Genomic selection in maize (Zea mays L.) has been one factor that has increased the rate of genetic gain when compared with other cereals. However, the technological foundations in maize also exist in other cereal crops that would allow prediction of hybrid performance based on general (GCA) and specific (SCA) combining abilities applied through genomic-enabled prediction models. Further, the incorporation of genotype × environment (G × E) interaction effects present an opportunity to deploy hybrids to targeted environments. To test these concepts, a factorial mating design of elite yet divergent grain sorghum lines generated hybrids for evaluation. Inbred parents were genotyped, and markers were used to assess population structure and develop the genomic relationship matrix (GRM). Grain yield, height, and days to anthesis were collected for hybrids in replicated trials, and best linear unbiased estimates were used to train classical GCA-SCA–based and genomic (GB) models under a hierarchical Bayesian framework. To incorporate population structure, GB was fitted using the GRM of both parents and hybrids. For GB models, G × E interaction effects were included by the Hadamard product between GRM and environments. A leave-one-out cross-validation scheme was used to study the prediction capacity of models. Classical and genomic models effectively predicted hybrid performance and prediction accuracy increased by including genomic data. Genomic models effectively partitioned the variation due to GCA, SCA, and their interaction with the environment. A strategy to implement genomic selection for hybrid sorghum [Sorghum bicolor (L.) Moench] breeding is presented herein.

中文翻译：

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评估组合能力、基因组数据和基因型 × 环境相互作用以预测杂交高粱的性能

玉米中的基因组选择（Zea maysL.) 是与其他谷物相比提高遗传增益率的一个因素。然而，玉米的技术基础也存在于其他谷类作物中，这将允许基于通过基因组预测模型应用的通用 (GCA) 和特定 (SCA) 组合能力来预测杂交性能。此外，基因型×环境（G×E）相互作用效应的结合为将杂交种部署到目标环境提供了机会。为了测试这些概念，优良但发散的高粱品系的因子交配设计产生了用于评估的杂种。对近交亲本进行基因分型，并使用标记来评估种群结构并开发基因组关系矩阵（GRM）。在重复试验中收集杂种的谷物产量、高度和开花天数，和最佳线性无偏估计用于在分层贝叶斯框架下训练经典的基于 GCA-SCA 和基因组 (GB) 模型。为了纳入种群结构，GB 使用亲本和杂种的 GRM 进行拟合。对于 GB 模型，G×E 交互效应包含在 GRM 和环境之间的 Hadamard 产品中。留一法交叉验证方案用于研究模型的预测能力。经典和基因组模型通过包含基因组数据有效地预测了杂交性能和预测准确性。基因组模型有效地划分了由 GCA、SCA 及其与环境相互作用引起的变异。一种实施杂交高粱基因组选择的策略[ 使用亲本和杂种的 GRM 拟合 GB。对于 GB 模型，G×E 交互效应包含在 GRM 和环境之间的 Hadamard 产品中。留一法交叉验证方案用于研究模型的预测能力。经典和基因组模型通过包含基因组数据有效地预测了杂交性能和预测准确性。基因组模型有效地划分了由 GCA、SCA 及其与环境相互作用引起的变异。一种实施杂交高粱基因组选择的策略[ 使用亲本和杂种的 GRM 拟合 GB。对于 GB 模型，G×E 交互效应包含在 GRM 和环境之间的 Hadamard 产品中。留一法交叉验证方案用于研究模型的预测能力。经典和基因组模型通过包含基因组数据有效地预测了杂交性能和预测准确性。基因组模型有效地划分了由 GCA、SCA 及其与环境相互作用引起的变异。一种实施杂交高粱基因组选择的策略[ 经典和基因组模型通过包含基因组数据有效地预测了杂交性能和预测准确性。基因组模型有效地划分了由 GCA、SCA 及其与环境相互作用引起的变异。一种实施杂交高粱基因组选择的策略[ 经典和基因组模型通过包含基因组数据有效地预测了杂交性能和预测准确性。基因组模型有效地划分了由 GCA、SCA 及其与环境相互作用引起的变异。一种实施杂交高粱基因组选择的策略[本文介绍了双色高粱(L.) Moench] 的育种。