Ren Hao, Lu Shan, Li Siting, Dong Qingfeng, Liu Dezheng, Ben-David Roi, Chen Liang, Hu Yin-Gang
State Key Laboratory for Crop Stress Resistance and High-Efficiency Production, College of Agronomy, Northwest A&F University, Yangling, Shaanxi, China.
Institute of Plant Sciences, Agricultural Research Organization-Volcani Center, Bet Dagan, Israel.
Theor Appl Genet. 2025 Jun 9;138(7):139. doi: 10.1007/s00122-025-04929-5.
A comprehensive GWAS reveals key QTLs, canditate genes, and networks underlying wheat lodging resistance. Lodging is a complex trait and has implications for wheat grain quality and yield, stem-associated traits are crucial for lodging resistance. Understanding the genetic basis of lodging-associated traits, especially single stem elasticity (SSE), stem strength (SS), and lodging index, is essential for developing effective strategies to enhance lodging resistance in wheat. In this study, 10 lodging-associated traits of 238 diverse wheat varieties were investigated across three diverse growing seasons. The ANOVA revealed significant variations on all traits among wheat genotypes and across three growing seasons. There was significant correlations between SSE, SS, three lodging indices (LI 1, LI 2, LI 3) and stem morphological traits. A total of 126 key candidate quantitative trait loci regions containing at least two marker-trait associations were identified using genome-wide association analysis. By integrating multiple analytical approaches, 84 key candidate genes were screened, including genes encoding enzymes related to stem cell wall synthesis, photosynthesis, hormone synthesis, and root development, which may play an important role in lodging resistance. By using Bayesian ridge regression for genome prediction, the prediction accuracy increased as the number of significant SNPs increased, and high prediction accuracy can also be achieved using only a few top-ranking SNPs. In summary, the results of this study would provide valuable insights for understanding lodging resistance in wheat and its genetic mechanism.
一项全面的全基因组关联研究揭示了小麦抗倒伏性的关键数量性状位点、候选基因和调控网络。倒伏是一个复杂的性状,对小麦籽粒品质和产量有影响,与茎相关的性状对倒伏抗性至关重要。了解倒伏相关性状的遗传基础,特别是单茎弹性(SSE)、茎强度(SS)和倒伏指数,对于制定提高小麦抗倒伏性的有效策略至关重要。在本研究中,在三个不同的生长季节对238个不同小麦品种的10个倒伏相关性状进行了调查。方差分析显示,小麦基因型之间以及三个生长季节的所有性状均存在显著差异。SSE、SS、三个倒伏指数(LI 1、LI 2、LI 3)与茎形态性状之间存在显著相关性。通过全基因组关联分析,共鉴定出126个关键候选数量性状位点区域,每个区域至少包含两个标记-性状关联。通过整合多种分析方法,筛选出84个关键候选基因,包括编码与茎细胞壁合成、光合作用、激素合成和根系发育相关酶的基因,这些基因可能在抗倒伏中发挥重要作用。通过使用贝叶斯岭回归进行基因组预测,预测准确性随着显著单核苷酸多态性(SNP)数量的增加而提高,仅使用少数排名靠前的SNP也可以实现较高的预测准确性。总之,本研究结果将为理解小麦抗倒伏性及其遗传机制提供有价值的见解。