Phenotypic Bayesian phylodynamics: hierarchical graph models, antigenic clustering and latent liabilities
|Date ||Wednesday February 05, 2014 at 9:30 AM |
|Location ||5229 Life Sciences Building |
|Speaker ||Gabriela Cybis, Doctoral Graduate Student, Department of Biomathematics, UCLA |
|Sponsoring Dept ||UCLA Biomathematics |
|Abstract ||Combining models for phenotypic and molecular evolution can lead to powerful
inference tools. Under the fl exible framework of Bayesian phylogenetics, I develop
statistical methods to address phylodynamic problems in this intersection.
First, I present a hierarchical phylogeographic method that combines information
across multiple datasets to draw inference on a common geographical spread
process. Each dataset represents a parallel realization of this geographic process
on a different group of taxa, and the method shares information between these
realizations through a hierarchical graph structure.
Additionally, I develop a multivariate latent liability model for assessing phenotypic
correlation among sets of traits, while controlling for shared evolutionary history.
This method can effi ciently estimate correlations between multiple continuous
traits, binary traits and discrete traits with many ordered or unordered outcomes.
Finally, I present a method that uses phylogenetic information to study the
evolution of antigenic clusters in infl uenza. The method builds an antigenic
cartography map informed by the assignment of each infl uenza strain to one of
the antigenic clusters. |
|Flyer ||Cybis_defense_seminar.pdf |