Legume Genomics and Genetics 2024, Vol.15, No.3, 126-139 http://cropscipublisher.com/index.php/lgg 128 3 Genomic Tools and Technologies in Soybean Breeding 3.1 Genomic selection (GS) Genomic Selection (GS) is an advanced breeding technique that employs genome-wide molecular markers to predict the genetic potential of plants for various traits, thus accelerating the breeding process. This method is particularly valuable for improving complex traits such as yield, disease resistance, and seed quality, which are controlled by multiple genes. In soybean breeding, GS has been shown to significantly enhance the accuracy of selection and the speed of breeding cycles. For example, a study demonstrated that using GS with a medium density of markers and a genomic best linear unbiased prediction (GBLUP) model could predict up to 39% of the phenotypic variation in yield among soybean lines (Duhnen et al., 2017). Another study highlighted the potential of GS to achieve predictive accuracies of 0.81 for protein content, 0.71 for oil content, and 0.26 for yield, demonstrating its effectiveness in soybean breeding programs (Stewart-Brown et al., 2019). The use of GS reduces the reliance on extensive phenotypic evaluations, which are time-consuming and labor-intensive, thereby streamlining the breeding process and enabling the rapid development of superior soybean varieties. 3.2 Marker-assisted selection (MAS) Marker-Assisted Selection (MAS) uses specific DNA markers linked to desirable traits to guide the selection process in breeding programs. This technique is particularly effective for traits that are difficult to assess through traditional phenotypic methods, such as resistance to diseases and pests, as well as tolerance to environmental stresses. In soybean breeding, MAS has been instrumental in improving resistance to key diseases like Phytophthora Root Rot and Soybean Cyst Nematode. For instance, MAS has been shown to be highly efficient in selecting soybean lines with improved resistance to these diseases, achieving high prediction accuracies and reducing the need for labor-intensive phenotypic evaluations. Additionally, MAS has been utilized to enhance pod shattering resistance in soybeans, with a study demonstrating prediction accuracies of up to 96% using specific markers (Kim et al., 2020). The integration of MAS into soybean breeding programs has led to the development of more robust and resilient soybean varieties, capable of withstanding various biotic and abiotic stresses, thereby contributing to higher yields and improved crop performance. 3.3 Genome-wide association studies (GWAS) Genome-Wide Association Studies (GWAS) are a powerful tool used to identify associations between genetic variations and traits across the entire genome. GWAS has been extensively applied in soybean breeding to uncover the genetic basis of complex traits such as yield, maturity, plant height, and seed composition. For example, a study involving 250 soybean accessions identified significant single nucleotide polymorphisms (SNPs) associated with traits like grain yield, plant height, and seed weight. This study used a Bayesian Information and Linkage Disequilibrium Iteratively Nested Keyway (BLINK) model to perform GWAS and found numerous SNPs linked to these important agronomic traits (Figure 1) (Ravelombola et al., 2020). The identification of these SNPs allows breeders to implement marker-assisted selection and genomic selection strategies more effectively. Moreover, GWAS has facilitated the discovery of novel alleles and the refinement of previously known loci, which are crucial for improving soybean traits. The integration of GWAS findings into breeding programs accelerates the development of high-yielding, disease-resistant, and stress-tolerant soybean varieties, enhancing the overall efficiency and effectiveness of soybean breeding efforts. 3.4 Quantitative trait loci (QTL) mapping Quantitative Trait Loci (QTL) mapping is a technique used to identify regions of the genome that are associated with specific quantitative traits. In soybean breeding, QTL mapping has been instrumental in identifying genomic regions linked to important traits such as yield, seed size, and disease resistance. For instance, a study using chromosome segment substitution lines (CSSLs) developed from a cross between wild and cultivated soybeans
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