ArticleBiotechnology for biofuels and bioproducts2022
4D genetic networks reveal the genetic basis of metabolites and seed oil-related traits in 398 soybean RILs.
Article in Biotechnology for biofuels and bioproducts, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Multi-omics revolution to promote plant breeding efficiency.Frontiers in plant science · 2022Pooled it
- Oil trait and multi-omic analyses reveal the regulatory network of triacylglycerol and fatty acid accumulation in hexaploid Camellia oleifera across different harvesting stages.BMC plant biology · 2025Article
- Genetic and transcriptome analyses of the effect of genotype-by-environment interactions on Brassica napus seed oil content.The Plant cell · 2025Article
- Quality traits drive the enrichment of Massilia in the rhizosphere to improve soybean oil content.Microbiome · 2024Article
- Article
- Lipid Metabolism and Improvement in Oilseed Crops: Recent Advances in Multi-Omics Studies.Metabolites · 2023Review
- Determination of superior Pistacia chinensis accession with high-quality seed oil and biodiesel production and revelation of LEC1/WRI1-mediated high oil accumulative mechanism for better developing woody biodiesel.BMC plant biology · 2023Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundThe yield and quality of soybean oil are determined by seed oil-related traits, and metabolites/lipids act as bridges between genes and traits. Although there are many studies on the mode of inheritance of metabolites or traits, studies on multi-dimensional genetic network (MDGN) are limited.
resultsIn this study, six seed oil-related traits, 59 metabolites, and 107 lipids in 398 recombinant inbred lines, along with their candidate genes and miRNAs, were used to construct an MDGN in soybean. Around 175 quantitative trait loci (QTLs), 36 QTL-by-environment interactions, and 302 metabolic QTL clusters, 70 and 181 candidate genes, including 46 and 70 known homologs, were previously reported to be associated with the traits and metabolites, respectively. Gene regulatory networks were constructed using co-expression, protein-protein interaction, and transcription factor binding site and miRNA target predictions between candidate genes and 26 key miRNAs. Using modern statistical methods, 463 metabolite-lipid, 62 trait-metabolite, and 89 trait-lipid associations were found to be significant. Integrating these associations into the above networks, an MDGN was constructed, and 128 sub-networks were extracted. Among these sub-networks, the gene-trait or gene-metabolite relationships in 38 sub-networks were in agreement with previous studies, e.g., oleic acid (trait)-GmSEI-GmDGAT1a-triacylglycerol (16:0/18:2/18:3), gene and metabolite in each of 64 sub-networks were predicted to be in the same pathway, e.g., oleic acid (trait)-GmPHS-D-glucose, and others were new, e.g., triacylglycerol (16:0/18:1/18:2)-GmbZIP123-GmHD-ZIPIII-10-miR166s-oil content.
conclusionsThis study showed the advantages of MGDN in dissecting the genetic relationships between complex traits and metabolites. Using sub-networks in MGDN, 3D genetic sub-networks including pyruvate/threonine/citric acid revealed genetic relationships between carbohydrates, oil, and protein content, and 4D genetic sub-networks including PLDs revealed the relationships between oil-related traits and phospholipid metabolism likely influenced by the environment. This study will be helpful in soybean quality improvement and molecular biological research.
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