Comparison of Genomic Prediction Methods for Yellow, Stem, and Leaf Rust Resistance in Wheat Landraces from Afghanistan
1
Department of Field Crops, Ege University, P.O. Box 35100 Bornova, Izmir, Turkey
2
International Center for Agricultural Research in the Dry Areas (ICARDA), ICARDA-PreBreeding & Genebank Operations, Biodiversity and Crop Improvement Program, P.O. Box 10000 Rabat, Morocco
3
International Maize and Wheat Improvement Center (CIMMYT), Carretera México-Veracruz Km. 45, El Batán, Texcoco C.P. 56237, Mexico
4
Sustainable Field Crops Programme, IRTA (Institute for Food and Agricultural Research and Technology), 25198 Lleida, Spain
5
International Center for Agricultural Research in the Dry Areas (ICARDA), Biodiversity and Crop Improvement Program, Regional Cereal Rust Research Center (RCRRC), P.O. Box 35661 Menemen, Izmir, Turkey
*
Author to whom correspondence should be addressed.
Academic Editor: Adnane Boualem
Plants 2021, 10(3), 558; https://doi.org/10.3390/plants10030558
Received: 25 January 2021
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Revised: 28 February 2021
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Accepted: 13 March 2021
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Published: 16 March 2021
(This article belongs to the Section Plant Genetics, Genomics and Biotechnology)
Wheat 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.
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Keywords:
genomic prediction; wheat landraces; yellow rust; leaf rust; stem rust
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MDPI and ACS Style
Tehseen, M.M.; Kehel, Z.; Sansaloni, C.P.; Lopes, M.d.S.; Amri, A.; Kurtulus, E.; Nazari, K. Comparison of Genomic Prediction Methods for Yellow, Stem, and Leaf Rust Resistance in Wheat Landraces from Afghanistan. Plants 2021, 10, 558. https://doi.org/10.3390/plants10030558
AMA Style
Tehseen MM, Kehel Z, Sansaloni CP, Lopes MdS, Amri A, Kurtulus E, Nazari K. Comparison of Genomic Prediction Methods for Yellow, Stem, and Leaf Rust Resistance in Wheat Landraces from Afghanistan. Plants. 2021; 10(3):558. https://doi.org/10.3390/plants10030558
Chicago/Turabian StyleTehseen, Muhammad M., Zakaria Kehel, Carolina P. Sansaloni, Marta d.S. Lopes, Ahmed Amri, Ezgi Kurtulus, and Kumarse Nazari. 2021. "Comparison of Genomic Prediction Methods for Yellow, Stem, and Leaf Rust Resistance in Wheat Landraces from Afghanistan" Plants 10, no. 3: 558. https://doi.org/10.3390/plants10030558
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