Next-generation bulked segregant analysis for Breeding 4.0

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

doi:10.1016/j.celrep.2023.113039

Next-generation bulked segregant analysis for Breeding 4.0

Cell Rep. 2023 Sep 26;42(9):113039. doi: 10.1016/j.celrep.2023.113039. Epub 2023 Aug 30.

Authors

Xi Wang¹, Linqian Han¹, Juan Li¹, Xiaoyang Shang¹, Qian Liu², Lin Li³, Hongwei Zhang⁴

Affiliations

¹ National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China.
² Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
³ National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China. Electronic address: hzaulilin@mail.hzau.edu.cn.
⁴ Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China. Electronic address: zhanghongwei@caas.cn.

PMID: 37651230
DOI: 10.1016/j.celrep.2023.113039

Abstract

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.

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

Publication types

Review