Novel Coalescent Approaches for Studying Evolutionary Processes

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Project Summary/Abstract: My laboratory research program in stochastic modeling and inference of evolutionary processes focuses on developing efficient methods for inference of evolutionary parameters from molecular data, and statistical tests for assessing evolutionary hypotheses. This proposal will focus on answering three fundamental questions in the study of evolutionary processes: Are the observed patters of genetic diversity the result of adaptive or non-adaptive evolution? What is the mode and strength of selection? How can we identify genomic regions undergoing selection? Whether adaptation, demography or local patterns of mutations are the sources of variation across populations, these forces influence the shape of the underlying genealogies and phylogenetic networks. Hence, assessing differences among genealogies provide information about differences in these forces, particularly among genealogies of different individuals, possibly living in different environments and times. We propose to approach these questions by defining new coalescent models of selection and exploiting a metric on the space of genealogies to define statistical tests. The computational advantage and the ease of biological interpretation, together with the mathematical properties of the proposed models and metric spaces, open the door to novel approaches for studying adaptation. Over the next five years, the Palacios laboratory will combine tools from combinatorial optimization, Bayesian inference, and coalescent theory to develop new coalescent models and tests applicable to studying the evolution of pathogens and other organisms.