BACKGROUND Genomic selection has the potential to increase genetic gains by using molecular markers as predictors of breeding values of individuals. This study evaluated the accuracy of predictions for grain yield, heading date, plant height, and yield components in soft red winter wheat under different prediction scenarios. Response to selection for grain yield was also compared across different selection strategies- phenotypic, marker-based, genomic, combination of phenotypic and genomic, and random selections. RESULTS Genomic selection was implemented through a ridge regression best linear unbiased prediction model in two scenarios- cross-validations and independent predictions. Accuracy for cross-validations was assessed using a diverse panel under different marker number, training population size, relatedness between training and validation populations, and inclusion of fixed effect in the model. The population in the first scenario was then trained and used to predict grain yield of biparental populations for independent validations. Using subsets of significant markers from association mapping increased accuracy by 64-70% for grain yield but resulted in lower accuracy for traits with high heritability such as plant height. Increasing size of training population resulted in an increase in accuracy, with maximum values reached when ~ 60% of the lines were used as a training panel. Predictions using related subpopulations also resulted in higher accuracies. Inclusion of major growth habit genes as fixed effect in the model caused increase in grain yield accuracy under a cross-validation procedure. Independent predictions resulted in accuracy ranging between - 0.14 and 0.43, dependent on the grouping of site-year data for the training and validation populations. Genomic selection was "superior" to marker-based selection in terms of response to selection for yield. Supplementing phenotypic with genomic selection resulted in approximately 10% gain in response compared to using phenotypic selection alone. CONCLUSIONS Our results showed the effects of different factors on accuracy for yield and agronomic traits. Among the factors studied, training population size and relatedness between training and validation population had the greatest impact on accuracy. Ultimately, combining phenotypic with genomic selection would be relevant for accelerating genetic gains for yield in winter wheat.

中文翻译：

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软红冬小麦籽粒产量和农艺性状基因组选择的准确性。

背景技术基因组选择具有通过使用分子标记作为个体育种值的预测因子来增加遗传增益的潜力。本研究评估了不同预测情景下软红冬小麦籽粒产量、抽穗日期、株高和产量构成的预测准确性。还比较了不同选择策略（表型、基于标记、基因组、表型和基因组的组合以及随机选择）对谷物产量选择的反应。结果基因组选择是通过岭回归最佳线性无偏预测模型在两种情况下实现的——交叉验证和独立预测。使用不同标记数量、训练群体规模、训练和验证群体之间的相关性以及模型中固定效应的不同面板来评估交叉验证的准确性。然后对第一种情况中的种群进行训练并用于预测双亲种群的谷物产量以进行独立验证。使用关联图谱中的重要标记子集可将谷物产量的准确性提高 64-70%，但导致高遗传力性状（如株高）的准确性降低。训练群体规模的增加导致准确性的提高，当约 60% 的品系用作训练组时达到最大值。使用相关亚群的预测也获得了更高的准确性。将主要生长习性基因作为固定效应纳入模型中，在交叉验证程序下导致谷物产量准确性的提高。独立预测的准确度范围在 - 0.14 和 0.43 之间，具体取决于训练和验证群体的现场年份数据分组。就产量选择的响应而言，基因组选择“优于”基于标记的选择。与单独使用表型选择相比，通过基因组选择补充表型可使反应增加约 10%。结论 我们的结果显示了不同因素对产量和农艺性状准确性的影响。在研究的因素中，训练群体规模以及训练和验证群体之间的相关性对准确性的影响最大。最终，将表型与基因组选择相结合将有助于加速冬小麦产量的遗传增益。