Farooq Muhammad, van Dijk Aalt D J, Nijveen Harm, Aarts Mark G M, Kruijer Willem, Nguyen Thu-Phuong, Mansoor Shahid, de Ridder Dick
Bioinformatics Group, Wageningen University, Wageningen, Netherlands.
Molecular Virology and Gene Silencing Lab, Agricultural Biotechnology Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), Punjab, Pakistan.
Front Genet. 2021 Jan 20;11:609117. doi: 10.3389/fgene.2020.609117. eCollection 2020.
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 ) and projected leaf area (PLA) in . 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 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.
对与生长相关的复杂性状进行预测对作物育种至关重要。光合效率和生物量是植物整体表现的直接指标，因此这些性状即使有微小的改善也能带来显著的育种效益。基因组学和表型组学的技术发展彻底改变了复杂性状的作物育种。利用日益丰富的基因组数据，基于全基因组标记的预测模型能够在无需表型信息的情况下，高效选择下一代的最佳亲本。到目前为止，这类模型大多直接从基因型预测表型，未能利用相关生物学知识。利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仍是一个悬而未决的问题。在本研究中，我们探索了利用公开可用的生物学信息对光合光利用效率（Φ ）和预测叶面积（PLA）进行基因组预测。为了探索各类知识的用途，我们将基因组多态性映射到基因本体论（GO）术语和基于转录组学的基因簇，并将其应用于基因组特征最佳线性无偏预测器（GFBLUP）模型，该模型是传统基因组最佳线性无偏预测器（GBLUP）基准的扩展。我们的结果表明，纳入先验生物学知识可以提高Φ 和PLA的基因组预测准确性。实现的改进取决于性状、知识类型和性状遗传力。此外，转录组学在用于定义基因功能组时，为基因本体论的改进提供了补充证据。总之，关于特定性状基因组的先验知识可以直接转化为改进的基因组预测。
