Unprecedented global surveillance of viruses will result in massive sequence data sets that require new statistical methods. These data sets press the limits of Bayesian phylogenetics as the high-dimensional parameters that comprise a phylogenetic tree increase the already sizable computational burden of these techniques. This burden often results in partitioning the data set, for example, by gene, and inferring the evolutionary dynamics of each partition independently, a compromise that results in stratified analyses that depend only on data within a given partition. However, parameter estimates inferred from these stratified models are likely strongly correlated, considering they rely on data from a single data set. To overcome this shortfall, we exploit the existing Monte Carlo realizations from stratified Bayesian analyses to efficiently estimate a nonparametric hierarchical wavelet-based model and learn about the time-varying parameters of effective population size that reflect levels of genetic diversity across all partitions simultaneously. Our methods are applied to complete genome influenza A sequences that span 13 years. We find that broad peaks and trends, as opposed to seasonal spikes, in the effective population size history distinguish individual segments from the complete genome. We also address hypotheses regarding intersegment dynamics within a formal statistical framework that accounts for correlation between segment-specific parameters.

Assunto(s): Evolução Molecular; Vírus da Influenza A/genética; Vírus da Influenza A/patogenicidade; Influenza Humana/virologia; Teorema de Bayes; Biologia Computacional; Genoma Viral; Glicoproteínas de Hemaglutininação de Vírus da Influenza/genética; Humanos; Influenza Humana/epidemiologia; Modelos Biológicos; Método de Monte Carlo; Neuraminidase/genética; Pandemias; Periodicidade; Filogenia; Proteínas Virais/genética; Análise de Ondaletas