A high-dimensional linkage analysis model for characterizing crossover interference

Jing Wang, Lidan Sun, Libo Jiang, Mengmeng Sang, Meixia Ye, Tangran Cheng, Qixiang Zhang, Rongling Wu

Research output: Contribution to journalArticle

2 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)382-393
Number of pages12
JournalBriefings in bioinformatics
Volume18
Issue number3
DOIs
StatePublished - May 1 2017

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Genes
Genome
Statistical Models
Chromosomes

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Molecular Biology

Cite this

Wang, Jing ; Sun, Lidan ; Jiang, Libo ; Sang, Mengmeng ; Ye, Meixia ; Cheng, Tangran ; Zhang, Qixiang ; Wu, Rongling. / A high-dimensional linkage analysis model for characterizing crossover interference. In: Briefings in bioinformatics. 2017 ; Vol. 18, No. 3. pp. 382-393.
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Wang, J, Sun, L, Jiang, L, Sang, M, Ye, M, Cheng, T, Zhang, Q & Wu, R 2017, 'A high-dimensional linkage analysis model for characterizing crossover interference', Briefings in bioinformatics, vol. 18, no. 3, pp. 382-393. https://doi.org/10.1093/bib/bbw033

A high-dimensional linkage analysis model for characterizing crossover interference. / Wang, Jing; Sun, Lidan; Jiang, Libo; Sang, Mengmeng; Ye, Meixia; Cheng, Tangran; Zhang, Qixiang; Wu, Rongling.

In: Briefings in bioinformatics, Vol. 18, No. 3, 01.05.2017, p. 382-393.

Research output: Contribution to journalArticle

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