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

IF 15.7 1区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Nature Communications Pub Date : 2025-04-03 DOI:10.1038/s41467-025-58081-1

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

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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.

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利用基于图谱的泛基因组进行 GWAS 元分析，提高水稻农艺性状的基因挖掘效率

全基因组关联研究受到群体结构和样本量的限制，影响了其有效性。虽然元分析减轻了这些问题，但它在水稻研究中的应用仍然有限。在这里，我们报告了一项大规模的荟萃分析，该荟萃分析了六个独立的水稻GWAS实验，以挖掘关键农艺性状的基因。通过整合水稻泛基因组图谱来鉴定结构变异，我们获得了6个群体（7765份）的6604,898个SNP和42,879个PAV变异。meta分析显著提高了数量性状位点（qtl）检出率和隐藏遗传率，分别提高了43%和37.88%。在6个农艺性状的156个qtl中，有116个qtl通过荟萃分析获得了全检结果，显示出较高的分辨率。通过CRISPR/Cas9对两个控制谷物宽度和长度的新qtl进行了功能验证，确认了它们的候选基因。我们的研究结果强调了这种基于泛基因组的meta-GWAS方法的实用性和潜在优势，为有效地从多种水稻种质中挖掘基因提供了一个可扩展的模型。

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来源期刊

Nature Communications Biological Science Disciplines-

CiteScore

24.90

自引率

2.40%

发文量

6928

审稿时长

3.7 months

期刊介绍： Nature Communications, an open-access journal, publishes high-quality research spanning all areas of the natural sciences. Papers featured in the journal showcase significant advances relevant to specialists in each respective field. With a 2-year impact factor of 16.6 (2022) and a median time of 8 days from submission to the first editorial decision, Nature Communications is committed to rapid dissemination of research findings. As a multidisciplinary journal, it welcomes contributions from biological, health, physical, chemical, Earth, social, mathematical, applied, and engineering sciences, aiming to highlight important breakthroughs within each domain.