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Genomic prediction of rust resistance in tetraploid wheat under field and controlled environment conditions

Creator: Azizinia, S.
Creator: Bariana, H.S.
Creator: Kolmer, J.A.
Creator: Pasam, R.K.
Creator: Bhavani, S.
Creator: Chhetri, M.
Creator: Toor, A.
Creator: Miah, H.
Creator: Hayden, M.
Creator: Pino del Carpio, D.
Creator: Bansal, U.
Creator: Daetwyler, H.D.
Year: 2020
URI: https://hdl.handle.net/10883/21179
Language: English
Publisher: MDPI
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 CIMMYT-Knowledge-Center@cgiar.org 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: Basel (Switzerland)
Issue: 11
Volume: 10
DOI: 10.3390/agronomy10111843
Keywords: Genomic Selection
Keywords: Adult Plant Resistance
Keywords: All Stage Resistance
Description: Genomic selection can increase the rate of genetic gain in crops through accumulation of positive alleles and reduce phenotyping costs by shortening the breeding cycle time. We performed genomic prediction for resistance to wheat rusts in tetraploid wheat accessions using three cross-validation with the objective of predicting: (1) rust resistance when individuals are not tested in all environments/locations, (2) the performance of lines across years, and (3) adult plant resistance (APR) of lines with bivariate models. The rationale for the latter is that seedling assays are faster and could increase prediction accuracy for APR. Predictions were derived from adult plant and seedling responses for leaf rust (Lr), stem rust (Sr) and stripe rust (Yr) in a panel of 391 accessions grown across multiple years and locations and genotyped using 16,483 single nucleotide polymorphisms. Different Bayesian models and genomic best linear unbiased prediction yielded similar accuracies for all traits. Site and year prediction accuracies for Lr and Yr ranged between 0.56–0.71 for Lr and 0.51–0.56 for Yr. While prediction accuracy for Sr was variable across different sites, accuracies for Yr were similar across different years and sites. The changes in accuracies can reflect higher genotype × environment (G × E) interactions due to climate or pathogenic variation. The use of seedling assays in genomic prediction was underscored by significant positive genetic correlations between all stage resistance (ASR) and APR (Lr: 0.45, Sr: 0.65, Yr: 0.50). Incorporating seedling phenotypes in the bivariate genomic approach increased prediction accuracy for all three rust diseases. Our work suggests that the underlying plant-host response to pathogens in the field and greenhouse screens is genetically correlated, but likely highly polygenic and therefore difficult to detect at the individual gene level. Overall, genomic prediction accuracies were in the range suitable for selection in early generations of the breeding cycle.
Agrovoc: TETRAPLOIDY
Agrovoc: WHEAT
Agrovoc: MARKER-ASSISTED SELECTION
Agrovoc: MULTIVARIATE ANALYSIS
Agrovoc: RESISTANCE TO INJURIOUS FACTORS
ISSN: 2073-4395
Journal: Agronomy
Article number: 1843


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

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