Carpenter Margaret A, Goulden David S, Woods Carmel J, Thomson Susan J, Kenel Fernand, Frew Tonya J, Cooper Rebecca D, Timmerman-Vaughan Gail M
The New Zealand Institute for Plant & Food Research Limited, Christchurch, New Zealand.
Front Plant Sci. 2018 Dec 20;9:1878. doi: 10.3389/fpls.2018.01878. eCollection 2018.
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 ( 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（GS）是一种育种工具，在植物育种中迅速受到欢迎，特别是对于难以测量的性状。其中一个这样的性状是豌豆（L.）对壳二孢叶枯病的抗性，由于它受环境影响很大且取决于多种病原体的自然发生情况，因此难以测定。在此，我们报告一项关于GS预测豌豆壳二孢叶枯病抗性功效的研究，以壳二孢叶枯病病情评分（ASC）表示，并使用通过简化基因组测序获得的核苷酸多态性数据。使用交叉验证比较了不同GS模型以及不同缺失基因型数据阈值（这改变了分析中使用的单核苷酸多态性数量）对预测准确性的影响。此外，还评估了在基因组最佳线性无偏预测（GBLUP）模型中纳入标记×环境互作的情况。最后，比较了使用双变量、空间和单阶段分析将来自两个田间试验的性状数据进行组合的不同方法与使用平均值获得的结果。使用ASC平均值和70%的数据质量阈值（即<30%的品系中存在SNP数据缺失）进行GBLUP分析时，ASC获得的最佳预测准确性为0.56。GBLUP和贝叶斯再生核希尔伯特空间回归（RKHS）的表现略优于其他试验模型，而不同的缺失数据阈值对预测准确性的影响最小。单个随机选择的测试/训练分区的预测准确性差异很大，突出了训练群体的选择对预测准确性的影响。纳入标记×环境互作并没有提高未进行表型分析的品系的预测准确性，但确实改善了跨环境的预测结果。对于追求壳二孢叶枯病抗性的豌豆育种计划，GS可能有用，既可以预测未进行表型分析的品系的育种值，也可以为已有性状数据的品系提供更高的估计育种值。
