Chunxia Wu , Qishan Chen

Modern Agricultural Research Center, Cuixi Academy of Biotechnology, Zhuji, 311800, Zhejiang, China
Author    Correspondence author
Legume Genomics and Genetics, 2025, Vol. 16, No. 1   
Received: 11 Nov., 2024    Accepted: 21 Dec., 2024    Published: 05 Jan., 2025

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.

Soybean (Glycine max) is a very important crop and is cultivated in many places around the world. It is rich in protein and oil and is a common and important part of agriculture and food production. With the continuous progress of genomic research, scientists have also found more ways to improve soybeans. Among them, GWAS (Genome-wide Association Study) is a commonly used technique that can be employed to identify the locations of genes related to agronomic traits. This study introduces the basic concepts and main methods of GWAS, such as how to conduct genotyping, how to collect phenotypic data, and common statistical analysis approaches. All these contents are closely related to soybean research. Nowadays, GWAS has been used to discover many genes related to soybean yield, disease resistance and stress tolerance. These discoveries have provided many references for breeding and also accelerated the progress of breeding. However, there are also many challenges in the practical application of GWAS. For instance, there are significant differences in genetic background among different soybean varieties, and their phenotypes are also easily influenced by the environment. In addition, the interaction between genes (superiorality) also makes the analysis more complex. This study also takes the disease resistance of soybeans as an example, focusing on introducing the achievements of GWAS in improving disease-resistant varieties, especially its application in genetically modified soybeans and the benefits it brings. Looking to the future, there are still many areas where GWAS can be improved. For instance, multiple omics data can be combined, more powerful computing tools can be used, and the technology for trait collection can also be improved. All these practices will make GWAS more useful in soybean research. GWAS plays a very important role in soybean breeding. This research has laid a foundation for future genetic studies and provided technical support for the screening and improvement of high-quality soybean varieties.

Soybean; Genome-wide association studies; Crop improvement; Genetic loci; Breeding programs