With increasingly available population-scale genetic variation data, a current high priority research goal is to understand how genetic variations influence complex diseases (or more generally genetic traits). Recombination is an important genetic process that plays a major role in the logic behind association mapping, a currently intensely studied method widely hoped to efficiently find genes (alleles) associated with complex genetic diseases. In this talk, I will present algorithmic and computational results on inferring historical recombination and constructing genealogical networks with recombination and applications to two biologically important problems: association mapping of complex diseases and detecting recombination hotspots. On association mapping, I will present a method that generates the most parsimonious genealogical networks uniformly and show how it can be applied in association mapping. I will introduce results on evaluating how well the inferred genealogy fits the given phenotypes (i.e. cases and controls) and locates genes associated with the disease. Our recent work on detecting recombination hotspots by inferring minimum recombination will also be briefly described. For both biological problems, I will demonstrate the effectiveness of these methods with experimental results on simulated or real data.
Yufeng Wu received his Master degree in Computer Science from University of Illinois at Urbana-Champaign in 1998 and Bachelor degree from Tsinghua University, China in 1994. From 1998 to 2003, he was a software engineer at a startup company in Illinois, USA. He is currently a PhD candidate in the Department of Computer Science, University of California, Davis. At UC Davis, he works with Prof. Dan Gusfield on algorithms in computational biology and bioinformatics. His current research is focused on computational problems arising in population genomics.