Yang Yaodong, Saand Mumtaz Ali, Huang Liyun, Abdelaal Walid Badawy, Zhang Jun, Wu Yi, Li Jing, Sirohi Muzafar Hussain, Wang Fuyou
Hainan Key Laboratory of Tropical Oil Crops Biology/Coconut Research Institute, Chinese Academy of Tropical Agricultural Sciences, Wenchang, China.
Department of Botany, Shah Abdul Latif University, Khairpur, Pakistan.
Front Plant Sci. 2021 Sep 3;12:563953. doi: 10.3389/fpls.2021.563953. eCollection 2021.
Multiple "omics" approaches have emerged as successful technologies for plant systems over the last few decades. Advances in next-generation sequencing (NGS) have paved a way for a new generation of different omics, such as genomics, transcriptomics, and proteomics. However, metabolomics, ionomics, and phenomics have also been well-documented in crop science. Multi-omics approaches with high throughput techniques have played an important role in elucidating growth, senescence, yield, and the responses to biotic and abiotic stress in numerous crops. These omics approaches have been implemented in some important crops including wheat ( L.), soybean (), tomato (), barley ( L.), maize ( L.), millet ( L.), cotton ( L.), , and rice ( L.). The integration of functional genomics with other omics highlights the relationships between crop genomes and phenotypes under specific physiological and environmental conditions. The purpose of this review is to dissect the role and integration of multi-omics technologies for crop breeding science. We highlight the applications of various omics approaches, such as genomics, transcriptomics, proteomics, metabolomics, phenomics, and ionomics, and the implementation of robust methods to improve crop genetics and breeding science. Potential challenges that confront the integration of multi-omics with regard to the functional analysis of genes and their networks as well as the development of potential traits for crop improvement are discussed. The panomics platform allows for the integration of complex omics to construct models that can be used to predict complex traits. Systems biology integration with multi-omics datasets can enhance our understanding of molecular regulator networks for crop improvement. In this context, we suggest the integration of entire omics by employing the "phenotype to genotype" and "genotype to phenotype" concept. Hence, top-down (phenotype to genotype) and bottom-up (genotype to phenotype) model through integration of multi-omics with systems biology may be beneficial for crop breeding improvement under conditions of environmental stresses.
在过去几十年里，多种“组学”方法已成为植物系统研究中的成功技术。新一代测序（NGS）技术的进步为新一代不同组学，如基因组学、转录组学和蛋白质组学，铺平了道路。然而，代谢组学、离子组学和表型组学在作物科学中也有充分的文献记载。高通量技术的多组学方法在阐明众多作物的生长、衰老、产量以及对生物和非生物胁迫的反应方面发挥了重要作用。这些组学方法已应用于一些重要作物，包括小麦（L.）、大豆（）、番茄（）、大麦（L.）、玉米（L.）、谷子（L.）、棉花（L.）、，以及水稻（L.）。功能基因组学与其他组学的整合突出了特定生理和环境条件下作物基因组与表型之间的关系。本综述的目的是剖析多组学技术在作物育种科学中的作用和整合。我们重点介绍了各种组学方法的应用，如基因组学、转录组学、蛋白质组学、代谢组学、表型组学和离子组学，以及为改进作物遗传学和育种科学而实施的稳健方法。讨论了多组学整合在基因及其网络功能分析以及作物改良潜在性状开发方面面临的潜在挑战。全景组学平台允许整合复杂的组学以构建可用于预测复杂性状的模型。系统生物学与多组学数据集的整合可以增强我们对作物改良分子调控网络的理解。在此背景下，我们建议采用“表型到基因型”和“基因型到表型”的概念来整合整个组学。因此，通过将多组学与系统生物学整合的自上而下（表型到基因型）和自下而上（基因型到表型）模型可能有利于在环境胁迫条件下的作物育种改良。
