Jun Lei , Zhuwei Xu , Xiaowei Shao , Huan Jiang , Yumei Zhang

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
Author    Correspondence author
Legume Genomics and Genetics, 2024, Vol. 15, No. 6   doi: 10.5376/lgg.2024.15.0026
Received: 03 Nov., 2024    Accepted: 05 Nov., 2024    Published: 15 Dec., 2024

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.

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)

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.

Soybean breeding; GWAS; Genomic selection; Yield improvement; Sustainable agriculture