Abstract：
CONCLUSIONS: Genomic selection and its importance in crop breeding. Integration of GS with new breeding tools and developing SOP for GS to achieve maximum genetic gain with low cost and time. The success of conventional breeding approaches is not sufficient to meet the demand of a growing population for nutritious food and other plant-based products. Whereas, marker assisted selection (MAS) is not efficient in capturing all the favorable alleles responsible for economic traits in the process of crop improvement. Genomic selection (GS) developed in livestock breeding and then adapted to plant breeding promised to overcome the drawbacks of MAS and significantly improve complicated traits controlled by gene/QTL with small effects. Large-scale deployment of GS in important crops, as well as simulation studies in a variety of contexts, addressed G × E interaction effects and non-additive effects, as well as lowering breeding costs and time. The current study provides a complete overview of genomic selection, its process, and importance in modern plant breeding, along with insights into its application. GS has been implemented in the improvement of complex traits including tolerance to biotic and abiotic stresses. Furthermore, this review hypothesises that using GS in conjunction with other crop improvement platforms accelerates the breeding process to increase genetic gain. The objective of this review is to highlight the development of an appropriate GS model, the global open source network for GS, and trans-disciplinary approaches for effective accelerated crop improvement. The current study focused on the application of data science, including machine learning and deep learning tools, to enhance the accuracy of prediction models. Present study emphasizes on developing plant breeding strategies centered on GS combined with routine conventional breeding principles by developing GS-SOP to achieve enhanced genetic gain.
摘要：
结论：基因组选择及其在作物育种中的重要性。将GS与新的育种工具整合，并开发GS的SOP，以低成本和时间实现最大的遗传增益。常规育种方法的成功不足以满足不断增长的人口对营养食品和其他基于植物的产品的需求。然而,标记辅助选择（MAS）在作物改良过程中无法有效捕获所有负责经济性状的有利等位基因。在家畜育种中开发的基因组选择（GS），然后适应植物育种，有望克服MAS的缺点，并显着改善由基因/QTL控制的复杂性状，效果较小。在重要农作物中大规模部署GS，以及各种情况下的模拟研究，解决了G×E相互作用效应和非加性效应，以及降低育种成本和时间。目前的研究提供了基因组选择的完整概述，其过程,以及在现代植物育种中的重要性，以及对其应用的见解。GS已用于改善复杂性状，包括对生物和非生物胁迫的耐受性。此外，这篇综述假设，将GS与其他作物改良平台结合使用可以加速育种过程以增加遗传增益。本次审查的目的是强调开发适当的GS模型，GS的全球开源网络，以及有效加速作物改良的跨学科方法。当前的研究集中在数据科学的应用上，包括机器学习和深度学习工具，提高预测模型的准确性。本研究强调通过开发GS-SOP来开发以GS为中心的植物育种策略，并结合常规常规育种原理，以提高遗传增益。
