Development of statistical genetic models and hierarchical Bayes procedures to predict emergence and dynamics of resistant alleles

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Setting a virus as a main target of the research, we developed new statistical models and procedures to estimate its genetic diversity and to predict the emergence of resistant alleles. Our hierarchical Bayes model detects inconsistency of phylogenetic relation between the segments of the genomes and estimates the recombination history. By our knowledge-based measure of the binding ability of a protein complex, it became possible to predict the change in the values of the coefficients that specify a population dynamic model. As a result, it became possible to predict the types and timing of new resistant alleles that are likely to be fixed among host population.