A high-dimensional linkage analysis model for characterizing crossover interference. (25th April 2016)

• Record Type: Journal Article
• Authors: Wang, Jing; Sun, Lidan; Jiang, Libo; Sang, Mengmeng; Ye, Meixia; Cheng, Tangran; Zhang, Qixiang; Wu, Rongling
• Is Part Of: Briefings in bioinformatics. Volume 18:Number 3(2017:May)
• Page Start: 382
• Page End: 393
• Publication Date: 2016-04-25
• Subjects: multi-point linkage analysis -- crossover interference -- EM algorithm -- high-order crossover interference; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbw033
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Linkage analysis has played an important role in understanding genome structure and evolution. However, two-point linkage analysis widely used for genetic map construction can rarely chart a detailed picture of genome organization because it fails to identify the dependence of crossovers distributed along the length of a chromosome, a phenomenon known as crossover interference. Multi-point analysis, proven to be more advantageous in gene ordering and genetic distance estimation for dominant markers than two-point analysis, is equipped with a capacity to discern and quantify crossover interference. Here, we review a statistical model for four-point analysis, which, beyond three-point analysis, can characterize crossover interference that takes place not only between two adjacent chromosomal intervals, but also over multiple successive intervals. This procedure provides an analytical tool to elucidate the detailed landscape of crossover interference over the genome and further infer the evolution of genome structure and organization.