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通过基因组学辅助育种设计适应盐渍环境的未来作物。

Genomics-assisted breeding for designing salinity-smart future crops.

作者信息

Raza Ali, Zaman Qamar U, Shabala Sergey, Tester Mark, Munns Rana, Hu Zhangli, Varshney Rajeev K

机构信息

Guangdong Key Laboratory of Plant Epigenetics, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China.

Shenzhen Engineering Laboratory for Marine Algal Biotechnology, Guangdong Technology Research Center for Marine Algal Biotechnology, Longhua Innovation Institute for Biotechnology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China.

出版信息

Plant Biotechnol J. 2025 Aug;23(8):3119-3151. doi: 10.1111/pbi.70104. Epub 2025 May 20.

Abstract

Climate change induces many abiotic stresses, including soil salinity, significantly challenging global agriculture. Salinity stress tolerance (SST) is a complex trait, both physiologically and genetically, and is conferred at various levels of plant functional organization. As both the sustainability and profitability of agricultural production systems are critically dependent on SST, plant breeders are trying to design and develop salinity-smart crop plants capable of thriving under high salinity conditions. The accessibility of extreme-quality reference genomes for cultivated crops, naturally salinity-smart plants, and crop wild relatives has fast-tracked the discovery of key genes and quantitative trait loci (QTLs), marker development, genotyping assays and molecular breeding products with improved SST. Employing fast-forward breeding tools, namely genomic selection (GS), haplotype-based breeding (HBB), artificial intelligence (AI) and high-throughput phenotyping (HTP), has shown influence not only for fast-tracking genetic gains but also for reducing the time and cost of developing commercial cultivars with enhanced SST and yield stability. This review discusses the advancement and prospects of various genomics-assisted breeding (GAB) tools, including genome sequencing, QTL mapping, GWAS, GS, HBB, pan-genomics, single-cell/tissue genomics and phenotyping, epigenomics and transgenomics, to exploit the genetic landscape for improving SST. Additionally, we explore the integration of HTP and AI, which demonstrates how these innovative approaches can optimize breeding efficiency and guide large-scale breeding efforts for designing salinity-smart crops to ensure sustainable agriculture and global food security. The collective adoption of these tools suggests bridging the gap between research and field application to deliver stress-smart varieties designed for saline-affected regions worldwide.

摘要

气候变化引发了许多非生物胁迫,包括土壤盐渍化,给全球农业带来了巨大挑战。耐盐胁迫(SST)在生理和遗传方面都是一个复杂的性状,并且在植物功能组织的各个层面上都有体现。由于农业生产系统的可持续性和盈利能力都严重依赖于SST,植物育种者正试图设计和培育能够在高盐条件下茁壮成长的耐盐智能作物。栽培作物、天然耐盐智能植物和作物野生近缘种的高质量参考基因组的可获取性,加速了关键基因和数量性状位点(QTL)的发现、标记开发、基因分型分析以及具有改良SST的分子育种产品的研发。采用快速育种工具,即基因组选择(GS)、单倍型育种(HBB)、人工智能(AI)和高通量表型分析(HTP),不仅对快速获得遗传增益有影响,而且还能减少培育具有增强SST和产量稳定性的商业品种的时间和成本。本综述讨论了各种基因组辅助育种(GAB)工具的进展和前景,包括基因组测序、QTL定位、全基因组关联研究(GWAS)、GS、HBB、泛基因组学、单细胞/组织基因组学和表型分析、表观基因组学和转基因学,以利用遗传图谱来提高SST。此外,我们还探讨了HTP和AI的整合,展示了这些创新方法如何优化育种效率,并指导大规模育种工作以设计耐盐智能作物,确保可持续农业和全球粮食安全。集体采用这些工具意味着弥合研究与田间应用之间的差距,以提供为全球受盐渍影响地区设计的耐胁迫品种。

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