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

Author

Listed:
  • Longbo Yang

    (Shanxi Agricultural University
    Chinese Academy of Agricultural Sciences)

  • Wenchuang He

    (Chinese Academy of Agricultural Sciences)

  • Yiwang Zhu

    (Chinese Academy of Agricultural Sciences)

  • Yang Lv

    (Chinese Academy of Agricultural Sciences)

  • Yilin Li

    (Shanxi Agricultural University
    Chinese Academy of Agricultural Sciences)

  • Qianqian Zhang

    (Shanxi Agricultural University
    Chinese Academy of Agricultural Sciences)

  • Yifan Liu

    (Shanxi Agricultural University
    Chinese Academy of Agricultural Sciences)

  • Zhiyuan Zhang

    (Shanxi Agricultural University
    Chinese Academy of Agricultural Sciences)

  • Tianyi Wang

    (Chinese Academy of Agricultural Sciences)

  • Hua Wei

    (Chinese Academy of Agricultural Sciences)

  • Xinglan Cao

    (Chinese Academy of Agricultural Sciences)

  • Yan Cui

    (Chinese Academy of Agricultural Sciences)

  • Bin Zhang

    (Chinese Academy of Agricultural Sciences)

  • Wu Chen

    (Chinese Academy of Agricultural Sciences)

  • Huiying He

    (Chinese Academy of Agricultural Sciences)

  • Xianmeng Wang

    (Chinese Academy of Agricultural Sciences)

  • Dandan Chen

    (Chinese Academy of Agricultural Sciences)

  • Congcong Liu

    (Chinese Academy of Agricultural Sciences)

  • Chuanlin Shi

    (Chinese Academy of Agricultural Sciences)

  • Xiangpei Liu

    (Chinese Academy of Agricultural Sciences)

  • Qiang Xu

    (Chinese Academy of Agricultural Sciences)

  • Qiaoling Yuan

    (Chinese Academy of Agricultural Sciences)

  • Xiaoman Yu

    (Chinese Academy of Agricultural Sciences)

  • Hongge Qian

    (Chinese Academy of Agricultural Sciences)

  • Xiaoxia Li

    (Chinese Academy of Agricultural Sciences)

  • Bintao Zhang

    (Chinese Academy of Agricultural Sciences)

  • Hong Zhang

    (Chinese Academy of Agricultural Sciences)

  • Yue Leng

    (Chinese Academy of Agricultural Sciences)

  • Zhipeng Zhang

    (Chinese Academy of Agricultural Sciences)

  • Xiaofan Dai

    (Chinese Academy of Agricultural Sciences)

  • Mingliang Guo

    (Chinese Academy of Agricultural Sciences)

  • Juqing Jia

    (Shanxi Agricultural University)

  • Qian Qian

    (Chinese Academy of Agricultural Sciences
    China National Rice Research Institute
    Yazhou District
    Chinese Academy of Agricultural Sciences)

  • Lianguang Shang

    (Chinese Academy of Agricultural Sciences
    Yazhou District
    Chinese Academy of Agricultural Sciences)

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.

Suggested Citation

  • 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 , 2025. "GWAS meta-analysis using a graph-based pan-genome enhanced gene mining efficiency for agronomic traits in rice," Nature Communications, Nature, vol. 16(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58081-1
    DOI: 10.1038/s41467-025-58081-1
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