Genomic selection has been adopted in many plant breeding programmes. In this paper, we cover some aspects of information necessary before starting genomic selection. Spring oat and barley breeding data sets from commercial breeding programmes were studied using single, multitrait and trait‐assisted models for predicting grain yield. Heritabilities were higher when estimated using multitrait models compared to single‐trait models. However, no corresponding increase in prediction accuracy was observed in a cross‐validation scenario. On the other hand, forward prediction showed a slight, but not significant, increase in accuracy of genomic estimated breeding values for breeding cohorts when a multitrait model was applied. When a correlated trait was used in a trait‐assisted model, on average the accuracies increased by 9%–14% for oat and by 11%–28% for barley compared with a single‐trait model. Overall, accuracies in forward validation varied between breeding cohorts and years for grain yield. Forward prediction accuracies for multiple cohorts and multiple years’ data are reported for oat for the first time.

中文翻译：

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商业芬兰燕麦（Avena sativa）和大麦（Hordeum vulgare）育种计划中谷物产量的基因组预测

基因组选择已在许多植物育种计划中采用。在本文中，我们介绍了开始基因组选择之前必要的信息。利用单种，多性状和性状辅助模型预测了商业产量，研究了来自商业育种计划的春燕麦和大麦育种数据集。与单性状模型相比，使用多性状模型估算时的遗传力较高。但是，在交叉验证方案中，未观察到预测准确性的相应提高。另一方面，当采用多性状模型时，前瞻性预测显示基因组估计育种群体的育种值的准确性略有提高，但不显着。当在性状辅助模型中使用相关性状时，与单性状模型相比，燕麦的平均准确度提高了9％–14％，大麦的准确度提高了11％–28％。总体而言，前瞻性验证的准确性因育种队列和谷物产量的年限而异。首次针对燕麦报告了多个队列的前瞻性预测准确性和多年数据。