High throughput DNA sequencing has transformed the landscape of genomic data and is expected to revolutionize our knowledge of evolution and genomic function. These data are expected to be of particular benefit to the study of recent
evolutionary processes, due to our ability to sequence multiple individuals from closely related species. While much excitement revolves around these emerging data sets, realizing this potential requires developing powerful and efficient inference methods that are capable of extracting insights on recent evolution from genome-wide sequence data. In this talk, I will be presenting some of my work in this area,
which examines what we can learn from complete individual genome sequences on population history and recent natural selection. I will start by describing a study on ancient human population demography in Africa, focusing on one of the deepest population divergence events in human history, dating roughly 130 thousand years ago.
I will then present work I did as part of a large-scale collaborative effort to study the early evolution of dogs using the complete genome sequences of two dogs and three gray wolves. I will show how we were able to settle several longstanding debates revolving around the origins of dogs using these genomes and an innovative computational approach. Lastly, I will introduce a line of research I recently
initiated, focused on studying the evolutionary roles of non coding regulatory elements in the human genome. I will present recently published work on natural selection on human transcription factor binding sites, and ongoing efforts to extend that approach to all functional non coding elements in the genome. The talk will focus on the main findings in these three studies and how they contribute to our understanding of recent evolution. I will highlight the computational challenges involved, and will conclude with a map of the opportunities and challenges we face in the study of evolution in a world of rapidly evolving genomic data sets.
