February 1 2018

11:00 158 HH

Joshua Schraiber
Institute for Genomics and Evolutionary Medicine
Department of Biology

Through a sequencer, darkly: statistical inference from ancient DNA


The advent of ancient DNA sequencing opened an unprecedented window into the recent evolutionary past. Hypotheses about population continuity and natural selections, once only accessible via circumstantial evidence, can now be tested directly. However, ancient DNA is often degraded and damaged, resulting in a blurry view. To get the most of out of this exciting data, we need careful statistical modeling. In this talk, I will present work on statistical methods for learning about demographic and selective history from ancient DNA. First, I will describe about a Bayesian approach for inference of natural selection from allele frequency time series. I show that accounting for demographic history has tremendous impacts on allele age estimates, and demonstrate on empirical data that modeling demography qualitatively impacts inferences about the mode of natural selection. However, assessing the strength of selection implicitly assumes that all samples are members of the same population through time. Moreover, many questions in evolutionary biology revolve around population continuity: are the individuals that live in a location now related to those that used to inhabit that area\? To assess population continuity, I developed a framework to leverage genome-wide data from low coverage ancient samples. When applying this approach to ancient Europeans, I see that the history of stone age Europe is characterized by small populations and local extinction. Finally, I will discuss ongoing work using deep learning to determine subtle details of the process of human-Neandertal admixture
































































































































































































































































































































































































































































































































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