Dan Cao , Yongguo Xue , Xiaofei Tang , Jianqiang Sun , Xiaoyan Luan , Qi Liu , Zifei Zhu , Wenjin He , Xinlei Liu

Soybean Research Institute, Heilongjiang Academy of Agricultural Science, Harbin, 150086, Heilongjiang, China
Author    Correspondence author
Molecular Plant Breeding, 2024, Vol. 15, No. 6   doi: 10.5376/mpb.2024.15.0035
Received: 28 Oct., 2024    Accepted: 30 Nov., 2024    Published: 07 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.

Cao D., Xue Y.G., Tang X.F., Sun J.Q., Luan X.Y., Liu Q., Zhu Z.F., He W.J., and Liu X.L., 2024, Identification and application of yield-related QTLs in soybean based on GWAS, Molecular Plant Breeding, 15(6): 371-378 (doi: 10.5376/mpb.2024.15.0035)

This study consolidates and analyzes advancements in identifying yield-related quantitative trait loci (QTLs) in soybean using genome-wide association studies (GWAS). Focusing on methods and outcomes for locating key QTLs that influence soybean yield, this paper summarizes the application of advanced genotyping techniques such as high-density single nucleotide polymorphism (SNP) arrays and whole-genome sequencing, along with the role of machine learning algorithms in improving QTL detection accuracy. The potential of these QTLs in marker-assisted selection (MAS) and genomic selection (GS) is also explored. Additionally, this review highlights the importance of multi-environment trials and candidate gene validation in enhancing QTL applicability, providing a theoretical foundation and technical outlook for high-yield soybean breeding. By synthesizing current findings, this study offers a comprehensive perspective for further genetic research on soybean yield traits and precision breeding strategies.

Soybean; Genome-wide association studies (GWAS); Quantitative trait loci (QTLs); Marker-assisted selection; Genomic selection; Yield traits; Machine learning