Chong-Yuan Sun1, Yu-Ming Yang1, Lin Jia1, Xiao-Qian Liu1, Huan-Qing Xu1, Hai-Yan Lv1, Zhong-Wen Huang2, Dan Zhang3. 1. Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou, 450002, China. 2. School of Life Science and Technology, Henan Institute of Science and Technology/Henan Collaborative Innovation Center of Modern Biological Breeding, Xinxiang, 453003, China. 3. Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou, 450002, China. zhangd@henau.edu.cn.
Abstract
MAIN CONCLUSION: QTL mapping of stem diameter was carried out in three RIL populations using a high-density genetic map, and candidate genes related to stem diameter were predicted. Stem diameter is an important agronomic trait affecting soybean lodging and productivity. However, this trait is underexploited, and the underlying genetic mechanism in soybean remains unclear. In this study, three recombinant inbred line (RIL) populations, including 156 F10 lines from Nannong 94-156 × Bogao (N × B), 127 F9 lines from Dongnong 50 × Williams 82 (D × W), and 146 F9 lines from Suinong 14 × Enrei (S × E), were used to identify QTLs for soybean stem diameter across multiple environments. Phenotype analysis revealed that stem diameter exhibited strong positive correlations with plant height and 100-seed weight, two of the most important yield components. A total of 12 QTLs for stem diameter were identified on eight chromosomes across three RIL populations and five environments. The most influential QTL that was stably identified across all the populations and environments, q11, explained 12.58-26.63% of the phenotypic variation. Detection of several environment-specific QTLs, including q14, q16, and q20, suggests that environments may also have important effects in shaping the natural variation in soybean stem diameter. Furthermore, we predicted candidate genes underlying the QTLs and found that several promising candidate genes may be responsible for the variation in stem diameter in soybean. Overall, the markers/genes linked closely or underlying the major QTLs may be used for marker-assisted selection of soybean varieties to enhance lodging resistance and even yield. Our results lay the foundation for the fine mapping of stem development-related genes to reveal the molecular mechanisms.
MAIN CONCLUSION: QTL mapping of stem diameter was carried out in three RIL populations using a high-density genetic map, and candidate genes related to stem diameter were predicted. Stem diameter is an important agronomic trait affecting soybean lodging and productivity. However, this trait is underexploited, and the underlying genetic mechanism in soybean remains unclear. In this study, three recombinant inbred line (RIL) populations, including 156 F10 lines from Nannong 94-156 × Bogao (N × B), 127 F9 lines from Dongnong 50 × Williams 82 (D × W), and 146 F9 lines from Suinong 14 × Enrei (S × E), were used to identify QTLs for soybean stem diameter across multiple environments. Phenotype analysis revealed that stem diameter exhibited strong positive correlations with plant height and 100-seed weight, two of the most important yield components. A total of 12 QTLs for stem diameter were identified on eight chromosomes across three RIL populations and five environments. The most influential QTL that was stably identified across all the populations and environments, q11, explained 12.58-26.63% of the phenotypic variation. Detection of several environment-specific QTLs, including q14, q16, and q20, suggests that environments may also have important effects in shaping the natural variation in soybean stem diameter. Furthermore, we predicted candidate genes underlying the QTLs and found that several promising candidate genes may be responsible for the variation in stem diameter in soybean. Overall, the markers/genes linked closely or underlying the major QTLs may be used for marker-assisted selection of soybean varieties to enhance lodging resistance and even yield. Our results lay the foundation for the fine mapping of stem development-related genes to reveal the molecular mechanisms.