Genotyping‐by‐sequencing has enabled approaches for genomic selection to improve yield, stress resistance and nutritional value. More and more resource studies are emerging providing 1000 and more genotypes and millions of SNPs for one species covering a hitherto inaccessible intraspecific genetic variation. The larger the databases are growing, the better statistical approaches for genomic selection will be available. However, there are clear limitations on the statistical but also on the biological part. Intraspecific genetic variation is able to explain a high proportion of the phenotypes, but a large part of phenotypic plasticity also stems from environmentally driven transcriptional, post‐transcriptional, translational, post‐translational, epigenetic and metabolic regulation. Moreover, regulation of the same gene can have different phenotypic outputs in different environments. Consequently, to explain and understand environment‐dependent phenotypic plasticity based on the available genotype variation we have to integrate the analysis of further molecular levels reflecting the complete information flow from the gene to metabolism to phenotype. Interestingly, metabolomics platforms are already more cost‐effective than NGS platforms and are decisive for the prediction of nutritional value or stress resistance. Here, we propose three fundamental pillars for future breeding strategies in the framework of Green Systems Biology: (i) combining genome selection with environment‐dependent PANOMICS analysis and deep learning to improve prediction accuracy for marker‐dependent trait performance; (ii) PANOMICS resolution at subtissue, cellular and subcellular level provides information about fundamental functions of selected markers; (iii) combining PANOMICS with genome editing and speed breeding tools to accelerate and enhance large‐scale functional validation of trait‐specific precision breeding.

中文翻译：

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PANOMICS 遇上种质。

测序基因分型使基因组选择方法能够提高产量、抗逆性和营养价值。越来越多的资源研究不断涌现，为一个物种提供了 1000 多个基因型和数百万个 SNP，涵盖了迄今为止无法获得的种内遗传变异。数据库规模越大，基因组选择的统计方法就会越好。然而，统计和生物学方面都存在明显的局限性。种内遗传变异能够解释很大一部分表型，但表型可塑性很大一部分也源于环境驱动的转录、转录后、翻译、翻译后、表观遗传和代谢调控。此外，同一基因的调控在不同的环境中可能会产生不同的表型输出。因此，为了解释和理解基于可用基因型变异的环境依赖性表型可塑性，我们必须整合进一步分子水平的分析，反映从基因到代谢再到表型的完整信息流。有趣的是，代谢组学平台已经比 NGS 平台更具成本效益，并且对于营养价值或抗压性的预测具有决定性作用。在这里，我们在绿色系统生物学的框架下提出了未来育种策略的三个基本支柱：（i）将基因组选择与环境依赖性全景分析和深度学习相结合，以提高标记依赖性性状表现的预测准确性；(ii) 亚组织、细胞和亚细胞水平的全景组学分辨率提供了有关选定标记物基本功能的信息；(iii) 将PANOMICS与基因组编辑和快速育种工具相结合，以加速和增强性状特异性精准育种的大规模功能验证。