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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月3日

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  • Longbo Yang,
  • Wenchuang He,
  • Yiwang Zhu,
  • Yang Lv,
  • Yilin Li,
  • Qianqian Zhang,
  • Yifan Liu,
  • Zhiyuan Zhang,
  • Tianyi Wang,
  • Hua Wei,
  • Xinglan Cao,
  • Yan Cui,
  • Bin Zhang,
  • Wu Chen,
  • 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

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.