Table_1_Genomic Selection for Ascochyta Blight Resistance in Pea.xlsx

Genomic selection (GS) is a breeding tool, which is rapidly gaining popularity for plant breeding, particularly for traits that are difficult to measure. One such trait is ascochyta blight resistance in pea (Pisum sativum L.), which is difficult to assay because it is strongly influenced by the environment and depends on the natural occurrence of multiple pathogens. Here we report a study of the efficacy of GS for predicting ascochyta blight resistance in pea, as represented by ascochyta blight disease score (ASC), and using nucleotide polymorphism data acquired through genotyping-by-sequencing. The effects on prediction accuracy of different GS models and different thresholds for missing genotypic data (which modified the number of single nucleotide polymorphisms used in the analysis) were compared using cross-validation. Additionally, the inclusion of marker × environment interactions in a genomic best linear unbiased prediction (GBLUP) model was evaluated. Finally, different ways of combining trait data from two field trials using bivariate, spatial, and single-stage analyses were compared to results obtained using a mean value. The best prediction accuracy achieved for ASC was 0.56, obtained using GBLUP analysis with a mean value for ASC and data quality threshold of 70% (i.e., missing SNP data in <30% of lines). GBLUP and Bayesian Reproducing kernel Hilbert spaces regression (RKHS) performed slightly better than the other models trialed, whereas different missing data thresholds made minimal differences to prediction accuracy. The prediction accuracies of individual, randomly selected, testing/training partitions were highly variable, highlighting the effect that the choice of training population has on prediction accuracy. The inclusion of marker × environment interactions did not increase the prediction accuracy for lines which had not been phenotyped, but did improve the results of prediction across environments. GS is potentially useful for pea breeding programs pursuing ascochyta blight resistance, both for predicting breeding values for lines that have not been phenotyped, and for providing enhanced estimated breeding values for lines for which trait data is available.

基因组选择（Genomic Selection, GS）是一种育种工具，目前在植物育种领域迅速普及，尤其适用于难以直接测定的性状。其中一类典型难测性状为豌豆（Pisum sativum L.）的壳二孢枯萎病（Ascochyta blight）抗性，该性状的鉴定难度极大，不仅受环境影响显著，还依赖于多种病原菌的自然侵染。本研究旨在探究基因组选择（GS）对豌豆壳二孢枯萎病抗性的预测效能，以壳二孢枯萎病病情指数（Ascochyta blight disease score, ASC）作为抗性表征指标，并采用通过测序分型技术（genotyping-by-sequencing, GBS）获取的核苷酸多态性数据开展分析。本研究通过交叉验证法，对比了不同GS模型以及不同缺失基因型数据阈值（该阈值会调整分析中使用的单核苷酸多态性位点数量）对预测精度的影响。此外，本研究还评估了在基因组最佳线性无偏预测（Genomic Best Linear Unbiased Prediction, GBLUP）模型中纳入标记-环境互作项的效果。最后，本研究对比了采用双变量分析、空间分析以及单阶段分析三种方式整合两个田间试验的性状数据的效果，并与仅使用性状平均值的分析结果进行了比较。本研究获得的ASC最高预测精度可达0.56，该结果通过GBLUP分析实现，此时使用ASC性状平均值，且数据质量阈值设定为70%（即品系的单核苷酸多态性（Single Nucleotide Polymorphism, SNP）缺失率低于30%）。GBLUP与贝叶斯再生核希尔伯特空间回归（Bayesian Reproducing Kernel Hilbert Spaces Regression, RKHS）的表现略优于其余测试模型，而不同的缺失数据阈值对预测精度的影响极小。随机选取的不同训练集/测试集分区的预测精度存在显著差异，这凸显了训练群体选择对预测精度的影响。在模型中纳入标记-环境互作项并未提升未表型品系的预测精度，但确实改善了跨环境的预测效果。基因组选择（GS）在致力于培育壳二孢枯萎病抗性品种的豌豆育种项目中具有应用潜力，既可用于预测未表型品系的育种值，也可为已有性状数据的品系提供更精准的估计育种值。