Deming  Yu , Qishan Chen

Modern Agricultural Research Center, Cuixi Academy of Biotechnology, Zhuji, 311800, Zhejiang, China
Author    Correspondence author
Legume Genomics and Genetics, 2025, Vol. 16, No. 4   
Received: 20 May, 2025    Accepted: 05 Jul., 2025    Published: 20 Jul., 2025

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Common bean (Phaseolus vulgaris L.) is a cornerstone protein and micronutrient source worldwide, yet improving its multifaceted seed quality traits has outpaced the capabilities of conventional breeding; to address this gap, we synthesize advances from integrating transcriptome and metabolome datasets for trait dissection. We first delineate nutritional attributes-protein, starch, and micronutrients-alongside anti-nutritional and functional metabolites such as tannins, phytates, polyphenols, and processing-related traits including cooking time, texture, flavor; we then review gene-expression dynamics during seed maturation, regulatory networks controlling nutrient deposition, and key transcription factors, in parallel with metabolomic profiles of primary and secondary metabolites and their environmental modulation; subsequently, we detail integrative strategies that correlate expression patterns with metabolite accumulation, employ network and pathway-level models, and nominate candidate biomarkers linked to quality; a comparative case study synthesizes landmark multi-omics investigations, highlighting recurrent pathways-cell-wall remodeling, amino-acid biosynthesis, phenylpropanoid metabolism-while distilling methodological lessons on sampling windows, normalization and batch control, and integration models that bolster interpretability; finally, we examine technical hurdles in data variability and metabolite identification, biological complexities from genotype × environment interactions, and opportunities for machine learning-driven predictive modeling. In summary, integrative omics has matured into a practical toolkit for prioritizing biomarkers and causal pathways that can accelerate marker-assisted and genomic selection; we anticipate near-term translation of multi-omics signals into breeding pipelines via robust, ML-enabled prediction and decision support, and a shift toward pan-omics and precision breeding to sustainably elevate common-bean seed quality under diverse environments.

Common bean; Seed quality; Transcriptome-metabolome integration; Biomarkers; Precision breeding