Abstract: The coalescent is a powerful statistical framework that allows us to infer past population dynamics leveraging the ancestral relationships reconstructed from sampled molecular sequence data. In many biomedical applications, such as in the study of infectious diseases, several distinct populations share evolutionary history and therefore become dependent. The inference of such dependence is a highly important, yet a challenging problem. With advances in sequencing technologies, we are well positioned to exploit the wealth of high resolution biological data for tackling this problem. In this talk, I will present a coalescent-based probabilistic model for estimating past population dynamics of dependent populations and quantifying their degree of dependence from genetic data. An essential feature of the approach is the ability to track the time-varying association between the populations while making minimal assumptions on their functional shapes. I will discuss nonparametric estimators and extensions of the base model that integrate multiple data sources and demonstrate the method using simulated data under various dependent population histories and real data of SARS-CoV-2.
Bio: Jaehee is an Assistant Professor in the Department of Computational Biology at Cornell University. She is interested in developing mathematical models and statistical inference methods for studying evolutionary processes, and application of evolutionary principles to problems with biomedical, legal, and social implications. Prior to joining Cornell, Jaehee did her postdoctoral work with Prof. Noah Rosenberg in the Department of Biology at Stanford University. She received her PhD in Physics at Stanford University - SLAC National Accelerator Laboratory, and BA in Mathematics and Physics at Columbia University.
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