Tehseen Muhammad Massub, Kehel Zakaria, Sansaloni Carolina P, Lopes Marta da Silva, Amri Ahmed, Kurtulus Ezgi, Nazari Kumarse
Department of Field Crops, Ege University, P.O. Box 35100 Bornova, Izmir, Turkey.
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.
Plants (Basel). 2021 Mar 16;10(3):558. doi: 10.3390/plants10030558.
Wheat rust diseases, including yellow rust (Yr; also known as stripe rust) caused by Westend. f. sp. , leaf rust (Lr) caused by Eriks. and stem rust (Sr) caused by Pres f. sp. 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.
小麦锈病，包括由小麦条锈菌（Puccinia striiformis Westend. f. sp. tritici）引起的条锈病（Yr；也称为条斑锈病）、由隐匿柄锈菌（Puccinia triticina Eriks.）引起的叶锈病（Lr）和由禾柄锈菌小麦专化型（Puccinia graminis Pers. f. sp. tritici）引起的秆锈病（Sr），是全球小麦生产的主要威胁。通过对抗性种质进行基因组辅助预测以提高单位时间内的遗传增益，可实现对小麦锈病的持久抗性。基因组预测（GP）是一项很有前景的技术，它利用基因组标记来估计基因组辅助育种值（GBEVs），以选择抗性植物基因型并积累对小麦锈病的成株抗性（APR）的有利等位基因。为了评估基因组预测，我们比较了九种不同的参数模型、半参数模型和贝叶斯模型的预测能力，这些模型包括基因组无偏线性预测（GBLUP）、岭回归（RR）、最小绝对收缩和选择算子（LASSO）、弹性网络（EN）、贝叶斯岭回归（BRR）、贝叶斯A（BA）、贝叶斯B（BB）、贝叶斯C（BC）和再生核希尔伯特空间模型（RKHS），以估计一组363份阿富汗起源的面包小麦地方品种对小麦条锈病、叶锈病和秆锈病的成株抗性的基因组育种值。基于五重交叉验证，条锈病（2016年）、条锈病（2017年）、叶锈病和秆锈病的平均预测能力分别为0.33、0.30、0.38和0.33。对于所有性状，没有一个模型的表现优于其他模型。在五个性状中的四个性状上，LASSO和EN表现出最低的预测能力。GBLUP和RR的预测能力相似，而贝叶斯模型之间也没有显著差异。我们还研究了分析中使用的基因型数量和标记对基因组预测模型预测能力的影响。使用1000个标记时预测能力最高，并且预测能力与训练群体大小呈线性趋势。该研究结果令人鼓舞，证实了基因组预测有效地应用于抗所有三种小麦锈病的育种计划的可行性。
