Molecular Plant Breeding 2024, Vol.15, No.5, 259-268 http://genbreedpublisher.com/index.php/mpb 259 Research Insight Open Access Improving Soybean Breeding Efficiency Using Marker-Assisted Selection Xiaomei Wang, Yuxin Qi, Guohong Sun, Shuai Zhang, Wen Li, Yanping Wang Heilongjiang Academy of Agricultural Sciences, Mudanjiang Branch, Mudanjiang, 157000, Heilongjiang, China Corresponding email: wyping1981@126.com Molecular Plant Breeding, 2024, Vol.15, No.5 doi: 10.5376/mpb.2024.15.0025 Received: 20 Aug., 2024 Accepted: 21 Sep., 2024 Published: 30 Sep., 2024 Copyright © 2024 Wang et al., This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Wang X.M., Qi Y.X., Sun G.H., Zhang S., Li W., and Wang Y.P., 2024, Improving soybean breeding efficiency using marker-assisted selection, Molecular Plant Breeding, 15(5): 259-268 (doi: 10.5376/mpb.2024.15.0025) Abstract This study provides an in-depth analysis of recent advances in MAS technology in soybeans, focusing on the identification of quantitative trait loci (QTLs) associated with key agronomic traits such as insect resistance, disease resistance, yield enhancement, and improved nutritional quality. The results showed that the integration of MAS into soybean breeding programs significantly shortened the breeding cycle and improved the accuracy of trait selection. This study also delves into case studies of the successful application of MAS in commercial soybean breeding programs. The aim of this study was to explore ways to improve the efficiency of soybean (Glycine max) breeding using marker-assisted selection (MAS), highlighting the potential of MAS to revolutionize soybean breeding and provide opportunities for the development of high-yield, disease-resistant and nutrient-enhanced soybean varieties. Keywords Soybean (Glycine max); Marker-assisted selection; Quantitative trait loci (QTLs); Breeding efficiency; Disease resistance 1 Introduction Soybean (Glycine max (L.) Merr.) is a cornerstone of global agriculture, serving as a vital source of protein and oil. It has been cultivated for thousands of years, initially in China and Eastern Asia, and has since become the most cultivated and utilized oilseed crop worldwide (Zhang, 2024). Today, approximately 80%~85% of the world's soybeans are grown in the Western Hemisphere, covering around 88 million hectares. Soybeans are integral to various industries, providing high-quality protein for livestock and aquaculture, oil for industrial uses, and essential components for human diets (Anderson et al., 2019). The crop’s economic significance is underscored by its role in meeting the increasing global demand for protein and oil, making it a critical agricultural commodity. Traditional soybean breeding methods, while successful in many respects, face several challenges. Classical breeding techniques, which rely on phenotypic selection, are often time-consuming and labor-intensive. Additionally, the complex genetic architecture of traits such as yield, protein, and oil content complicates the breeding process. Environmental factors further influence these traits, making it difficult to achieve consistent improvements across different growing conditions. The negative correlation between seed protein and oil concentration adds another layer of complexity, as improving one trait can adversely affect the other (Fields et al., 2023). These challenges necessitate the development of more efficient and precise breeding methods. Marker-assisted selection (MAS) has emerged as a powerful tool to enhance the efficiency and precision of soybean breeding. MAS leverages molecular markers linked to desirable traits, allowing breeders to select plants with the desired genetic makeup early in the breeding process. This method significantly reduces the time and resources required for developing new cultivars. Studies have demonstrated the effectiveness of MAS in identifying quantitative trait loci (QTLs) associated with key traits such as seed protein and oil content, thereby facilitating the development of soybean varieties with improved nutritional profiles (Van and McHale, 2017; Fields et al., 2023). The integration of MAS with other advanced breeding technologies, such as genomic selection and CRISPR/Cas9 gene editing, holds promise for further accelerating soybean improvement (Miller et al., 2023; Yao et al., 2023).
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