NodeGWAS: Leveraging Graph Pangenomes for Sensitive and Accurate Association Analysis in Diverse Diploid and Polyploid Species
This study introduces NodeGWAS, a graph pangenome-based framework that performs association analysis using graph nodes. Compared with conventional GWAS approaches, NodeGWAS strikes a balance between sensitivity and interpretability, and its applications in Arabidopsis thaliana and sugarcane demonstrate its effectiveness in dissecting complex traits.
Authors
- Baiyu Wang (ORCID: https://orcid.org/0000-0003-4123-3815)
- Taihua Wu (ORCID: https://orcid.org/0000-0001-9544-818X)
- Haibao Tang (ORCID: https://orcid.org/0000-0002-3460-8570)
- Yumin Huang (ORCID: https://orcid.org/0000-0002-6163-6286)
- Yixing Zhang (ORCID: https://orcid.org/0000-0001-8780-4324)
- Yiying Qi
- Yuheng Wang
- Yue Tan
- Yuhao Wang
- Qing Zhang
- Yan Lin
- Jisen Zhang
- Zhengguang Wang
Institutions
- Guangxi University (CN)
- Fujian Agriculture and Forestry University (CN)
Publication Details
- Journal
- Plant Communications
- Published
- 2026-03-01
- DOI
- https://doi.org/10.1016/j.xplc.2026.101835
- Primary Topic
- Genomics and Rare Diseases
- Type
- article
- Field-Weighted Citation Impact
- 0.00