Facultad de Telemática, Universidad de Colima, Colima, 28040, México.
Department of Crop and Soil Sciences, Washington State University, Pullman, WA, 99164, USA.
BMC Genomics. 2024 May 31;25(1):544. doi: 10.1186/s12864-024-10438-4.
In the realm of multi-environment prediction, when the goal is to predict a complete environment using the others as a training set, the efficiency of genomic selection (GS) falls short of expectations. Genotype by environment interaction poses a challenge in achieving high prediction accuracies. Consequently, current efforts are focused on enhancing efficiency by integrating various types of inputs, such as phenomics data, environmental information, and other omics data. In this study, we sought to evaluate the impact of incorporating environmental information into the modeling process, in addition to genomic and phenomics information. Our evaluation encompassed five data sets of soft white winter wheat, and the results revealed a significant improvement in prediction accuracy, as measured by the normalized root mean square error (NRMSE), through the integration of environmental information. Notably, there was an average gain in prediction accuracy of 49.19% in terms of NRMSE across the data sets. Moreover, the observed prediction accuracy ranged from 5.68% (data set 3) to 60.36% (data set 4), underscoring the substantial effect of integrating environmental information. By including genomic, phenomic, and environmental data in prediction models, plant breeding programs can improve selection efficiency across locations.
在多环境预测领域，当目标是使用其他环境作为训练集来预测完整环境时，基因组选择（GS）的效率不尽如人意。基因型与环境互作在实现高精度预测方面带来了挑战。因此，目前的努力集中在通过整合各种类型的输入，如表型数据、环境信息和其他组学数据，来提高效率。在这项研究中，我们试图评估将环境信息纳入建模过程的影响，除了基因组和表型信息。我们的评估涵盖了五个软白冬小麦数据集，结果表明，通过整合环境信息，预测准确性有了显著提高，以归一化均方根误差（NRMSE）衡量。值得注意的是，在所有数据集上，NRMSE 的平均预测准确性提高了 49.19%。此外，观察到的预测准确性范围从 5.68%（数据集 3）到 60.36%（数据集 4），这突显了整合环境信息的显著效果。通过在预测模型中包含基因组、表型和环境数据，植物育种计划可以提高跨地点的选择效率。
