title: Inference about recombination from haplotype data: lower bounds and recombination hotspots.
abstract: Recombination is an important evolutionary mechanism responsible for creating the patterns of haplotype variation observable in human populations. Recently, there has been extensive research on understanding the fine-scale variation in recombination across the human genome using DNA polymorphism data. Historical recombination events leave signature patterns in haplotype data. A nonparametric approach for estimating the number of historical recombination events is to compute the minimum number of recombination events in the history of a set of haplotypes. In this paper, we provide new and improved methods for computing lower bounds on the minimum number of recombination events. These methods are shown to detect a higher number of recombination events for a haplotype dataset from a region in the lipoprotein lipase gene than previous lower bounds. We apply our methods to two datasets for which recombination hotspots have been experimentally determined and demonstrate a high density of detectable recombination events in the regions annotated as recombination hotspots. The programs implementing the methods in this paper are available at www.cs.ucsd.edu/users/vibansal/RecBounds/.
authors.affiliation: Department of Computer Science and Engineering, University of California at San Diego, La Jolla, 92093, USA.
authors.forename: Vineet
authors.initials: V
authors.lastname: Bafna
authors.forename: Vikas
authors.initials: V
authors.lastname: Bansal
mesh_terms: D002877:Chromosomes, Human; D005819:Genetic Markers; D005828:Genetics, Population; D015894:Genome, Human; D006239:Haplotypes; D006801:Humans; D015810:Linkage Disequilibrium; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D011995:Recombination, Genetic
chemical_list: D005819:Genetic Markers