Dataset to accompany genomics combined with UAS data enhances prediction of grain yield in winter wheat

With the human population continuing to increase worldwide, there is pressure to employ novel technologies to increase genetic gain in plant breeding programs that contribute to nutrition and food security. Genomic selection (GS) has the potential to increase genetic gain because it can accelerate the breeding cycle, increase the accuracy of estimated breeding values, and improve selection accuracy. However, with recent advances in high throughput phenotyping in plant breeding programs, the opportunity to integrate genomic and phenotypic data to increase prediction accuracy is present. In this paper, we applied GS to winter wheat data integrating two types of inputs: genomic and phenotypic. We observed the best prediction performance when combining both genomic and phenotypic inputs, while only using genomic information fared poorly. Interestingly, using only phenotypic information was slightly worse in some cases than the combination of both sources, whereas in other cases, using only phenotypic information provided the best prediction performance. Our results are encouraging because it is clear we can enhance the prediction accuracy of GS by integrating more related inputs in the models.

随着全球人口持续增长，植物育种项目亟需借助新兴技术提升遗传增益，从而助力营养保障与粮食安全。基因组选择（Genomic Selection, GS）具备提升遗传增益的潜力，其可通过加速育种周期、提高估计育种值的准确度以及优化选择精度来实现目标。然而，随着植物育种项目中高通量表型分析技术的近年突破，整合基因组与表型数据以提升预测精度的契机已然浮现。本研究针对冬小麦数据集开展基因组选择分析，整合了基因组与表型两类输入特征。研究结果显示，同时整合基因组与表型两类输入时，模型的预测性能最优；而仅使用基因组信息时，表现欠佳。值得注意的是，在部分场景下仅使用表型信息的表现略逊于双源整合方案，但在另一些场景中，仅使用表型信息即可实现最优预测性能。本研究结果令人振奋，因为已然证实，通过在模型中整合更多相关输入特征，可有效提升基因组选择的预测精度。