|Xu, Fang -|
|Lyu, Yafei -|
|Tong, Chunfa -|
|Wu, Weimiao -|
|Zhu, Xuli -|
|Yin, Danni -|
|Yan, Qin -|
|Zhang, Jian -|
|Pang, Xiaoming -|
|Wu, Rongling -|
Submitted to: Briefings in Bioinformatics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: September 23, 2013
Publication Date: October 30, 2013
Citation: Xu, F., Lyu, Y., Tong, C., Wu, W., Zhu, X., Yin, D., Yan, Q., Zhang, J., Pang, X., Tobias, C.M., Wu, R. 2013. A statistical model for QTL mapping in polysomic autotetraploids underlying double reduction. Briefings in Bioinformatics. DOI: 10.1093/bib/bbt073. Interpretive Summary: Interpretive Summary: Because of their biological and economic importance, genetic analysis in polyploids has long intrigued geneticists and evolutionary biologists. Linkage analysis with molecular markers has particular power to study the structure, organization and function of polyploid genomes and is the basis for mapping quantitative trait loci that affect complex traits. Statistical models for linkage mapping in polyploids are qualitatively different from those in diploids because of unique properties of the former. Several models that take into account these properties have been developed and play an increasingly important part in polyploid mapping. In this work a computer simulation was used to demonstrate statistical properties of a new mapping procedure and its analytical merits. A working example of the approach was demonstrated by an analysis of real data in tetraploid switchgrass.
Technical Abstract: Technical Abstract: As a group of economically important species, linkage mapping of polysomic autotetraploids, including potato, sugarcane and rose, is difficult to conduct due to their unique meiotic property of double reduction that allows sister chromatids to enter into the same gamete. We describe and assess a statistical model for mapping quantitative trait loci (QTLs) in polysomic autotetraploids. The model incorporates double reduction, built in the mixture model-based framework and implemented with the expectation–maximization algorithm. It allows the simultaneous estimation of QTL positions, QTL effects and the degree of double reduction as well as the assessment of the estimation precision of these parameters. We performed computer simulation to examine the statistical properties of the method and validate its use through analyzing real data in tetraploid switchgrass.