Yang Longbo, He Wenchuang, Zhu Yiwang, Lv Yang, Li Yilin, Zhang Qianqian, Liu Yifan, Zhang Zhiyuan, Wang Tianyi, Wei Hua, Cao Xinglan, Cui Yan, Zhang Bin, Chen Wu, He Huiying, Wang Xianmeng, Chen Dandan, Liu Congcong, Shi Chuanlin, Liu Xiangpei, Xu Qiang, Yuan Qiaoling, Yu Xiaoman, Qian Hongge, Li Xiaoxia, Zhang Bintao, Zhang Hong, Leng Yue, Zhang Zhipeng, Dai Xiaofan, Guo Mingliang, Jia Juqing, Qian Qian, Shang Lianguang
College of Agriculture, Shanxi Agricultural University, Shanxi, 030801, China.
Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.
Nat Commun. 2025 Apr 3;16(1):3171. doi: 10.1038/s41467-025-58081-1.
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.
全基因组关联研究（GWAS）受到群体结构和样本量的限制，影响了其功效。尽管荟萃分析可以缓解这些问题，但其在水稻研究中的应用仍然有限。在此，我们报告了对水稻六个独立GWAS实验的大规模荟萃分析，以挖掘关键农艺性状的基因。通过整合水稻泛基因组图谱来识别结构变异，我们为六个样本组（7765份种质）获得了6,604,898个单核苷酸多态性（SNP）和42,879个结构变异（PAV）。荟萃分析显著提高了数量性状位点（QTL）的检测能力和隐性遗传力，分别提高了43%和37.88%。在为六个农艺性状鉴定出的156个QTL中，有116个是通过荟萃分析单独检测到的，突出了其更高的分辨率。通过CRISPR/Cas9对控制粒宽和粒长的两个新QTL进行了功能验证，确认了它们的候选基因。我们的研究结果强调了这种基于泛基因组的荟萃GWAS方法的实用性和潜在优势，为从不同水稻种质中高效挖掘基因提供了一个可扩展的模型。
