Molecular Plant Breeding 2024, Vol.15, No.6, 371-378 http://genbreedpublisher.com/index.php/mpb 371 Research Insight Open Access Identification and Application of Yield-Related QTLs in Soybean Based on GWAS 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 Corresponding email: nkyddslxl@163.com 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 Copyright © 2024 Cao 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: 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) Abstract 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. Keywords Soybean; Genome-wide association studies (GWAS); Quantitative trait loci (QTLs); Marker-assisted selection; Genomic selection; Yield traits; Machine learning 1 Introduction Soybean (Glycine max [L.] Merr.) is one of the most significant crops globally, primarily due to its high oil and protein content, which are essential for human and animal nutrition. It serves as a critical source of edible oil and protein-rich food, making it indispensable in the agricultural sector (Kumar et al., 2021; Rani et al., 2023). The increasing global population and the rising demand for soybean products necessitate the development of improved soybean varieties to ensure food security and meet nutritional needs. Improving soybean yield is crucial for enhancing agricultural productivity and profitability. Higher yields can help meet the growing demand for soybean products, thereby supporting the agricultural economy and ensuring a stable supply of essential nutrients. However, achieving significant yield improvements is challenging due to the complex genetic interactions and environmental factors that influence soybean growth and development (Kumar et al., 2021; Rani et al., 2023). Therefore, identifying and utilizing genetic factors that contribute to yield enhancement is of paramount importance. Quantitative trait loci (QTLs) play a vital role in understanding the genetic basis of yield-related traits in crops. QTL mapping and genome-wide association studies (GWAS) are powerful tools for identifying genomic regions associated with important agronomic traits, including yield (Rani et al., 2023; Wang et al., 2023). By pinpointing specific QTLs, researchers can develop molecular markers for marker-assisted selection (MAS), facilitating the breeding of high-yielding soybean varieties. The identification of stable and novel QTLs can significantly contribute to the genetic improvement of soybean, enabling breeders to enhance yield and other desirable traits more efficiently (Huang et al., 2020; Kim et al., 2023). This study analyzes diverse soybean germplasm resources, consolidating genomic loci associated with key yield-related traits and identifying candidate genes as potential targets for future breeding programs. It aims to provide valuable insights into the genetic factors affecting soybean yield and to support the development of high-yield soybean varieties through marker-assisted selection.
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