数字农科院2.0

Next-generation bulked segregant analysis for Breeding 4.0

文献类型： 外文期刊

作者： Xi Wang;Linqian Han;Juan Li;Xiaoyang Shang;Qian Liu;Lin Li;Hongwei Zhang

作者机构：

关键词： biological big data;Breeding 4.0;CP: Genomics;CP: Plants;machine learning;next-generation bulked segregant analysis;quantitative trait genes

期刊名称： Cell Reports

ISSN： 2211-1247

年卷期： 2023 年 42 卷 9 期

页码：

收录情况： SCIE（2023版）

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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