Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, Potsdam-Golm 14476, Germany.
Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm 14476, Germany.
G3 (Bethesda). 2022 Aug 25;12(9). doi: 10.1093/g3journal/jkac170.
Genomic prediction has revolutionized crop breeding despite remaining issues of transferability of models to unseen environmental conditions and environments. Usage of endophenotypes rather than genomic markers leads to the possibility of building phenomic prediction models that can account, in part, for this challenge. Here, we compare and contrast genomic prediction and phenomic prediction models for 3 growth-related traits, namely, leaf count, tree height, and trunk diameter, from 2 coffee 3-way hybrid populations exposed to a series of treatment-inducing environmental conditions. The models are based on 7 different statistical methods built with genomic markers and ChlF data used as predictors. This comparative analysis demonstrates that the best-performing phenomic prediction models show higher predictability than the best genomic prediction models for the considered traits and environments in the vast majority of comparisons within 3-way hybrid populations. In addition, we show that phenomic prediction models are transferrable between conditions but to a lower extent between populations and we conclude that chlorophyll a fluorescence data can serve as alternative predictors in statistical models of coffee hybrid performance. Future directions will explore their combination with other endophenotypes to further improve the prediction of growth-related traits for crops.
尽管在将模型转移到未知环境条件和环境方面仍然存在问题，但基因组预测已经彻底改变了作物育种。使用内表现型而不是基因组标记，可以构建表型预测模型，部分解决这一挑战。在这里，我们比较和对比了 2 个咖啡 3 向杂交群体在一系列诱导环境条件下的 3 个与生长相关的性状（叶数、树高和树干直径）的基因组预测和表型预测模型。这些模型基于 7 种不同的统计方法，使用基因组标记和 ChlF 数据作为预测因子。这项比较分析表明，在绝大多数 3 向杂交群体内的比较中，表现最佳的表型预测模型在考虑的性状和环境下，比表现最佳的基因组预测模型具有更高的可预测性。此外，我们表明，表型预测模型可以在条件之间转移，但在群体之间的转移程度较低，我们得出结论，叶绿素荧光数据可以作为咖啡杂种性能统计模型的替代预测因子。未来的方向将探索它们与其他内表现型的结合，以进一步提高对作物生长相关性状的预测。
