选择(遗传算法)
分子育种
生物技术
人口
生物
回交
植物育种
计算机科学
遗传学
基因
农学
人工智能
社会学
人口学
作者
Zhong Bian,Dong-Ping Cao,Wenshu Zhuang,Shuwei Zhang,Qiaoquan Liu,Lin Zhang
出处
期刊:PubMed
日期:2023-09-20
卷期号:45 (9): 718-740
标识
DOI:10.16288/j.yczz.23-092
摘要
As one of the major staple crops, rice feeds more than one half of the world population. Due to increasing population and dramatic climate change, the rice varieties with higher yield performance and excellent overall agronomic performance should be developed. The raise of molecular design breeding concept provides opportunity to get new breakthrough for variety development, and it is important to clarify the efficient gene combination during actual breeding. In this review, we summarize the recent advances about rice variety improvement either by marker assisted selection (MAS) breeding or popular gene editing technique, which will be beneficial to understand different aspects of the molecular design breeding. We provide genetic views for the classical MAS application, including the genetic effect of key genes and their combinations, the recurrent genome recovery rate at different backcross generations, linkage drag and recombination selection. Moreover, we compare the breeding value of recently-developed molecular techniques, including the advantage of high-throughput genotyping and the way and effect of gene editing in creating useful traits. Considering the current status and actual demands of rice breeding, we raise the strategy to take advantages of both traditional breeding resources and popular molecular techniques, which might pave the way to optimize the process of molecular design breeding in future.水稻是世界上最重要的粮食作物之一,养育了全球超过1/2的人口。随着人口增加及气候条件变化,当前对水稻品种产量及其他综合性状表现提出了更高要求。分子设计育种概念的提出为快速突破现有品种的局限提供了契机,然而如何在育种实践中有效实现不同基因的组合利用是需要关注的重点。本文结合近年来水稻分子标记辅助选择(maker assisted selection,MAS)育种及重要性状基因编辑育种研究进展,对分子设计育种可能涉及的不同方面进行了归纳总结,既包括关键育种基因及其组合的遗传效应、不同回交世代遗传背景恢复特点、连锁累赘及重组筛选等经典MAS遗传规律总结,也涵盖高通量基因分型技术利用、基因编辑创制有利性状变异的实现途径及其效果等前沿技术应用评价。最后,结合当前水稻育种现状及实际需求,对传统育种资源及现代分子技术的综合利用策略进行了展望,以期为今后进一步优化分子设计育种流程提供思路。.
科研通智能强力驱动
Strongly Powered by AbleSci AI