Genomic prediction of hybrid wheat performance

Zhao, Yusheng, Zeng, Jian, Fernando, Rohan and Reif, Jochen C. (2013) Genomic prediction of hybrid wheat performance. Crop Science, 53 3: 802-810. doi:10.2135/cropsci2012.08.0463

Author Zhao, Yusheng
Zeng, Jian
Fernando, Rohan
Reif, Jochen C.
Title Genomic prediction of hybrid wheat performance
Journal name Crop Science   Check publisher's open access policy
ISSN 0011-183X
Publication date 2013-03-01
Year available 2013
Sub-type Article (original research)
DOI 10.2135/cropsci2012.08.0463
Open Access Status Not yet assessed
Volume 53
Issue 3
Start page 802
End page 810
Total pages 9
Place of publication Heidelberg, Germany
Publisher Springer
Language eng
Formatted abstract
Accurate prediction of single cross performance is of fundamental importance to increase the selection gain in wheat (Triticum aestivum L.) hybrid breeding programs. We used experimental data from a commercial wheat breeding program as well as simulated data sets and evaluated the prospects of predicting grain yield performance of untested hybrids applying different cross-validation scenarios. We used ridge regression best linear unbiased prediction (RR -BLUP), BayesA, BayesB, BayesC, and BayesCπ facilitating genomic selection for additive and dominance effects. In total 90 hybrids were evaluated for grain yield in unreplicated trials at four locations. The parental lines were fingerprinted with a 9000 (9k) single nucleotide polymorphism array. We observed in the crossvalidation study high prediction accuracies for all five genomic models with a slight superiority of RR -BLUP and BayesB. Interestingly, ignoring dominance effects resulted in equal or even higher prediction accuracies. This lack of improvement points toward the need for further refine the genomic selection models to more precisely estimate dominance effects.
Keyword Agronomy
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID FKZ0315945D
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Institute for Molecular Bioscience - Publications
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Citation counts: TR Web of Science Citation Count  Cited 36 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 37 times in Scopus Article | Citations
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