ABI Innovation: Coalescence-based Inference of Adaptation

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This project will develop a mathematical framework for investigating genomic sequences that respond differently to local environmental stresses. This framework will compare mutation patterns from samples taken at different locations to estimate possible genealogies of these samples; these genealogies will be used to estimate the parameters of a number of population genetic models. These parameters will be able to tell us about potential selection differences among geographical locations. This new mathematically rigorous framework has the promise to supersede current ad hoc and inadequate summary statistics. Potential applications of this framework include improving interventions for diseases (e.g. individualized responses for HIV patients), and improving our understanding of which gene regions are responsible for long-term survival in harsh environments. The framework will be publicly available in standalone computer software that can be run on small computers or large computing clusters. This research draws from multiple science and technology disciplines (biology, computational science, and statistics) and thus will provide a great basis on which to mentor undergraduate, graduate, and postdoctoral students and foster their interest towards a field that desperately needs more training opportunities. The building blocks for this framework are rooted in coalescence theory, a branch of theoretical population genetics discussing the shapes of genealogies of individuals, and Bayesian statistics evaluating different scenarios and integrating over possible solutions using Markov chain Monte Carlo technology. The data will be genomic sequences which are known to contain technical errors; to successfully differentiate among gene regions that are under selection for particular environments, these errors must be taken into account, but currently are not. Additionally, samples from different geographical locations (for example different patients, different islands, or different habitats) can be grouped in different ways, which requires that the framework be capable of delivering optimality criteria that help to order different scenarios. Progress and the final work will be documented on the websites http://popgen.sc.fsu.edu and http://peterbeerli.com.