Legume Genomics and Genetics 2026, Vol.17, No.1, 49-67 http://cropscipublisher.com/index.php/lgg 49 Review Article Open Access Genomic Diversity and Population Structure Analysis of Global Soybean Germplasm Using SNP Markers Xingzhu Feng Hainan Institute of Biotechnology, Haikou, 570206, Hainan, China Corresponding email: xingzhu.feng@hitar.org Legume Genomics and Genetics, 2026 Vol.17, No.1 doi: 10.5376/lgg.2026.17.0004 Received: 13 Feb., 2026 Accepted: 17 Feb., 2026 Published: 27 Mar., 2026 Copyright © 2026 Feng, 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: Feng X.Z., 2026, Genomic diversity and population structure analysis of global soybean germplasm using SNP markers, Legume Genomics and Genetics, 17(1): 49-67 (doi: 10.5376/lgg.2026.17.0004) Abstract Soybean (Glycine max L.) is one of the most important oilseed and protein crops worldwide, and the effective utilization of global soybean germplasm resources plays a critical role in genetic improvement and sustainable agricultural development. With the rapid advancement of high-throughput sequencing technologies, single nucleotide polymorphism (SNP) markers have become powerful tools for studying genomic diversity and population structure in crop species. This study reviews the current progress in genomic diversity analysis of global soybean germplasm based on SNP markers. First, the distribution and conservation status of global soybean germplasm resources and the main methods used in genetic diversity research are summarized. Subsequently, the development and screening of SNP markers, evaluation metrics for genomic diversity, and commonly used bioinformatics analysis approaches are discussed. Furthermore, the population structure of soybean germplasm from different geographic regions is analyzed, and its relationship with genetic diversity and important agronomic traits is explored. A case study focusing on the population structure of global soybean core germplasm is also presented to illustrate the application of SNP-based analysis in germplasm evaluation and molecular breeding. Finally, the prospects for applying SNP markers in soybean genetic improvement, including marker-assisted selection, genomic selection, and multi-omics integration, are discussed. This review provides a theoretical reference for the efficient utilization of soybean germplasm resources and the development of improved soybean varieties. Keywords Soybean (Glycine max L.); SNP markers; Genomic diversity; Population structure; Germplasm resources 1 Introduction Genetic diversity within crop species underpins long-term gains in yield, resilience, and quality, and soybean is a prime example of a crop whose global importance is tightly linked to the breadth and structure of its germplasm resources. As a major source of plant protein and oil for food, feed, and industrial uses, soybean (Glycine max (L.) Merr.) contributes substantially to global food security, yet modern breeding has often relied on a relatively narrow subset of the available genetic pool (Duan et al., 2025). Historical domestication from wild soybean (Glycine soja) and subsequent breeding in geographically isolated programs have produced strong genetic bottlenecks and regionally distinct allelic compositions, especially in North American, South American, and East Asian cultivars (Viana et al., 2022). At the same time, emerging production regions such as sub-Saharan Africa, Southern Africa, and Central Asia are expanding soybean cultivation, often with germplasm of limited diversity adapted to local environments (Zatybekov et al., 2025). Comprehensive characterization of global soybean germplasm—encompassing landraces, modern cultivars, and wild relatives—is therefore essential to identify unique alleles, diagnose redundancy, understand population structure, and design effective strategies for broadening the genetic base and improving adaptation. Systematic germplasm research provides the framework for targeted introgression and informed parental selection in breeding programs. Studies in the USDA Soybean Germplasm Collection and other large panels have demonstrated that detailed molecular “fingerprinting” can reveal hidden population structure, delineate domestication and improvement sweeps, and identify private alleles maintained in specific gene pools or breeding programs (Kofsky et al., 2018). Analyses of African and tropical soybean collections have similarly shown that, while some elite lines possess broad within-population diversity, many regional germplasm pools exhibit low molecular diversity and strong relatedness, with most variation residing within rather than among populations
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