Legume Genomics and Genetics 2024, Vol.15, No.3, 126-139 http://cropscipublisher.com/index.php/lgg 134 In combating the soybean cyst nematode (SCN), Espindola et al. (2016) utilized microsatellite markers Sat_141 and Sat_168 to identify SCN-resistant genotypes. This study demonstrated the effectiveness of MAS in selecting resistant plants, achieving high accuracy in genotypic versus phenotypic evaluations (Espindola et al., 2016). Dubiela et al. (2019) developed SNP markers associated with resistance to Meloidogyne incognita, a root-knot nematode. These markers were validated and used in MAS protocols to accurately identify resistant plants, showcasing the practical application of molecular markers in breeding programs. 5.3 Notable GWAS findings and their implications Genome-wide association studies (GWAS) have identified numerous genetic loci associated with key agronomic traits in soybean, such as yield, oil content, and stress tolerance. For example, a GWAS identified loci associated with seed protein and oil content, which are crucial for soybean quality improvement (Cao et al., 2017). Another study highlighted the discovery of loci related to drought tolerance, providing insights into the genetic mechanisms underlying stress responses in soybean (Li et al., 2017). These findings have significant implications for soybean breeding, enabling the development of varieties with enhanced nutritional value and environmental resilience. Che et al. (2020) conducted a GWAS for resistance to Soybean Mosaic Virus (SMV) and identified novel loci and candidate genes on chromosomes 8 and 20. These findings provided new genetic sources for MAS breeding programs aimed at developing SMV-resistant soybean varieties. Another notable study by Ravelombola et al. (2021) combined GWAS and GS to dissect the genetic basis of yield-related traits. They identified significant SNPs associated with yield, plant height, and seed weight, contributing valuable markers for breeding programs focused on yield improvement. 5.4 Advances in QTL mapping and utilization Quantitative Trait Loci (QTL) mapping has been instrumental in identifying genetic regions associated with important traits in soybean. Pham et al. (2015) fine-mapped resistance genes to Cercospora sojina, the causal agent of frogeye leaf spot. This study narrowed down the resistance loci to specific genomic regions, facilitating the development of resistant cultivars through MAS. Another significant advancement was made by Chandra et al. (2022), who reviewed the progress in genetic mapping for resistance to Phytophthora root and stem rot (PRSR). This review highlighted the identification of multiple Rps genes and partial resistance loci, contributing to the genetic improvement of soybean against PRSR. Moreover, Zhong et al. (2018) identified and fine-mapped a novel Phytophthora resistance gene, RpsHC18, using next-generation sequencing. This study developed diagnostic markers for MAS, enhancing the selection process for Phytophthora-resistant soybean cultivars. 5.5 Breakthroughs with gene editing in soybean Gene editing technologies, particularly CRISPR/Cas9, have revolutionized soybean breeding by enabling precise modifications of the genome. Successful examples include the development of high oleic acid soybean varieties through CRISPR/Cas9-mediated editing of the fatty acid desaturase genes (Do et al., 2019). Additionally, gene editing has been used to enhance resistance to herbicides, facilitating more effective weed management in soybean cultivation (Wei et al., 2023). These breakthroughs demonstrate the potential of gene editing to accelerate the development of soybean varieties with improved traits and adaptability. Li et al. (2017), who used CRISPR/Cas9 to enhance oleic acid content in soybean oil. This study showcased the potential of gene editing to improve the nutritional quality of soybean, aligning with health and market demands. In addition, Nagamine et al. (2020) successfully edited multiple genes simultaneously using CRISPR/Cas9, demonstrating the feasibility of multiplex gene editing in soybean. This approach significantly accelerates the development of varieties with stacked traits, enhancing overall crop performance.
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