Comparison of Genomic Prediction Methods for Yellow, Stem, and Leaf Rust Resistance in Wheat Landraces from Afghanistan

Basit Öğe Kaydı
dc.contributor.authorTehseen, Muhammad Massub
dc.contributor.authorKehel, Zakaria
dc.contributor.authorSansaloni, Carolina P.
dc.contributor.authorLopes, Marta da Silva
dc.contributor.authorAmri, Ahmed
dc.contributor.authorKurtulus, Ezgi
dc.contributor.authorNazari, Kumarse
dc.date.accessioned2021-05-03T20:27:37Z
dc.date.available2021-05-03T20:27:37Z
dc.date.issued2021
dc.departmentEge Üniversitesien_US
dc.description.abstractWheat 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.en_US
dc.description.sponsorshipDelivering Genetic Gain in Wheat project - Bill and Melinda Gates Foundation (BMGF); UK Department for International Development (DFID) [OPP1133199]en_US
dc.description.sponsorshipThis research was funded by Delivering Genetic Gain in Wheat project supported by Bill and Melinda Gates Foundation (BMGF) and the UK Department for International Development (DFID), grant number OPP1133199.en_US
dc.identifier.doi10.3390/plants10030558en_US
dc.identifier.issn2223-7747
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85102551740en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.3390/plants10030558
dc.identifier.urihttps://hdl.handle.net/11454/69592
dc.identifier.volume10en_US
dc.identifier.wosWOS:000634083700001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofPlants-Baselen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectgenomic predictionen_US
dc.subjectwheat landracesen_US
dc.subjectyellow rusten_US
dc.subjectleaf rusten_US
dc.subjectstem rusten_US
dc.titleComparison of Genomic Prediction Methods for Yellow, Stem, and Leaf Rust Resistance in Wheat Landraces from Afghanistanen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayın Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu