大块分离分析
生物
克隆(编程)
计算生物学
选择(遗传算法)
特质
人口
吞吐量
生物技术
遗传学
计算机科学
基因
基因定位
人工智能
社会学
人口学
程序设计语言
无线
电信
染色体
作者
Xi Wang,Linqian Han,Juan Li,Xiaoyang Shang,Qian Liu,Lin Li,Hongwei Zhang
出处
期刊:Cell Reports
[Cell Press]
日期:2023-08-30
卷期号:42 (9): 113039-113039
被引量:13
标识
DOI:10.1016/j.celrep.2023.113039
摘要
Functional cloning and manipulation of genes controlling various agronomic traits are important for boosting crop production. Although bulked segregant analysis (BSA) is an efficient method for functional cloning, its low throughput cannot satisfy the current need for crop breeding and food security. Here, we review the rationale and development of conventional BSA and discuss its strengths and drawbacks. We then propose next-generation BSA (NG-BSA) integrating multiple cutting-edge technologies, including high-throughput phenotyping, biological big data, and the use of machine learning. NG-BSA increases the resolution of genetic mapping and throughput for cloning quantitative trait genes (QTGs) and optimizes candidate gene selection while providing a means to elucidate the interaction network of QTGs. The ability of NG-BSA to efficiently batch-clone QTGs makes it an important tool for dissecting molecular mechanisms underlying various traits, as well as for the improvement of Breeding 4.0 strategy, especially in targeted improvement and population improvement of crops.
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