Legume Genomics and Genetics 2026, Vol.17, No.1, 49-67 http://cropscipublisher.com/index.php/lgg 57 mass, and related traits across multiple environments (Ravelombola et al., 2021). In several studies, stable QTL or SNP-based haplotypes co-regulate seed yield and component traits such as 100-seed weight or seeds per plant, reflecting pleiotropy or tight linkage and clarifying trade-offs among traits. High-diversity association panels encompassing landraces, elite cultivars, and exotic accessions enhance the power to detect such loci and confirm that exotic and wild-derived alleles can increase yield or specific components in elite backgrounds (Diers et al., 2018). Thus, genomic diversity, when captured with dense SNP markers, directly translates into exploitable allelic variation for yield improvement and guides the design of heterotic and complementary crossing schemes. 5.2 The relationship between genetic diversity and stress tolerance traits Genomic diversity is also crucial for buffering soybean against biotic and abiotic stresses, with SNP-based GWAS increasingly clarifying the genetic bases of stress tolerance traits. High-density SNP genotyping of diverse germplasm panels has identified loci and candidate genes associated with root system architecture, which underlies nutrient uptake efficiency and tolerance to drought and other climate-related stresses (Kim et al., 2023). For example, GWAS using 180K or SLAF-seq SNP datasets in landraces and spring soybean panels detected over 100 significant loci for root and shoot traits, and prioritized candidate genes whose expression levels correlate with root branching and early seedling vigor, traits strongly linked to resilience under low-input or stressful environments. Similarly, genome-wide analyses of seed flooding tolerance at germination identified SNPs and hub genes associated with electrical conductivity, germination rate, root length, and shoot length under flooding, revealing allelic variants that confer enhanced stress tolerance and can be pyramided by marker-assisted breeding (Sharmin et al., 2021). Resistance to major diseases such as soybean mosaic virus (SMV) also depends on standing genomic variation at resistance loci. GWAS of global or regional panels challenged with SMV strains uncovered multiple resistance loci across chromosomes and pinpointed candidate genes such as Glyma.04G086700, encoding an LRR protein kinase involved in pathogen recognition, with distinct haplotypes explaining differential resistance responses among accessions (Zhao et al., 2025). Population structure analyses in these panels indicate that specific resistance alleles or haplotypes are often enriched in particular geographic or breeding subgroups, emphasizing the need to sample broadly to capture the full spectrum of stress-related diversity (Sharmin et al., 2021). Collectively, these findings demonstrate that maintaining and utilizing genomic diversity—particularly in landraces, wild relatives, and regionally adapted varieties—provides the allelic reservoir necessary for breeding soybean cultivars resilient to current and emerging stresses. 5.3 Applications of SNP markers in genome-wide association studies (GWAS) SNP markers form the backbone of modern GWAS in soybean and have transformed understanding of the genetic architecture of yield, domestication, and adaptive traits. High-density arrays (e.g., 50K–180K SoyaSNP) and GBS or SLAF-seq platforms routinely generate tens to hundreds of thousands of polymorphic SNPs across germplasm panels, providing sufficient marker density for genome-wide coverage and fine mapping of loci through linkage disequilibrium (Ravelombola et al., 2021). GWAS using mixed-model and multilocus approaches (e.g., MLM, MLMM, FarmCPU, BLINK) have identified numerous SNPs and quantitative trait nucleotides for seed yield, maturity, plant height, seed weight, pod and seed number, root traits, domestication-related traits, and stress responses (Mandozai et al., 2021). Many significant SNPs co-localize with known QTL or cloned genes (e.g., E-loci for maturity, Dt1 for plant height, pod-shattering genes), while others represent novel regions underlying complex trait variation or domestication signatures (Sonah et al., 2015). Beyond single-marker tests, haplotype-based GWAS and integrative models extend the utility of SNP datasets. Haplotype analyses refine association signals, identify stable multi-SNP blocks with strong effects across environments, and reveal pleiotropic haplotypes affecting multiple agronomic traits (Bhat et al., 2022). Structural equation model-based GWAS further decomposes SNP effects into direct and indirect components along causal trait networks, clarifying how genomic regions simultaneously influence yield components such as pod number, grain number, and seed weight (Suela et al., 2025). Many GWAS also couple SNP discovery with genomic
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