Bioscience Methods 2024, Vol.15, No.6, 255-263 http://bioscipublisher.com/index.php/bm 261 valuable insights and methodologies that can be adapted for soybean breeding (Francia et al., 2005; Miedaner and Korzun, 2012). 7 Concluding Remarks The research on the impact of marker-assisted selection (MAS) on soybean yield and disease resistance has yielded significant insights. Several studies have identified quantitative trait loci (QTL) associated with yield and disease resistance, demonstrating the potential of MAS in soybean breeding. For instance, a study identified 46 yield QTL, with five being novel, explaining 4.5% to 11.9% of the phenotypic variation for yield. Another study identified four QTL associated with resistance to sudden death syndrome (SDS), accounting for 65% of the phenotypic variability in disease incidence. Additionally, the accelerated yield technology™ (AYT™) approach has been effective in combining forward selection for simple traits with context-specific MAS for complex traits like yield. The validation of MAS for pod shattering resistance showed high prediction accuracy, confirming its applicability in breeding programs. Furthermore, genomic selection (GS) has been shown to be as effective as phenotypic selection for yield, with the potential for greater efficiency if marker assay costs are reduced. The findings from these studies have several implications for soybean breeding programs. The identification of specific QTL for yield and disease resistance traits provides valuable markers that can be used to enhance selection efficiency and accuracy. For example, the use of markers for SDS resistance can significantly improve the selection of resistant genotypes, thereby protecting yield in infested fields. The integration of MAS and GS into breeding programs can streamline the selection process, reducing the time and resources required for developing high-yielding, disease-resistant soybean varieties. The successful application of MAS for traits like pod shattering resistance and SCN resistance further underscores its utility in addressing specific breeding challenges. Overall, these advancements can lead to the development of soybean varieties with improved yield potential and resilience to biotic and abiotic stresses. Future research should focus on several key areas to further enhance the impact of MAS on soybean breeding. First, there is a need for continued identification and validation of QTL associated with important agronomic traits across diverse genetic backgrounds and environments. This will ensure the robustness and applicability of MAS in different breeding contexts. Second, the development of cost-effective genotyping methods will be crucial for the widespread adoption of GS and MAS in breeding programs. Third, research should explore the integration of MAS with other advanced breeding techniques, such as genome editing, to accelerate the development of superior soybean varieties. Finally, breeding programs should prioritize the pyramiding of multiple resistance genes to develop cultivars with broad-spectrum and durable resistance to various diseases, as demonstrated in the case of SMV resistance. By addressing these areas, future research can significantly contribute to the sustainability and productivity of soybean agriculture. Acknowledgments We are grateful to Dr. Zhao for critically reading the manuscript and providing valuable feedback that improved the clarity of the text. We express our heartfelt gratitude to the two anonymous reviewers for their valuable comments on the manuscript. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. Reference Adamič S., and Leskovšek R., 2021, Soybean (Glycine max (L.) Merr.) growth, yield, and nodulation in the early transition period from conventional tillage to conservation and no-tillage systems, Agronomy, 11(12): 2477. https://doi.org/10.3390/agronomy11122477 Arruda M., Lipka A., Brown P., Krill A., Thurber C., Brown-Guedira G., Dong Y., Foresman B., and Kolb F., 2016, Comparing genomic selection and marker-assisted selection for Fusarium head blight resistance in wheat (Triticum aestivumL.), Molecular Breeding, 36: 1-11. https://doi.org/10.1007/s11032-016-0508-5 Chandra S., Choudhary M., Bagaria P., Nataraj V., Kumawat G., Choudhary J., Sonah H., Gupta S., Wani S., and Ratnaparkhe M., 2022, Progress and prospectus in genetics and genomics of Phytophthora root and stem rot resistance in soybean (Glycine max L.), Frontiers in Genetics, 13: 939182. https://doi.org/10.3389/fgene.2022.939182
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