METHODS article
Front. Bioinform.
Sec. Genomic Analysis
Volume 5 - 2025 | doi: 10.3389/fbinf.2025.1607119
Enhancing Genomic Prediction in Arabidopsis thaliana with Optimized SNP Subset by Leveraging Gene Ontology Priors and Bin-based Combinatorial Optimization
Provisionally accepted
- 1湖南农业大学植物保护学院, 湖南长沙, China
- 2Plant protection, Hunan Agricultural University, changsha, China
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With the rapid development of high-density molecular marker chips and high-throughput sequencing technologies, genomic selection/prediction (GS/GP) has been widely applied in plant breeding. Arabidopsis thaliana, as a common model organism, provides important resources for dissecting genetic variation and evolutionary mechanisms of complex traits. Quantitative traits are typically influenced by multiple minor-effect genes, which are often functionally related and can be enriched within gene ontology (GO) pathways. However, optimizing marker subsets associated with these pathways to enhance GP performance remains challenging. In this study, we propose an improved GS framework called binGO-GS by integrating GO-based biological priors with a novel bin-based combinatorial SNP subset selection strategy. We evaluated the performance of binGO-GS on nine quantitative traits from two A. thaliana datasets, comprising nearly 1,000 samples and over 1.8 million SNPs. Compared with using either the full marker set or randomly selected markers with Genomic BLUP (GBLUP), binGO-GS achieved statistically significant improvements in prediction accuracy across all traits. Similar improvements were observed across six additional regression models when applying binGO-GS instead of the full marker set. Furthermore, the selected markers for identical or similar morphological traits exhibited consistent patterns in quantity and genomic distribution, supporting the polygenic model of complex quantitative traits driven by minor-effect genes. Taken together, binGO-GS offers a powerful and interpretable approach to enhance GS performance, providing a methodological reference for accelerating plant breeding and germplasm innovation.
Keywords: genomic selection/prediction, SNP, Subset Selection, gene ontology, biological priors, Arabidopsis thaliana
Received: 08 Apr 2025; Accepted: 05 Jun 2025.
Copyright: © 2025 巴, dai, yuan and zhou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: zhijun dai, 湖南农业大学植物保护学院, 湖南长沙, China
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