Legume Genomics and Genetics 2024, Vol.15, No.3, 126-139 http://cropscipublisher.com/index.php/lgg 132 4.4 Nutritional and industrial trait improvement Improving the nutritional and industrial traits of soybeans is essential for meeting the increasing global demand for high-quality food and feed. Genomic tools have been instrumental in enhancing traits such as oil content, protein quality, and fatty acid composition. For instance, genome-wide association studies (GWAS) have identified SNPs linked to high oil and protein content, facilitating the development of soybean varieties with enhanced nutritional profiles. Advances in omics technologies, including genomics, transcriptomics, and metabolomics, have provided comprehensive insights into the genetic basis of these traits. The integration of these technologies has enabled the identification of key regulatory genes and metabolic pathways involved in seed composition. For example, the GmFBL144 gene has been identified to interact with small heat shock proteins, playing a role in regulating drought stress tolerance and potentially influencing seed composition (Xu et al., 2022). CRISPR/Cas9 has also been employed to create specific mutations that enhance oil quality and protein content in soybeans, demonstrating the potential of gene editing in nutritional improvement (Nagamine and Ezura, 2022). Additionally, high-throughput genotyping and phenotyping platforms have improved the precision and efficiency of selecting for desirable traits, further enhancing the potential for nutritional and industrial trait improvement in soybean breeding programs (Bhat and Yu, 2021). 4.5 Enhancing biodiversity and genetic variation Enhancing biodiversity and genetic variation is fundamental for the sustainability and adaptability of soybean breeding programs. Genomic tools have enabled the identification and incorporation of novel alleles from diverse germplasm collections into cultivated soybean varieties. Pangenome analysis and GWAS have provided insights into the genetic diversity of soybean, revealing significant structural variations and uncovering beneficial alleles from wild soybean relatives. For example, a study involving the resequencing of 302 wild and cultivated soybean accessions identified genes related to domestication and improvement, highlighting the importance of genetic variation for future breeding efforts (Zhou et al., 2015). The development of comprehensive genomic resources, such as SNP datasets and pangenomes, has facilitated the identification of alleles that can enhance traits like yield, disease resistance, and stress tolerance. By integrating these resources into breeding programs, breeders can develop soybean varieties with enhanced genetic diversity, ensuring their resilience and adaptability to changing environmental conditions (Petereit et al., 2022). Furthermore, the use of genomic selection and marker-assisted selection has accelerated the incorporation of diverse genetic material into breeding lines, promoting biodiversity and improving the overall genetic health of soybean crops. 5 Case Studies and Success Stories 5.1 Successful implementation of genomic selection (GS) in soybean breeding programs Genomic selection (GS) has proven to be a powerful tool in enhancing the efficiency of soybean breeding programs, particularly for complex traits like yield and seed composition. A prominent example is the study conducted by Stewart-Brown et al. (2019), which utilized GS to improve yield and seed composition traits within an applied soybean breeding program. The study involved 483 elite breeding lines genotyped with BARCSoySNP6K iSelect BeadChips. Cross-validation using RR-BLUP revealed high predictive abilities for protein (0.81), oil (0.71), and yield (0.26) at the largest tested training set size. This demonstrates the potential of GS to enhance genetic gains in soybean breeding programs (Figure 3) (Stewart-Brown et al., 2019). Genomic selection (GS) has been successfully implemented in soybean breeding programs, leading to significant improvements in genetic gain and efficiency. For example, a study on the implementation of GS in soybean demonstrated its potential to enhance breeding efficiency by predicting the genetic merit of soybean lines more accurately than traditional methods (Jin et al., 2023). Another study confirmed that GS could reduce the breeding cycle time, accelerating the development of high-yield and disease-resistant soybean varieties (Bernardo, 2016).
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