Prediction of growth-related complex traits is highly important for crop breeding. Photosynthesis efficiency and biomass are direct indicators of overall plant performance and therefore even minor improvements in these traits can result in significant breeding gains. Crop breeding for complex traits has been revolutionized by technological developments in genomics and phenomics. Capitalizing on the growing availability of genomics data, genome-wide marker-based prediction models allow for efficient selection of the best parents for the next generation without the need for phenotypic information. Until now such models mostly predict the phenotype directly from the genotype and fail to make use of relevant biological knowledge. It is an open question to what extent the use of such biological knowledge is beneficial for improving genomic prediction accuracy and reliability. In this study, we explored the use of publicly available biological information for genomic prediction of photosynthetic light use efficiency (Φ_PSII) and projected leaf area (PLA) in Arabidopsis thaliana. To explore the use of various types of knowledge, we mapped genomic polymorphisms to Gene Ontology (GO) terms and transcriptomics-based gene clusters, and applied these in a Genomic Feature Best Linear Unbiased Predictor (GFBLUP) model, which is an extension to the traditional Genomic BLUP (GBLUP) benchmark. Our results suggest that incorporation of prior biological knowledge can improve genomic prediction accuracy for both Φ_PSII and PLA. The improvement achieved depends on the trait, type of knowledge and trait heritability. Moreover, transcriptomics offers complementary evidence to the Gene Ontology for improvement when used to define functional groups of genes. In conclusion, prior knowledge about trait-specific groups of genes can be directly translated into improved genomic prediction.

与生长相关的复杂性状的预测对于作物育种非常重要。光合作用效率和生物量是植物整体性能的直接指标，因此即使这些性状的微小改进也可以带来显着的育种收益。基因组学和表型组学技术的发展彻底改变了复杂性状作物育种。利用不断增长的基因组学数据，基于标记的全基因组预测模型可以有效地为下一代选择最好的亲本，而无需表型信息。到目前为止，此类模型大多直接从基因型预测表型，未能利用相关的生物学知识。使用此类生物学知识在多大程度上有利于提高基因组预测的准确性和可靠性是一个悬而未决的问题。在这项研究中，我们探索了利用公开的生物信息对拟南芥光合光利用效率 (Φ _PSII ) 和预计叶面积 (PLA) 进行基因组预测。为了探索各种类型知识的用途，我们将基因组多态性映射到基因本体（GO）术语和基于转录组学的基因簇，并将其应用到基因组特征最佳线性无偏预测（GFBLUP）模型中，该模型是传统的基因组 BLUP (GBLUP) 基准。我们的结果表明，结合先前的生物学知识可以提高 Φ _PSII和 PLA 的基因组预测准确性。所取得的进步取决于特质、知识类型和特质遗传力。此外，转录组学为基因本体论在用于定义基因功能组时提供了改进的补充证据。 总之，关于性状特异性基因组的先验知识可以直接转化为改进的基因组预测。