Model inference, comparison, and averaging for genetically structured populations

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Researchers collect genetic data to assess variability of animal and plant populations and their ancestral relationships. For such datasets, thousands of different relationships can be specified. For example, are polar bear populations isolated from each other and if not what is the pattern of relationships of polar bears from different populations? This research will develop new methods to calculate the likelihood of different patterns of relationships using Bayesian inference. These methods will be coded into freely available software that will be able to evaluate large data sets. This will achieve accurate ranking of alternative hypotheses using genomic data sets considering complexities such as recombination and data collection artifacts.This study will provide freely available software along with a user manual for the research community. The new methods will facilitate many types of studies, including disease epidemics, and the studies of the effect of changes in climate onto the distribution and maintenance of genetic variability in crop-related and other species. The software will be disseminated through online tutorials and workshops aimed at senior researchers. There will also be two workshops on computer literacy for individuals ranging from high-school science teachers, to undergraduates, and graduate students.