title: Identification of regions of positive selection using Shared Genomic Segment analysis.
abstract: We applied a shared genomic segment (SGS) analysis, incorporating an error model, to identify complete, or near complete, selective sweeps in the HapMap phase II data sets. This method is based on detecting heterozygous sharing across all individuals within a population, to identify regions of sharing with at least one allele in common. We identified multiple interesting regions, many of which are concordant with positive selection regions detected by previous population genetic tests. Others are suggested to be novel regions. Our finding illustrates the utility of SGS as a method for identifying regions of selection, and some of these regions have been proposed to be candidate regions for harboring disease genes.
authors.affiliation: Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT 84108, USA. z.cai@utah.edu
authors.forename: Zheng
authors.initials: Z
authors.lastname: Cai
authors.forename: Nicola J
authors.initials: NJ
authors.lastname: Camp
authors.forename: Lisa
authors.initials: L
authors.lastname: Cannon-Albright
authors.forename: Alun
authors.initials: A
authors.lastname: Thomas
mesh_terms: D000465:Algorithms; D030541:Databases, Genetic; D005787:Gene Frequency; D014644:Genetic Variation; D005828:Genetics, Population / Q000379:methods* / Q000706:statistics & numerical data; D015894:Genome, Human; D023281:Genomics / Q000379:methods*; D006239:Haplotypes; D006579:Heterozygote; D006801:Humans; D008957:Models, Genetic; D044382:Population Groups / Q000235:genetics*; D018401:Sample Size; D012641:Selection, Genetic; D012984:Software