Legume Genomics and Genetics 2026, Vol.17, No.1, 49-67 http://cropscipublisher.com/index.php/lgg 50 (Obua et al., 2020). Such findings have direct implications for breeding strategies, highlighting the need for pre-breeding, expansion of geographic sources of introductions, and more deliberate use of wild and exotic accessions to counteract the erosion of genetic variation (Duan et al., 2025). In this context, global, SNP-based assessments of diversity and population structure are a critical step toward rational utilization, conservation, and deployment of soybean genetic resources. Molecular marker technologies have transformed crop genetic research by enabling robust, high-throughput assessment of diversity, relatedness, and genome–trait associations independent of environmental noise. Traditional approaches based on morphology or biochemical markers are limited by genotype-by-environment interactions, developmental stage specificity, and the small number of traits that can be scored reliably (Rani et al., 2023). DNA markers overcome these constraints by directly assaying heritable variation at the nucleotide level, and have been widely used in soybeans and other crops for diversity analysis, QTL mapping, marker-assisted selection (MAS), genomic selection, and cultivar identification (Bunjkar et al., 2024). A broad suite of marker systems—including RFLP, RAPD, AFLP, SSR, EST-SSR, ISSR, and SNPs—has been deployed in legume genetics, each with specific advantages in terms of polymorphism, cost, throughput, and ease of scoring (Bunjkar et al., 2024). In soybean, SSR and EST-SSR markers have played a central role in early diversity and population structure studies, revealing high polymorphism and enabling differentiation of germplasm from diverse geographic origins (Zatybekov et al., 2023). However, continuing advances in genotyping technologies and next-generation sequencing have shifted the focus toward sequence-based markers, especially single nucleotide polymorphisms (SNPs), which now dominate large-scale diversity and association studies. Modern SNP platforms such as genotyping-by-sequencing (GBS), diversity array technology (DArTseq), medium-density panels, and fixed arrays like SoySNP50K and its nested derivatives have greatly reduced the cost per data point and enabled genome-wide coverage in large germplasm collections (Song et al., 2024). These markers feed directly into multivariate and model-based analytical frameworks—principal component analysis (PCA), principal coordinate analysis (PCoA), hierarchical clustering, and Bayesian or likelihood-based STRUCTURE-type models—allowing fine-scale dissection of population structure, admixture, and genetic differentiation (Bunjkar et al., 2024; Zatybekov et al., 2025). Integration of SNP data with phenotypic and environmental information further supports genome-wide association studies and genomic prediction, accelerating the identification and deployment of favorable alleles in breeding pipelines (Chander et al., 2021). Within this spectrum of marker systems, SNPs offer particular advantages for soybean genome research and for global germplasm diversity and structure analyses. SNPs are the most abundant form of genetic variation in eukaryotic genomes, broadly and evenly distributed across coding and non-coding regions, and exhibit low recurrent mutation rates that make them evolutionarily stable and well suited for tracing haplotypes and demographic history (Rani et al., 2023; Bunjkar et al., 2024). High-throughput SNP assays, including SoySNP50K, Axiom® SoyaSNP, and reduced panels such as SoySNP6K, SoySNP3K and SoySNP1K, combine high marker density with automation, low error rates, and scalability from hundreds to thousands of accessions (Kofsky et al., 2018). These platforms have enabled comprehensive genotyping of entire national and international germplasm collections, facilitating the detection of redundant accessions, construction of haplotype block maps, and precise estimation of linkage disequilibrium patterns across wild, landrace, and elite populations (Song et al., 2024). In soybean, SNP-based studies have successfully resolved population structure at global and regional scales, distinguishing wild from cultivated accessions, identifying transitional genotypes, and quantifying genetic similarity between local germplasm and foreign cultivars (Tsindi et al., 2023). SNP markers are also particularly powerful for integrated analyses that link diversity patterns to breeding history and future improvement prospects. Population structure analyses using SNP panels have revealed low genetic differentiation among some regional collections, reflecting extensive germplasm exchange, but also identified unique clusters and private alleles in underexploited gene pools that can serve as reservoirs of novel variation for
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