GWAS meta-analysis using a graph-based pan-genome enhanced gene mining efficiency for agronomic traits in rice
Longbo Yang, Wenchuang He, Yiwang Zhu, Yang Lv, Yilin Li, Qianqian Zhang, Yifan Liu, Zhiyuan Zhang, Tianyi Wang, Wei Hua, Xinglan Cao, Yan Cui, Bin Zhang, Chen Wu, Huiying He, Xianmeng Wang, Dandan Chen, Congcong Liu, Chuanlin Shi, Xiangpei Liu, Qiang Xu, Qiaoling Yuan, Xiaoman Yu, Hongge Qian, Xiaoxia Li, Bintao Zhang, Hong Zhang, Yue Leng, Zhipeng Zhang, Xiaofan Dai, Mingliang Guo, Juqing Jia, Qian Qian, Lianguang Shang
Abstract
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. Here, the authors perform a pan-genome-based genome-wide meta-analysis of six independent GWAS experiments in rice, demonstrating the potential of this approach to increase understanding of the genetic basis of, and genes underlying key rice traits.