Algorithms for Inferring Recombination and Association Mapping in Populations

A current high priority research goal is to understand how genetic variations influence complex genetic diseases (or more generally traits). Recombination is an important biological and 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 diseases. Recently, population-scale genetic variation data has become increasingly available. In this dissertation, I will present algorithmic and computational results on inferring historical recombination and constructing genealogical networks with recombination. I will demonstrate applications of these results to two biologically important problems: detecting recombination hotspots and association mapping of complex diseases. A central computational problem in this dissertation is the recombination minimization problem. I will first describe methods that compute lower bounds on the minimum number of recombinations needed to derive a set of SNP sequences and its application in detecting recombination hotspots. I will also present methods for detecting recombination hotspots and association mapping using genealogical networks. For both biological problems, I will demonstrate the effectiveness of these methods with experimental results on simulated or real data. Professor Dan Gusfield Dissertation Committee Chair Algorithms for Inferring Recombination and Association Mapping in Populations