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, 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 () and Lianguang Shang ()
Additional contact information
Longbo Yang: Shanxi Agricultural University
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
Qianqian Zhang: Shanxi Agricultural University
Yifan Liu: Shanxi Agricultural University
Zhiyuan Zhang: Shanxi Agricultural University
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
Lianguang Shang: Chinese Academy of Agricultural Sciences

Nature Communications, 2025, vol. 16, issue 1, 1-9

Abstract: 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.

Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-58081-1 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58081-1

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-58081-1

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().