Training populations for pedigree and genomic prediction in plant breeding programmes are largely updated with superior genotypes from multi‐environment trials, where they are tested with the goal of variety development. Such a selective phenotyping has, however, a negative impact on prediction abilities, especially when only a subset of breeding lines can be tested, as for laborious and costly to phenotype traits. This study focused, thus, on investigating the impact of selective phenotyping in the training population of an applied wheat breeding programme, and assessing the potential to mitigate this impact by pedigree and genomic multi‐trait prediction models as well as bi‐directionally selected training populations for several baking quality parameters. Combining both pedigree and genomic information in multi‐trait prediction models with pre‐existing phenotypic information for protein content and sedimentation value compensated for the observed loss in prediction ability, while entering few inferior breeding lines into the training population further mitigated the impact of selective phenotyping and even led to a slight increase of prediction ability in comparison to a randomly chosen training population.

中文翻译：

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通过多特征谱系和基因组选择模型减轻训练人群中选择性表型对预测能力的影响

植物育种计划中用于谱系和基因组预测的培训人群在很大程度上得到了来自多环境试验的优良基因型的更新，并以品种开发为目标进行了测试。然而，这种选择性表型对预测能力具有负面影响，特别是在仅可以测试一个繁殖系子集的情况下，如对表型性状费力且昂贵。因此，本研究的重点是调查选择性表型对应用小麦育种计划的培训人群的影响，并通过谱系和基因组多性状预测模型以及双向选择的培训人群评估减轻这种影响的潜力几个烘烤质量参数。