Molecular Plant Breeding 2025, Vol.16, No.1, 35-43 http://genbreedpublisher.com/index.php/mpb 39 4.5 CRISPR/Cas9 and its potential synergy with MAS The CRISPR/Cas9 genome editing technology holds great promise for enhancing MAS in soybean breeding. This technology allows for precise modifications of specific genes, enabling the introduction of desirable traits or the elimination of deleterious ones. The potential synergy between CRISPR/Cas9 and MAS lies in the ability to combine targeted gene editing with marker-assisted selection to achieve rapid and accurate trait improvement. While the application of CRISPR/Cas9 in soybean breeding is still in its early stages, its integration with MAS could lead to significant advancements in crop improvement (Francia et al., 2005; Jena and Mackill, 2008). 5 Challenges and Limitations of MAS in Soybean Breeding 5.1 Cost and resource requirements Marker-assisted selection (MAS) in soybean breeding, while promising, is often hindered by significant cost and resource requirements. The process involves extensive genotyping and phenotyping, which can be expensive and labor-intensive. For instance, the need for high-throughput genotyping platforms and advanced bioinformatics tools to analyze large datasets adds to the overall cost (He et al., 2014; Chen et al., 2016). Additionally, the development and validation of molecular markers require substantial investment in both time and resources, making it challenging for smaller breeding programs to adopt MAS (Babu et al., 2004; Kumawat et al., 2020). 5.2 Marker density and genomic coverage Another critical challenge in MAS is achieving adequate marker density and genomic coverage. High-density markers are essential for accurately mapping and selecting for traits, especially in complex genomes like that of soybean. However, the use of low or medium-density markers can lead to incomplete genomic coverage, reducing the efficiency of MAS (Babu et al., 2004; Sandhu et al., 2022). This limitation is particularly pronounced in polygenic traits, where multiple genes across the genome influence the trait, necessitating a comprehensive marker system to capture all relevant genetic variations (Miedaner and Korzun, 2012). 5.3 Genetic background effects The genetic background of the breeding material can significantly impact the effectiveness of MAS. The presence of background genetic noise can obscure the association between markers and target traits, leading to inaccurate selection (Francia et al., 2005; Miedaner and Korzun, 2012). This issue is exacerbated in quantitative traits, where the effects of individual quantitative trait loci (QTL) are often small and influenced by the genetic context in which they are expressed. Consequently, the introgression of desirable traits can be less predictable and more challenging to achieve (Ribaut and Ragot, 2006; Wang et al., 2024). 5.4 Challenges in trait introgression and stability Introgressing traits into elite soybean lines using MAS can be complicated by several factors. One major issue is the stability of the introgressed traits across different environments and genetic backgrounds. Traits that perform well in one genetic context may not exhibit the same level of expression or stability in another, leading to inconsistent breeding outcomes (Miedaner and Korzun, 2012; Kumawat et al., 2020). Additionally, the process of backcrossing to recover the recurrent parent genome while retaining the target trait can be time-consuming and may not always result in complete trait stability (Francia et al., 2005; Ribaut and Ragot, 2006). This challenge underscores the need for careful planning and extensive field testing to ensure the successful integration and stability of desired traits in soybean breeding programs. 6 Case Study: Successful Implementation of MAS for Soybean Cyst Nematode Resistance 6.1 Background and problem statement Soybean cyst nematode (SCN, Heterodera glycines Ichinohe) is a significant pest affecting soybean production worldwide, causing substantial yield losses. Traditional breeding methods for SCN resistance are time-consuming and labor-intensive, necessitating the development of more efficient approaches. Marker-assisted selection (MAS) offers a promising solution by enabling the identification and selection of resistant genotypes based on genetic markers linked to SCN resistance traits (Li et al., 2009; Kadam et al., 2016; Santana et al., 2014).
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