dc.creatorTehseen, M.M.
dc.creatorKehel, Z.
dc.creatorSansaloni, C.P.
dc.creatorLopes, M.S.
dc.creatorAmri, A.
dc.creatorKurtulus, E.
dc.creatorNazari, K.
dc.date2021-03-30T00:10:15Z
dc.date2021-03-30T00:10:15Z
dc.date2021
dc.date.accessioned2023-07-17T20:07:13Z
dc.date.available2023-07-17T20:07:13Z
dc.identifierhttps://hdl.handle.net/10883/21330
dc.identifier10.3390/plants10030558
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7513111
dc.descriptionWheat rust diseases, including yellow rust (Yr; also known as stripe rust) caused by Puccinia striiformis Westend. f. sp. tritici, leaf rust (Lr) caused by Puccinia triticina Eriks. and stem rust (Sr) caused by Puccinia graminis Pres f. sp. tritici are major threats to wheat production all around the globe. Durable resistance to wheat rust diseases can be achieved through genomic-assisted prediction of resistant accessions to increase genetic gain per unit time. Genomic prediction (GP) is a promising technology that uses genomic markers to estimate genomic-assisted breeding values (GBEVs) for selecting resistant plant genotypes and accumulating favorable alleles for adult plant resistance (APR) to wheat rust diseases. To evaluate GP we compared the predictive ability of nine different parametric, semi-parametric and Bayesian models including Genomic Unbiased Linear Prediction (GBLUP), Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net (EN), Bayesian Ridge Regression (BRR), Bayesian A (BA), Bayesian B (BB), Bayesian C (BC) and Reproducing Kernel Hilbert Spacing model (RKHS) to estimate GEBV’s for APR to yellow, leaf and stem rust of wheat in a panel of 363 bread wheat landraces of Afghanistan origin. Based on five-fold cross validation the mean predictive abilities were 0.33, 0.30, 0.38, and 0.33 for Yr (2016), Yr (2017), Lr, and Sr, respectively. No single model outperformed the rest of the models for all traits. LASSO and EN showed the lowest predictive ability in four of the five traits. GBLUP and RR gave similar predictive abilities, whereas Bayesian models were not significantly different from each other as well. We also investigated the effect of the number of genotypes and the markers used in the analysis on the predictive ability of the GP model. The predictive ability was highest with 1000 markers and there was a linear trend in the predictive ability and the size of the training population. The results of the study are encouraging, confirming the feasibility of GP to be effectively applied in breeding programs for resistance to all three wheat rust diseases.
dc.languageEnglish
dc.publisherMDPI
dc.relationhttps://www.mdpi.com/2223-7747/10/3/558#supplementary
dc.rightsCIMMYT 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
dc.rightsOpen Access
dc.source3
dc.source10
dc.source2223-7747
dc.sourcePlants
dc.source558
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectGenomic Prediction
dc.subjectWheat Landraces
dc.subjectYellow Rust
dc.subjectLeaf Rust
dc.subjectStem Rust
dc.subjectGENOMICS
dc.subjectWHEAT
dc.subjectLAND RACES
dc.subjectRUSTS
dc.titleComparison of genomic prediction methods for yellow, stem, and leaf rust resistance in wheat landraces from Afghanistan
dc.typeArticle
dc.typePublished Version
dc.coverageBasel (Switzerland)


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