SynthesisNature communications2025
GWAS meta-analysis using a graph-based pan-genome enhanced gene mining efficiency for agronomic traits in rice.
Abstract readMeta-Analysis
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20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- eQTL Meta-Analysis Reveals Conserved and Population-Specific Regulatory Variation Underlying Nutritional Trait Evolution and Domestication in Tomato.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Pooled it
- Review
- Genomic Insights into the Genetic Diversity, Selection Signals, and Agronomic Traits of Rice (Plants (Basel, Switzerland) · 2026Article
- A Restricted Two-Stage Multi-Locus Multi-Allele Genome-Wide Association Study Reveals Genomic Loci and Candidate Genes Controlling Plant-Height-Related Traits in Soybean Under Normal and Shade Conditions.International journal of molecular sciences · 2026Article
- Challenges in Bringing Pangenome Research Into Breeding: A Case Study in Rice.Plant biotechnology journal · 2026Review
- Genome-wide association studies in horticultural crops: decoding genetic diversity for precision breeding.Horticulture research · 2026Article
- Integrative multi-omics analysis reveals the domestication mechanism of black rice.National science review · 2026Article
- Genome-Wide Association Study and Candidate Gene Identification for Resistance to Bacterial Stem and Root Rot in Sweetpotato.Biology · 2026Article
- Assembling unmapped reads reveals hidden variation in South Asian genomes.Nature communications · 2026Article
- Article
- The pangenome enhances the understanding of the genetic diversity of papaya.Horticulture research · 2026Article
- Dynamics of genome evolution in the era of pangenome analysis.Cell genomics · 2026Review
- Comprehensive identification ofFrontiers in plant science · 2026Article
- Identification of QTLs and genes regulating panicle number through genome-wide association studies inFrontiers in plant science · 2026Article
- Genome-Wide Association Study Using High-Density SNP Chip Markers Revealed Novel Bacterial Panicle Blight (Burkholderia glumae) Resistance Genes in Rice.The plant pathology journal · 2025Article
- Next-generation translational genomics for developing future crops.Functional & integrative genomics · 2025Review
- Biotic and Abiotic Factors Influencing Maize Plant Height.International journal of molecular sciences · 2025Review
- Integrated QTL mapping and candidate gene analysis for yield-related traits and salt tolerance in a rice RIL population.Frontiers in plant science · 2025Article
- From map to blueprint: the plant pan-genome unraveling genetic mysteries and powering precision breeding.Frontiers in plant science · 2025Review
- From data to decisions: a paradigm shift in fruit agriculture through the integration of multi-omics, modern phenotyping, and cutting-edge bioinformatic tools.Frontiers in plant science · 2025Review
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Authors and funding
34 authors.
Longbo Yang *College of Agriculture, Shanxi Agricultural University, Shanxi, 030801, China.ORCID http://orcid.org/0009-0001-2247-5627
Wenchuang He *Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.ORCID http://orcid.org/0000-0001-5568-7215
Yiwang Zhu *Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.
Yang Lv *Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.
Yilin LiCollege of Agriculture, Shanxi Agricultural University, Shanxi, 030801, China.
Qianqian ZhangCollege of Agriculture, Shanxi Agricultural University, Shanxi, 030801, China.
Yifan LiuCollege of Agriculture, Shanxi Agricultural University, Shanxi, 030801, China.
Zhiyuan ZhangCollege of Agriculture, Shanxi Agricultural University, Shanxi, 030801, China.
Tianyi WangShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.
Hua WeiShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.
Xinglan CaoShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.
Yan CuiShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.
Bin ZhangShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.
Wu ChenShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.
Huiying HeShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.
Xianmeng WangShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.
Dandan ChenShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.
Congcong LiuShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.
Chuanlin ShiShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.ORCID http://orcid.org/0000-0003-4776-1942
Xiangpei LiuShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.ORCID http://orcid.org/0000-0002-3567-1782
Qiang XuShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.ORCID http://orcid.org/0000-0002-9307-0504
Qiaoling YuanShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.
Xiaoman YuShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.
Hongge QianShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.
Xiaoxia LiShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.
Bintao ZhangShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.
Hong ZhangShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.
Yue LengShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.
Zhipeng ZhangShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.
Xiaofan DaiShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.
Mingliang GuoShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.
Juqing JiaCollege of Agriculture, Shanxi Agricultural University, Shanxi, 030801, China.ORCID http://orcid.org/0000-0001-6312-7912
Qian QianShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China. qianqian188@hotmail.com.ORCID http://orcid.org/0000-0002-0349-4937
Lianguang ShangShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China. shanglianguang@cass.cn.ORCID http://orcid.org/0000-0002-4606-2800
Funding
National Natural Science Foundation of China (National Science Foundation of China) 32188102 and 32372148
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.
Indexed as
Genome, PlantGenome-Wide Association StudyOryzaQuantitative Trait LociData MiningPhenotypePolymorphism, Single Nucleotide
Identifiers
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