End-use quality phenotyping is laborious and expensive, thus, testing may not occur until later generations in wheat breeding programs. We investigated the pattern of genotype × environment (G × E) interaction for end-use quality traits in soft white wheat (Triticum aestivum L.) and tested the effectiveness of implementing genomic selection to optimize breeding for these traits. We used a multi-environment unbalanced dataset comprised of 672 breeding lines and cultivars adapted to the Pacific Northwest region of the United States, which were evaluated for 14 end-use quality traits. Genetic correlations between environments based on factor analytic models showed low-to-moderate G × E interaction for most traits but high G × E interaction for grain and flour protein. A total of 40,518 single-nucleotide polymorphism markers were used for genomic prediction. Genomic prediction accuracies were high for most traits thereby justifying the use of genomic selection to assist breeding for superior end-use quality in soft white wheat. Excluding outlier environments based on genetic correlations between environments was more effective in increasing genomic prediction accuracies compared with that based on environment clustering analysis. For kernel size, kernel weight, milling score, ash, and flour swelling volume, excluding outlier environments increased prediction accuracies by 1–11%. However, for grain and flour protein, flour yield, and cookie diameter, excluding outlier environments did not improve genomic prediction performance.

中文翻译：

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软白冬小麦最终用途品质性状的环境表征和基因组预测

最终用途质量表型分析既费力又昂贵，因此，在小麦育种计划中直到后代才能进行测试。我们研究了软白小麦 ( Triticum aestivum ) 最终用途品质性状的基因型 × 环境 (G × E) 相互作用模式L.）并测试了实施基因组选择以优化这些性状育种的有效性。我们使用了一个多环境不平衡数据集，该数据集由 672 个适应美国太平洋西北地区的育种系和品种组成，并针对 14 个最终用途质量性状进行了评估。基于因子分析模型的环境之间的遗传相关性显示，大多数性状的 G × E 相互作用为低至中等，但谷物和面粉蛋白的 G × E 相互作用高。共有 40,518 个单核苷酸多态性标记用于基因组预测。大多数性状的基因组预测准确度很高，因此证明使用基因组选择来辅助育种以在软白小麦中获得优异的最终用途质量是合理的。与基于环境聚类分析的方法相比，基于环境之间的遗传相关性排除异常环境在提高基因组预测准确性方面更有效。对于籽粒大小、籽粒重量、研磨分数、灰分和面粉膨胀量，排除异常环境后，预测准确度提高了 1-11%。然而，对于谷物和面粉蛋白质、面粉产量和饼干直径，排除异常环境并没有提高基因组预测性能。