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Evaluations of genomic prediction and identification of new loci for resistance to stripe rust disease in wheat (Triticum aestivum L.)

Creator: Tomar, V.
Creator: Dhillon, G.S.
Creator: Singh, D.
Creator: Singh, R.P.
Creator: Poland, J.A.
Creator: Chaudhary, A.A.
Creator: Bhati, P.K.
Creator: Joshi, A.K.
Creator: Kumar, U.
Year: 2021
URI: https://hdl.handle.net/10883/21707
Language: English
Publisher: Frontiers
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: Switzerland
Volume: 12
DOI: 10.3389/fgene.2021.710485
Keywords: Genome-Wide Association Study
Keywords: Genomic Selection
Keywords: Best Linear Unbiased Estimator
Keywords: Stripe Rust
Keywords: Genotyping by Sequencing
Description: Stripe rust is one of the most destructive diseases of wheat (Triticum aestivum L.), caused by Puccinia striiformis f. sp. tritici (Pst), and responsible for significant yield losses worldwide. Single-nucleotide polymorphism (SNP) diagnostic markers were used to identify new sources of resistance at adult plant stage to wheat stripe rust (YR) in 141 CIMMYT advanced bread wheat lines over 3 years in replicated trials at Borlaug Institute for South Asia (BISA), Ludhiana. We performed a genome-wide association study and genomic prediction to aid the genetic gain by accumulating disease resistance alleles. The responses to YR in 141 advanced wheat breeding lines at adult plant stage were used to generate G × E (genotype × environment)-dependent rust scores for prediction and genome-wide association study (GWAS), eliminating variation due to climate and disease pressure changes. The lowest mean prediction accuracies were 0.59 for genomic best linear unbiased prediction (GBLUP) and ridge-regression BLUP (RRBLUP), while the highest mean was 0.63 for extended GBLUP (EGBLUP) and random forest (RF), using 14,563 SNPs and the G × E rust score results. RF and EGBLUP predicted higher accuracies (∼3%) than did GBLUP and RRBLUP. Promising genomic prediction demonstrates the viability and efficacy of improving quantitative rust tolerance. The resistance to YR in these lines was attributed to eight quantitative trait loci (QTLs) using the FarmCPU algorithm. Four (Q.Yr.bisa-2A.1, Q.Yr.bisa-2D, Q.Yr.bisa-5B.2, and Q.Yr.bisa-7A) of eight QTLs linked to the diagnostic markers were mapped at unique loci (previously unidentified for Pst resistance) and possibly new loci. The statistical evidence of effectiveness and distribution of the new diagnostic markers for the resistance loci would help to develop new stripe rust resistance sources. These diagnostic markers along with previously established markers would be used to create novel DNA biosensor-based microarrays for rapid detection of the resistance loci on large panels upon functional validation of the candidate genes identified in the present study to aid in rapid genetic gain in the future breeding programs.
Agrovoc: MARKER-ASSISTED SELECTION
Agrovoc: QUANTITATIVE TRAIT LOCI
Agrovoc: RUSTS
Agrovoc: GENOTYPING
Agrovoc: TRITICUM AESTIVUM
Related Datasets: https://figshare.com/collections/Evaluations_of_Genomic_Prediction_and_Identification_of_New_Loci_for_Resistance_to_Stripe_Rust_Disease_in_Wheat_Triticum_aestivum_L_/5636869
ISSN: 1664-8021
Journal: Frontiers in Genetics
Article number: 710485


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

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