Abstract

Genetic dissection of agronomic traits is important for crop improvement and global food security. Phenotypic variation of tassel branch number (TBN), a major breeding target, is controlled by many quantitative trait loci (QTLs). The lack of large-scale QTL cloning methodology constrains the systematic dissection of TBN, which hinders modern maize breeding. Here, we devise QTG-Miner, a multi-omics data-based technique for large-scale and rapid cloning of quantitative trait genes (QTGs) in maize. Using QTG-Miner, we clone and verify seven genes underlying seven TBN QTLs. Compared to conventional methods, QTG-Miner performs well for both major- and minor-effect TBN QTLs. Selection analysis indicates that a substantial number of genes and network modules have been subjected to selection during maize improvement. Selection signatures are significantly enriched in multiple biological pathways between female heterotic groups and male heterotic groups. In summary, QTG-Miner provides a large-scale approach for rapid cloning of QTGs in crops and dissects the genetic base of TBN for further maize breeding.

The lack of large-scale QTL cloning method hampers systematic dissection of genetic base of quantitative traits. Here, the authors develop a multi-omics data-based technique for large-scale and rapid cloning of quantitative genes of tassel branch number and discovery of selection signatures in maize breeding.

Details

Title
QTG-Miner aids rapid dissection of the genetic base of tassel branch number in maize
Author
Wang, Xi 1   VIAFID ORCID Logo  ; Li, Juan 1 ; Han, Linqian 1 ; Liang, Chengyong 1 ; Li, Jiaxin 1 ; Shang, Xiaoyang 1 ; Miao, Xinxin 1 ; Luo, Zi 1 ; Zhu, Wanchao 1 ; Li, Zhao 1 ; Li, Tianhuan 1 ; Qi, Yongwen 2 ; Li, Huihui 3   VIAFID ORCID Logo  ; Lu, Xiaoduo 4 ; Li, Lin 1   VIAFID ORCID Logo 

 Huazhong Agricultural University, National Key Laboratory of Crop Genetic Improvement, Wuhan, China (GRID:grid.35155.37) (ISNI:0000 0004 1790 4137); Hubei Hongshan Laboratory, Wuhan, China (GRID:grid.35155.37) 
 Zhongkai University of Agriculture and Engineering, College of Agriculture and Biology, Guangzhou, China (GRID:grid.449900.0) (ISNI:0000 0004 1790 4030) 
 Chinese Academy of Agricultural Sciences, Institute of Crop Science, Beijing, China (GRID:grid.410727.7) (ISNI:0000 0001 0526 1937) 
 Qilu Normal University, Institute of Molecular Breeding for Maize, Jinan, China (GRID:grid.488158.8) (ISNI:0000 0004 1765 9725) 
Pages
5232
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2857484757
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.