Bioscience Methods 2024, Vol.15, No.6, 255-263 http://bioscipublisher.com/index.php/bm 259 Asian soybean rust (ASR), caused by Phakopsora pachyrhizi, is a devastating foliar disease. Although not explicitly covered in the provided data, the principles of MAS for disease resistance in other pathogens can be applied to ASR. The identification and deployment of resistance genes through MAS can significantly enhance resistance to ASR. 4.2 Identification and deployment of disease resistance genes The identification of disease resistance genes involves high-resolution mapping, genomic sequencing, and the development of molecular markers. For instance, the identification of candidate genes for PRR resistance has been achieved through high-resolution mapping and RNA-seq analysis, pinpointing specific genes that can be targeted for MAS (Jiang et al., 2020; Karhoff et al., 2022). Similarly, the development of SNP markers for SCN resistance loci Rhg1 and Rhg4 has facilitated the differentiation of resistant and susceptible genotypes, accelerating the breeding of resistant cultivars (Kadam et al., 2016). 4.3 Case study: utilization of MAS for enhancing SCN resistance in soybean Screening for SCN resistance involves evaluating a large number of soybean accessions for resistance traits. For example, the use of the SoySNP50K iSelect BeadChip has enabled the evaluation of phylogenetic diversity and the identification of novel sources of SCN resistance. The integration of resistance genes into elite soybean cultivars is achieved through MAS. Gene-specific markers for Rhg1 and Rhg4 have been developed, allowing for the precise selection and incorporation of these resistance genes into breeding programs (Kim et al., 2016; Kadam et al., 2016). The stability of resistance and its impact on yield are critical factors in the success of MAS. Studies have shown that resistance alleles can significantly increase yield in disease-prone fields without negatively affecting yield in less disease-prone environments (Karhoff et al., 2022). Continuous evaluation and breeding efforts are necessary to ensure the durability and effectiveness of resistance genes (Kim et al., 2016). 4.4 Strategies for combining disease resistance and yield traits Combining disease resistance with high yield traits is a major goal in soybean breeding. The identification of QTL associated with both yield and disease resistance can facilitate the development of high-yielding, disease-resistant cultivars. For instance, the identification of yield QTL and their integration with disease resistance genes through MAS can enhance both yield and resistance in soybean (Fallen et al., 2015). Advanced genomic approaches, such as genomic selection and genome editing, offer promising strategies for achieving this goal (Chandra et al., 2022). 5 Challenges and Limitations of MAS in Soybean Breeding 5.1 Technical and operational challenges Marker-assisted selection (MAS) in soybean breeding faces several technical and operational challenges. One significant issue is the complexity of traits such as yield, which are controlled by multiple quantitative trait loci (QTL) with small individual effects. This complexity makes it difficult to identify and utilize effective markers for MAS (Fallen et al., 2015). Additionally, the accuracy of MAS can be compromised by the presence of residual heterogeneity within elite soybean populations, which affects the detection and selection of yield QTL (Sebastian et al., 2010). Another operational challenge is the labor-intensive and time-consuming nature of phenotypic evaluations required for traits like pod shattering resistance, which complicates the integration of MAS into breeding programs (Kim et al., 2020). 5.2 Genetic and Environmental Interactions The effectiveness of MAS is often limited by genetic and environmental interactions. For instance, the expression of QTL can vary significantly across different environments, making it challenging to identify stable markers that are effective in diverse conditions (Fallen et al., 2015). This context-specific variability necessitates the development of models that can predict genotype performance within specific environmental contexts, which adds another layer of complexity to the breeding process (Sebastian et al., 2010). Moreover, the interaction between different resistance genes, such as those for soybean mosaic virus (SMV), can lead to unexpected susceptibility in certain genetic backgrounds, further complicating the use of MAS (Maroof et al., 2008).
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