数字农科院2.0

GWAS meta-analysis using a graph-based pan-genome enhanced gene mining efficiency for agronomic traits in rice

文献类型： 外文期刊

作者： 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

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期刊名称： NATURE COMMUNICATIONS

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年卷期： 2025 年 16 卷 1 期

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收录情况： SCIE（2025版）

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.

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