Genomic prediction for rust resistance in diverse wheat landraces

Daetwyler, Hans D., Bansal, Urmil K., Bariana, Harbans S., Hayden, Matthew J. and Hayes, Ben J. (2014) Genomic prediction for rust resistance in diverse wheat landraces. Theoretical and Applied Genetics, 127 8: 1795-1803. doi:10.1007/s00122-014-2341-8


Author Daetwyler, Hans D.
Bansal, Urmil K.
Bariana, Harbans S.
Hayden, Matthew J.
Hayes, Ben J.
Title Genomic prediction for rust resistance in diverse wheat landraces
Journal name Theoretical and Applied Genetics   Check publisher's open access policy
ISSN 0040-5752
1432-2242
Publication date 2014-08
Sub-type Article (original research)
DOI 10.1007/s00122-014-2341-8
Open Access Status Not yet assessed
Volume 127
Issue 8
Start page 1795
End page 1803
Total pages 9
Place of publication Heidelberg, Germany
Publisher Springer
Language eng
Formatted abstract
Key message We have demonstrated that genomic selection in diverse wheat landraces for resistance to leaf, stem and strip rust is possible, as genomic breeding values were moderately accurate. Markers with large effects in the Bayesian analysis confirmed many known genes, while also discovering many previously uncharacterised genome regions associated with rust scores. Genomic selection, where selection decisions are based on genomic estimated breeding values (GEBVs) derived from genome-wide DNA markers, could accelerate genetic progress in plant breeding. In this study, we assessed the accuracy of GEBVs for rust resistance in 206 hexaploid wheat (Triticum aestivum) landraces from the Watkins collection of phenotypically diverse wheat genotypes from 32 countries. The landraces were genotyped for 5,568 SNPs using an Illumina iSelect 9 K bead chip assay and phenotyped for field-based leaf rust (Lr), stem rust (Sr) and stripe rust (Yr) responses across multiple years. Genomic Best Linear Unbiased Prediction (GBLUP) and a Bayesian Regression method (BayesR) were used to predict GEBVs. Based on fivefold cross-validation, the accuracy of genomic prediction averaged across years was 0.35, 0.27 and 0.44 for Lr, Sr and Yr using GBLUP and 0.33, 0.38 and 0.30 for Lr, Sr and Yr using BayesR, respectively. Inclusion of PCR-predicted genotypes for known rust resistance genes increased accuracy more substantially when the marker was diagnostic (Lr34/Sr57/Yr18) for the presence-absence of the gene rather than just linked (Sr2). Investigation of the impact of genetic relatedness between validation and reference lines on accuracy of genomic prediction showed that accuracy will be higher when each validation line had at least one close relationship to the reference lines. Overall, the prediction accuracies achieved in this study are encouraging, and confirm the feasibility of genomic selection in wheat. In several instances, estimated marker effects were confirmed by published literature and results of mapping experiments using Watkins accessions.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Queensland Alliance for Agriculture and Food Innovation
 
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