Friday, October 6, 2017
11 AM - 12 PM
It is a common sense that sampled individuals in a population are descendants of some common ancestors if tracing backward in time long enough. Indeed, these sampled individuals share a genealogical history that specifies the ancestry of these individuals. This genealogy is very informative. For example, the genealogy is useful to explain why some individuals are more susceptible to some phenotypic traits than others. It might be the case that individuals sharing a trait are more closely related to each other on the genealogy than the rest of the population. In practice, however, using gene genealogy is not easy. First, genealogy is not directly observable and must be inferred from genetic data. Second, genealogy is greatly complicated by the meiotic recombination. With recombination, genealogy may be different at different positions in the genome. Recently, with the rapid development of DNA sequencing technologies, population-scale genetic data is now available. This provides data for inferring gene genealogy. There is a fundamental population genetics theory called coalescent theory, which provides the underlying model for genealogy. However, coalescent theory is known to be challenging computationally. Recombination adds more complexity for genealogy inference.
In this talk, I will present some of my recent work on developing algorithms for several genetics problems, including efficient computation of coalescent likelihood under the so-called multispecies coalescent model, and the inference of gene genealogy with recombination. The first part of talk is about multispecies coalescent, which concerns gene lineages that originate from multiple related populations (species). Given a gene genealogy and a species tree (the evolutionary history of species), the likelihood of the gene tree is the probability of observing the gene tree topology on the species tree under coalescent theory. I will describe an algorithm (published in a paper in Evolution, 2012) for computing this coalescent probability, which is much faster than a previous algorithm. During the second part of the talk, I will present a recent approach on inferring gene genealogy from haplotypes when there is recombination.
Research partly supported by NSF grants IIS-1526415 and CCF-1718093.
Yufeng Wu is an associate professor in Department of Computer Science and Engineering at University of Connecticut. His main research interests are in computational biology and bioinformatics. He did his undergraduate study at Tsinghua University, Beijing, China, where he received a bachelor's degree in Mechanical Engineering and a second bachelor's degree in Computer Science and Technology in 1994. After receiving a Master's degree from University of Illinois at Urbana-Champaign, he worked in the IT industry for five years. Then he returned to school and received a PhD in Computer Science at University of California, Davis in 2007. From 2007 to 2013, he was an assistant professor at Computer Science and Engineering Department, University of Connecticut. He received NSF CAREER award in 2010.
Dr. Yanni Sun