Abstract
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.
Highlights
Wheat is one of the most important cereal crops of the world, grown from the Equator to the Arctic Circle, and considered a staple food of 35% of the world population, which serves as the major source of carbohydrates in the human diet [1,2]
This study aims to compare and evaluate the Genomic prediction (GP) accuracies of nine different methods including parametric, semi-parametric, and Bayesian models to predict adult plant resistance (APR) for yellow, Plants 2021, 10, 558 leaf, and stem rust of wheat in a panel of bread wheat landraces from Afghanistan
The landraces showed the highest percentage of resistant investigated whether the number of landraces in the training population and the reaction to leaf rust in 2016 (21.5%), while the lowest percentage of resistant landraces was numbers of markers usedinin have an case effect on therust predictive ability of similar the observed leafthe rustanalyses in 2017 (9%)
Summary
Wheat is one of the most important cereal crops of the world, grown from the Equator to the Arctic Circle, and considered a staple food of 35% of the world population, which serves as the major source of carbohydrates in the human diet [1,2]. Several genes for resistance to each of the three rusts have been catalogued and genetically characterized [4], many of the known genes have become ineffective, that is susceptible, to newly virulent races of the pathotypes. A continuous search for new sources of resistance is of paramount importance in the fight against the wheat rust diseases [5]. Fungicides have been used to control wheat rusts; new races frequently develop resistance to commonly used fungicides, complicating the control of these diseases [6]. Environmental concerns have prompted some countries (e.g., the EU) to impose regulations to restrict the amounts of chemical products used in agriculture (Directive 2009/128/EC), reinforcing the importance of ensuring rusts resistance in modern varieties
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