Bioscience Evidence 2025, Vol.15, No.5, 237-248 http://bioscipublisher.com/index.php/be 243 domestication of soybeans. Its different alleles are significantly related to the increase of oil content, the decrease of protein and the enlargement of grains. Therefore, it has become a key research object for functional verification and breeding (Wang et al., 2020). 7.1.2 Functional characterization: knockout/overexpression studies, phenotypic changes Researchers used near-isogenic lines and genetically modified soybeans for verification. The results show that the superior alleles of GmSWEET10a can increase the oil content and grain weight of grains, but will reduce the protein content. It and the homologous gene GmSWEET10b are mainly responsible for transporting sucrose and hexose from the seed coat to the endosperm, regulating carbon source allocation, and thereby affecting lipid and protein accumulation (Wang et al., 2020). When overexpressed, the oil content of the grains was higher than that of the control. After knockout, the oil content decreased and the protein content increased. 7.1.3 Field validation: how findings translated into yield and composition shifts In field trials, the oil content and yield of materials with superior alleles increased simultaneously, while the protein content slightly decreased. This effect is relatively stable in different environments and genetic backgrounds, indicating that it has application potential in production (Wang et al., 2020). 7.1.4 Breeding application: integration into elite lines At present, the superior allele of GmSWEET10a has been introduced into modern major soybean varieties and has been used as a key target for MAS and gene editing. Meanwhile, its homologous gene GmSWEET10b has also begun to receive attention. In the future, by aggregating these superior genes, the oil content and yield of soybeans may be further increased (Wang et al., 2020). 7.2 Discussion: lessons learned—bridging functional genomics with practical breeding The research case of GmSWEET10a shows that combining population genetics, transgenic or gene editing and field trials can achieve an effective transformation from gene discovery to breeding application. It not only explains the contradiction between protein and oil, but also provides a reference for the simultaneous improvement of multiple traits. More multi-omics data and precise editing methods are needed in the future to accelerate the application of functional genomics achievements in molecular breeding (Wang et al., 2020; Zhang et al., 2021; Kumar et al., 2022). 8. Future Directions 8.1 Integrative approaches: multi-omics, machine learning, and predictive breeding In the future, the improvement of soy protein and oil content will rely more on the integration of multi-omics data (genomic, transcriptomic, proteomic, metabolomic). By integrating machine learning and big data analysis, complex traits can be predicted more accurately, enabling design breeding. Multi-omics studies have revealed the pathways of protein and oil accumulation, key genes and their interactions, which provide theoretical support for resolving the protein-oil contradiction (Kumar et al., 2021; Xu et al., 2022). Technologies such as machine learning and remote sensing have been used to efficiently predict protein and oil content in the field, improving the efficiency of breeding and field management (Hernandez et al., 2023). In the future, the combination of deep learning, data fusion and environmental factors will further enhance the accuracy of prediction and the ability of breeding decision-making (Patil et al., 2017). 8.2 Climate resilience: protein and oil traits under stress conditions. Climate change poses new challenges to the protein and oil content of soybeans. Studies have shown that under future climatic conditions, oil content may increase with the increase in production, but protein content may decrease (Araji et al., 2020). The responses of different genotypes to stresses such as high temperature and drought are significantly different, which indicates that it is very important to screen and breed varieties with strong climate adaptability and stable quality (Araji et al., 2020; Duan et al., 2023). Multi-omics and QTL mapping can help identify genes related to stress response, providing a molecular basis for breeding new varieties with high protein, high oil content and stress resistance (Kumar et al., 2021; Xu et al., 2022; Duan et al., 2023).
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