Table_4_Prior Biological Knowledge Improves Genomic Prediction of Growth-Related Traits in Arabidopsis thaliana.XLSX

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 (ΦPSII) 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 ΦPSII 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.

预测与生长相关复杂性状对于作物育种至关重要。光合作用效率和生物量是整体植物性能的直接指标，因此，这些性状的微小改进均可带来显著的育种收益。基因组学和表型学技术的进步已彻底革新了复杂性状的作物育种。充分利用日益丰富的基因组数据，全基因组标记预测模型允许高效地选择下一代最佳亲本，而无需依赖表型信息。迄今为止，此类模型主要直接从基因型预测表型，未能充分利用相关的生物学知识。至于在多大程度上利用此类生物学知识有助于提升基因组预测的准确性和可靠性，仍是一个悬而未决的问题。在本研究中，我们探讨了利用公开的生物信息进行拟南芥（Arabidopsis thaliana）光合光能利用效率（ΦPSII）和预测叶面积（PLA）的基因组预测。为了探索各种类型知识的运用，我们将基因组多态性与基因本体（Gene Ontology，GO）术语和基于转录组学的基因簇进行映射，并将这些应用于基因组特征最优线性无偏预测（Genomic Feature Best Linear Unbiased Predictor，GFBLUP）模型中，该模型是传统基因组最佳线性无偏预测（Genomic BLUP，GBLUP）基准的扩展。我们的结果表明，整合先验生物学知识可以提升ΦPSII和PLA的基因组预测准确度。所实现的改进取决于性状、知识类型和性状遗传力。此外，当用于定义基因的功能群体时，转录组学为基因本体提供了互补的证据，有助于改进。总之，关于性状特异性基因群体的先验知识可以直接转化为基因组预测的改进。