihood Ratio Test,Maximum Likelihood Estimates,Estimate, Maximum Likelihood,Estimates, Maximum Likelihood,Function, Likelihood,Functions, Likelihood,Likelihood Function,Maximum Likelihood Estimate,Test, Likelihood Ratio
D017720 Molecular Epidemiology The application of molecular biology to the answering of epidemiological questions. The examination of patterns of changes in DNA to implicate particular carcinogens and the use of molecular markers to predict which individuals are at highest risk for a disease are common examples. Epidemiology, Molecular,Genetic Epidemiology,Epidemiologies, Genetic,Epidemiologies, Molecular,Epidemiology, Genetic,Genetic Epidemiologies,Molecular Epidemiologies

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