Legume Genomics and Genetics 2026, Vol.17, No.1, 49-67 http://cropscipublisher.com/index.php/lgg 62 landraces and wild soybean. Collectively, SNP-based diversity and structure analyses have shifted soybean from a sparsely characterized crop to one with dense global genomic coverage, providing a foundation for modern molecular breeding. Despite this progress, several challenges limit full exploitation of genomic diversity for soybean improvement. Many regional germplasm pools, including Brazilian, Kazakh, and Southern African collections, show narrow genetic bases and low molecular diversity, often reflecting heavy reliance on a small set of ancestors and extensive germplasm sharing. Such bottlenecks constrain long-term genetic gain, reduce resilience to emerging stresses, and increase vulnerability to climate change. Even where diversity exists globally, it is unevenly represented in working breeding pools, and pre-breeding to introgress favorable alleles from wild and exotic sources remains limited and slow. In addition, most diversity studies report that the overwhelming proportion of variation resides within rather than among populations, emphasizing that effective use of diversity requires careful within-pool sampling and crossing strategies, not just inter-population contrasts. Methodological and translational gaps also persist. Although high-density SNP resources are abundant, their integration with deep, standardized multi-environment phenotyping remains incomplete, leading to underpowered GWAS for complex traits and limited validation of candidate genes. Many association signals are population-or environment-specific, and genotype-by-environment interactions reduce the robustness of marker–trait relationships for direct deployment in breeding. In emerging production regions, infrastructure for high-throughput genotyping and data analysis is often insufficient, slowing adoption of genomic tools. Finally, even where strong genomic resources exist (e.g., USDA and Asian collections), regulatory, logistical, and data-sharing barriers can impede the exchange and utilization of diverse germplasm and associated genomic data at a truly global scale. Future work on soybean genomic diversity should prioritize systematic broadening of breeding germplasm using global SNP and resequencing resources as guides. Whole-genome analyses already demonstrate that wild soybean, Asian landraces, and regionally adapted landraces harbor substantially higher diversity and distinct haplotypes than many modern cultivars. Strategic identification of complementary parental combinations—such as crossings between narrow-based regional pools and diverse foreign or wild accessions—can be optimized using genome-wide similarity matrices, haplotype block maps, and FST scans to maximize novel allelic recombination while preserving local adaptation. Pre-breeding pipelines that couple genomic prediction with targeted introgression of domestication and adaptation alleles from underutilized germplasm will be essential to generate broadly adapted, yet genetically rich, base populations for both temperate and tropical regions. At the same time, integrating SNP-based diversity analyses with multi-omics and advanced computational approaches will deepen understanding of how genetic variation translates into phenotype. Combining genome-wide SNP data with transcriptomics, metabolomics, epigenomics, and high-throughput phenotyping, together with AI-driven modeling, can resolve causal genes and networks underlying yield, stress resilience, and quality traits and refine models of genotype-by-environment interaction. Large consolidated variant resources and curated mutant libraries derived from millions of SNPs and InDels offer powerful starting points for reverse genetics and genome editing to validate candidate alleles and engineer ideal haplotypes. Continued development of low-cost, breeder-friendly SNP panels tailored to regional germplasm, combined with training and infrastructure for MAS, GS, and GWAS in under-resourced programs, will help translate genomic diversity knowledge into practical genetic gain worldwide. Together, these directions point toward a future in which global soybean improvement is driven by coordinated, data-rich exploitation of the full spectrum of genomic diversity. Acknowledgments Thanks to the reviewers for providing detailed comments and guidance on the manuscript of this study. The reviewers’ keen insights into the issues and attention to detail have greatly benefited the authors.
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