Abstract: In this talk, I will outline some current research challenges in statistical genetics, and describe recent progress my group has made towards solving them. First, I will introduce the sequentially Markov coalescent (SMC), which is an important class of methods for approximating the likelihood of DNA sequence data under realistic models of evolution. Examples of the types of questions we can address using SMC include: When did humans migrate out of Africa? How did polar bears fare during the last global warming event? Why did Neanderthals disappear? We derive new Bayesian and frequentist inference procedures for SMC that are faster and have less bias than existing methods. The key new insight is to establish connections between SMC and certain well-studied models in changepoint detection. In the second portion of the talk, I will discuss a new, model-based procedure we have developed for detecting signatures of natural selection in genetic data. Our estimator is adept at discovering instances of directional and balancing selection in the human genome, and has a concrete interpretation in terms of gene tree imbalance. Finally, time permitting, I will share some early results on using these and other methods to study SARS-CoV-2 biology and the ongoing global pandemic.

This seminar will be livestreamed via Zoom https://umich.zoom.us/j/94350208889 There will be a virtual reception to follow

http://www-personal.umich.edu/~jonth/