Legume Genomics and Genetics 2024, Vol.15, No.6, 270-279 http://cropscipublisher.com/index.php/lgg 270 Research Insight Open Access Integrating GWAS and Genomic Selection to Enhance Soybean Breeding JunLei 1, Zhuwei Xu1, Xiaowei Shao1, Huan Jiang1, Yumei Zhang2 1 Quzhou Academy of Agricultural and Forestry Sciences, Quzhou, 324000, Zhejiang, China 2 Institute of Crop Sciences, Fujian Academy of Agricultural Sciences, Fujian Engineering Research Center for Characteristic Dry Crop Varieties Breeding, Fuzhou, 350013, Fujian, China Corresponding email: zym1122@126.com Legume Genomics and Genetics, 2024 Vol.15, No.6 doi: 10.5376/lgg.2024.15.0026 Received: 03 Nov., 2024 Accepted: 05 Dec., 2024 Published: 15 Dec., 2024 Copyright © 2024 Lei 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: Lei J., Xu Z.W., Shao X.W., Jiang H., and Zhang Y.M., 2024, Integrating GWAS and genomic selection to enhance soybean breeding, Legume Genomics and Genetics, 15(6): 270-279 (doi: 10.5376/lgg.2024.15.0026) Abstract This study explores the integration of Genome-Wide Association Studies (GWAS) and Genomic Selection (GS) to enhance soybean breeding efficiency. By leveraging GWAS for genetic insights and GS for predictive selection, the study identifies key agronomic traits, including yield, disease resistance, and stress tolerance, that are essential to soybean crop improvement. Through case studies, it highlights the effectiveness of GWAS and GS in identifying high-performing genotypes and accelerating breeding cycles. The study further addresses challenges such as the resource demands of genomic technologies and potential solutions, including machine learning and high-throughput phenotyping. The findings underscore the transformative potential of combining GWAS and GS for breeding programs, aiming to meet global demands for high-yielding, resilient soybean varieties and to promote sustainable agricultural practices. Keywords Soybean breeding; GWAS; Genomic selection; Yield improvement; Sustainable agriculture 1 Introduction Soybean (Glycine max L.) is a globally significant crop, renowned for its versatile applications in food, feed, and industrial sectors. It is a primary source of plant-based protein and oil, contributing substantially to human diets, livestock feed, and various industrial products (Anderson et al., 2019; Singer et al., 2023). The crop's adaptability and high nutritional value have made it a staple in many regions, particularly in the Western Hemisphere, where the majority of the world's soybean cultivation occurs. Soybeans are also integral to the production of biodiesel and other industrial materials, underscoring their economic importance. The increasing global population and changing dietary preferences have escalated the demand for soybeans. Traditional breeding methods, while effective, are often time-consuming and may not keep pace with the rapid need for improved crop varieties (Anderson et al., 2019; Kumar et al., 2021). Enhanced breeding techniques are essential to develop soybean varieties that can meet the growing demand for higher yields, improved nutritional quality, and resistance to biotic and abiotic stresses (Miller et al., 2023). Advanced breeding technologies, such as genomic selection and genome editing, offer promising solutions to accelerate the development of superior soybean cultivars (Yao et al., 2023). Genome-Wide Association Studies (GWAS) and Genomic Selection (GS) are cutting-edge techniques that have revolutionized plant breeding. GWAS involves scanning the genome of diverse germplasm collections to identify genetic variants associated with specific traits, thereby facilitating the discovery of quantitative trait loci (QTLs) and candidate genes (Kim et al., 2023). This method has been instrumental in dissecting the genetic basis of complex traits in soybeans, such as yield, protein content, and disease resistance (Wang et al., 2023). Genomic Selection (GS), on the other hand, uses genome-wide markers to predict the breeding values of individuals, enabling the selection of superior genotypes early in the breeding cycle. This approach has shown significant promise in improving traits like yield, protein, and oil content in soybean breeding programs (Miller et al., 2023). By integrating GWAS and GS, breeders can enhance the accuracy and efficiency of selecting desirable traits, thereby accelerating the development of high-performing soybean varieties (Rani et al., 2023).
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