First and third authors: The Ohio State University, Ohio Agricultural Research and Development Center Department of Horticulture and Crop Science, 1680 Madison Ave, Wooster 44691; and second author: Sejong University Korea Department of Bioresources Engineering, 209 Neungdon-ro, Gwangjin-gu, Seoul, South Korea.
Phytopathology. 2018 Mar;108(3):392-401. doi: 10.1094/PHYTO-12-16-0431-R. Epub 2018 Jan 29.
Bacterial spot affects tomato crops (Solanum lycopersicum) grown under humid conditions. Major genes and quantitative trait loci (QTL) for resistance have been described, and multiple loci from diverse sources need to be combined to improve disease control. We investigated genomic selection (GS) prediction models for resistance to Xanthomonas euvesicatoria and experimentally evaluated the accuracy of these models. The training population consisted of 109 families combining resistance from four sources and directionally selected from a population of 1,100 individuals. The families were evaluated on a plot basis in replicated inoculated trials and genotyped with single nucleotide polymorphisms (SNP). We compared the prediction ability of models developed with 14 to 387 SNP. Genomic estimated breeding values (GEBV) were derived using Bayesian least absolute shrinkage and selection operator regression (BL) and ridge regression (RR). Evaluations were based on leave-one-out cross validation and on empirical observations in replicated field trials using the next generation of inbred progeny and a hybrid population resulting from selections in the training population. Prediction ability was evaluated based on correlations between GEBV and phenotypes (r), percentage of coselection between genomic and phenotypic selection, and relative efficiency of selection (r/r). Results were similar with BL and RR models. Models using only markers previously identified as significantly associated with resistance but weighted based on GEBV and mixed models with markers associated with resistance treated as fixed effects and markers distributed in the genome treated as random effects offered greater accuracy and a high percentage of coselection. The accuracy of these models to predict the performance of progeny and hybrids exceeded the accuracy of phenotypic selection.
细菌斑点病影响在潮湿条件下种植的番茄作物（Solanum lycopersicum）。已经描述了对该病害具有抗性的主要基因和数量性状位点（QTL），需要将来自不同来源的多个位点进行组合，以改善病害控制。我们研究了对黄单胞菌（Xanthomonas euvesicatoria）抗性的基因组选择（GS）预测模型，并通过实验评估了这些模型的准确性。训练群体由来自四个来源的抗性的 109 个家系组成，这些家系从 1100 个个体的群体中定向选择。通过在接种试验中对小区进行评估，并使用单核苷酸多态性（SNP）进行基因型分析，对这些家系进行评估。我们比较了使用 14 到 387 个 SNP 开发的模型的预测能力。使用贝叶斯最小绝对收缩和选择算子回归（BL）和岭回归（RR）从基因组估计育种值（GEBV）中导出。评估是基于交叉验证和在田间重复试验中的经验观察，使用下一代自交系和从训练群体中选择的杂交群体。预测能力是基于 GEBV 与表型之间的相关性（r）、基因组选择和表型选择之间的共选择百分比以及选择的相对效率（r/r）来评估的。BL 和 RR 模型的结果相似。仅使用先前被确定与抗性显著相关但根据 GEBV 加权的标记，或使用将与抗性相关的标记作为固定效应并将标记分布在基因组中作为随机效应的混合模型的模型，提供了更高的准确性和更高的共选择百分比。这些模型预测后代和杂种性能的准确性超过了表型选择的准确性。
