文献类型： 外文期刊

作者： Yang, Longbo ¹ ; He, Wenchuang ² ; Zhu, Yiwang ² ; Lv, Yang ² ; Li, Yilin ¹ ; Zhang, Qianqian ¹ ; Liu, Yifan ¹ ; Zhang, Zhiyuan ¹ ; Wang, Tianyi ² ; Wei, Hua ² ; Cao, Xinglan ² ; Cui, Yan ² ; Zhang, Bin ² ; Chen, Wu ² ; He, Huiying ² ; Wang, Xianmeng ² ; Chen, Dandan ² ; Liu, Congcong ² ; Shi, Chuanlin ² ; Liu, Xiangpei ² ; Xu, Qiang ² ; Yuan, Qiaoling ² ; Yu, Xiaoman ² ; Qian, Hongge ² ; Li, Xiaoxia ² ; Zhang, Bintao ² ; Zhang, Hong ² ; Leng, Yue ² ; Zhang, Zhipeng ² ; Dai, Xiaofan ² ; Guo, Mingliang ² ; Jia, Juqing ¹ ; Qian, Qian ² ; Shang, Lianguang ² ;

作者机构： 1.Shanxi Agr Univ, Coll Agr, Jinzhong 030801, Shanxi, Peoples R China

2.Chinese Acad Agr Sci, Agr Genom Inst Shenzhen, Minist Agr & Rural Affairs, Genome Anal Lab,Guangdong Lab Lingnan Modern Agr,S, Shenzhen 518120, Peoples R China

3.China Natl Rice Res Inst, State Key Lab Rice Biol, Hangzhou 310006, Peoples R China

4.Yazhouwan Lab, 8 Huanjin Rd, Sanya 572024, Hainan, Peoples R China

5.Chinese Acad Agr Sci, Natl Nanfan Res Inst Sanya, Academician Workstat, Sanya 572024, Peoples R China

期刊名称：NATURE COMMUNICATIONS （ 影响因子：15.7； 五年影响因子：17.2 ）

ISSN：

年卷期： 2025 年 16 卷 1 期

页码：

收录情况： SCI

摘要： Genome-wide association studies (GWASs) encounter limitations from population structure and sample size, restricting their efficacy. Though meta-analysis mitigates these issues, its application in rice research remains limited. Here, we report a large-scale meta-analysis of six independent GWAS experiments in rice to mine genes for key agronomic traits. By integrating a rice pan-genome graph to identify structural variants, we obtained 6,604,898 SNP and 42,879 PAV variants for the six panels (7765 accessions). Meta-analysis significantly improved quantitative trait loci (QTLs) detection and hidden heritability by up to 43 and 37.88%, respectively. Among 156 QTLs identified for six agronomic traits, 116 were exclusively detected through meta-analysis, highlighting its superior resolution. Two novel QTLs governing grain width and length were functionally validated through CRISPR/Cas9, confirming their candidate genes. Our findings underscore the utility and potential advantages of this pan-genome-based meta-GWAS approach, providing a scalable model for efficiently gene mining from diverse rice germplasms.