ArticleGenome biology2025
Integrative phenomics, metabolomics and genomics analysis provides new insights for deciphering the genetic basis of metabolism in polished rice.
Article in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Early detection and genetic insights into salt tolerance in rice by linking physiology and spectral reflectance during seed germination.Plant phenomics (Washington, D.C.) · 2026Article
- Genome-phenome association prediction using weighted deep matrix factorization with a multisource graph attention network.Quantitative biology (Beijing, China) · 2026Article
- ELMERF: A deep-learning-assisted hydroponic RGB phenotyping framework for rice seedling salt-stress evaluation and genetic mapping.Plant phenomics (Washington, D.C.) · 2026Article
- Article
- Integrating AI in seed science: Toward an intelligent design paradigm.Plant communications · 2026Review
- Integrated Analysis of Genetic, Spectral, Phenotypic, and Stress-Resistant Traits inPlants (Basel, Switzerland) · 2026Article
- Hyperspectral imaging of grains uncovers the genetic architecture of nitrogen response of development in bread wheat.Molecular breeding : new strategies in plant improvement · 2025Article
- Integrative phenomics, metabolomics and genomics analysis provides new insights for deciphering the genetic basis of metabolism in polished rice.Genome biology · 2025Article
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13 authors.
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Abstract
backgroundMetabolomics is one of the most widely used omics tools for deciphering the functional networks of the metabolites for crop improvement. However, it is technically demanding and costly.
resultsWe propose a relatively inexpensive approach for metabolomics analysis in which metabolomics is combined with hyperspectral imaging via machine learning. This approach can be used to target important steps in flavonoid and lipid biosynthesis in rice. We extract 1848 hyperspectral indices and 887 metabolites from polished grains of 533 Oryza sativa accessions. Hyperspectral indices are then linked to metabolites through correlation analysis and modelling. Based on this, a total of 554 metabolites and 1313 hyperspectral indices are identified for further genome-wide association study (GWAS). By GWAS, we detect 17,509 significant locus-trait associations with 2882 single nucleotide polymorphisms (SNPs). Colocalization analysis links these SNPs to the corresponding metabolites and hyperspectral indices. We detect 6415 pairs of metabolites and hyperspectral indices within a linkage disequilibrium of 300 kb in the Oryza sativa genome. We then characterize 1761 candidate genes colocalized to these loci by transcriptomic analysis. We further verify novel candidate genes encoding a novel flavonoid (LOC_Os09g18450) and a flavonoid/lipid (LOC_Os07g11020) respectively by gene editing and overexpression in rice.
conclusionOur findings indicate that hyperspectral imaging combined with machine learning methods could serve as a powerful tool for quickly and inexpensively assessing crop metabolites.
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