生物技术
基因组学
新兴技术
钥匙(锁)
基因组编辑
生物
植物育种
分子育种
作物
基因组
非生物胁迫
可持续农业
农作物产量
生产(经济)
绿色革命
农林复合经营
农业
遗传资源
再生
商业化
计算机科学
持续性
多倍体
诱变育种
作者
Qian Du,Liang Nie,Ya-Nan Kou,Shu‐pei Hou,Li‐Yu Chen
摘要
Sugarcane serves as one of the world's most crucial economic crops, with fundamental importance to global sugar production and the sustainable biofuel industry. To meet the rising global demands and tackle various biotic and abiotic stresses, there is an urgent necessity to enhance the sugarcane breeding programme. Deciphering the intricate sugarcane genomes and integrating valuable agronomic traits into elite cultivars are vital to sustainable crop improvement. We review the major challenges in genomics-enabled sugarcane improvement, including genome complexity, polyploidy, and the limitation of traditional breeding methods. We discuss recent advancements in sugarcane genomic research, with a particular focus on cutting-edge strategies and algorithms for polyploid genome assembly, the key features and characteristics of newly assembled sugarcane genomes, and the development of graph-based pangenomes to capture genomic diversity. Our review highlights genomics-based sugarcane breeding from three perspectives: the history of conventional breeding, advanced breeding strategies empowered by genomic resources, and the emerging era of big data-driven crop improvement. These advancements provide powerful tools to accelerate sugarcane breeding and genetic improvement. Over the past decades, sugarcane genomics has made significant progress, including the assembly of high-quality reference genomes, the generation of comprehensive multi-omics datasets, and the development of precision genetic engineering tools. These advancements enable targeted manipulation of key alleles to improve yield, stress tolerance, and other important agronomic traits. Finally, this review summarises key breakthroughs in sugarcane gene-editing technologies and offers future perspectives on how these innovations are reshaping crop enhancement strategies.
科研通智能强力驱动
Strongly Powered by AbleSci AI