Show simple item record

Genomic and pedigree-based prediction for leaf, stem, and stripe rust resistance in wheat

Author: Juliana, P.
Author: Singh, R. P.
Author: Singh, P.K.
Author: Crossa, J.
Author: Huerta-Espino, J.
Author: Caixia Lan
Author: Bhavani, S.
Author: Rutkoski, J.
Author: Poland, J.
Author: Bergstrom, G.C.
Author: Sorrells, M.E.
Year: 2017
Abstract: The unceasing plant-pathogen arms race and ephemeral nature of some rust resistance genes have been challenging for wheat (Triticum aestivum L.) breeding programs and farmers. Hence, it is important to devise strategies for effective evaluation and exploitation of quantitative rust resistance. One promising approach that could accelerate gain from selection for rust resistance is ‘genomic selection’ which utilizes dense genome-wide markers to estimate the breeding values (BVs) for quantitative traits. Our objective was to compare three genomic prediction models including genomic best linear unbiased prediction (GBLUP), GBLUP A that was GBLUP with selected loci as fixed effects and reproducing kernel Hilbert spaces-markers (RKHS-M) with least-squares (LS) approach, RKHS-pedigree (RKHS-P), and RKHS markers and pedigree (RKHS-MP) to determine the BVs for seedling and/or adult plant resistance (APR) to leaf rust (LR), stem rust (SR), and stripe rust (YR). The 333 lines in the 45th IBWSN and the 313 lines in the 46th IBWSN were genotyped using genotyping-by-sequencing and phenotyped in replicated trials. The mean prediction accuracies ranged from 0.31–0.74 for LR seedling, 0.12–0.56 for LR APR, 0.31–0.65 for SR APR, 0.70–0.78 for YR seedling, and 0.34–0.71 for YR APR. For most datasets, the RKHS-MP model gave the highest accuracies, while LS gave the lowest. GBLUP, GBLUP A, RKHS-M, and RKHS-P models gave similar accuracies. Using genome-wide marker-based models resulted in an average of 42% increase in accuracy over LS. We conclude that GS is a promising approach for improvement of quantitative rust resistance and can be implemented in the breeding pipeline.
Format: PDF
Language: English
Publisher: Springer
Copyright: CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose.
Type: Article
Place of Publication: Berlin, Germany
Pages: 1415–1430
Issue: 7
Volume: 130
DOI: 10.1007/s00122-017-2897-1
Agrovoc: WHEAT
Related Datasets:
Journal: Theoretical and Applied Genetics

Files in this item


This item appears in the following Collection(s)

  • Wheat
    Wheat - breeding, phytopathology, physiology, quality, biotech

Show simple item record