Morphological descriptor-based multivariate approaches for comprehensive genetic diversity analysis in soybean (Glycine max L. Merrill)

Assessment of genetic diversity in germplasm collections is crucial for effective genetic improvement in soybean (Glycine max L. Merrill). The present study evaluated 165 soybean germplasm lines, including five checks, at JNKVV, Jabalpur, during Kharif 2022, Rabi-Summer 2023 and Kharif 2023 to characterize morphological variability based on the DUS guidelines. A thorough statistical analysis involving phylogenetic assessment, hierarchical clustering and principal component analysis (PCA) was accomplished to elucidate the genetic divergence and trait-based associations among the genotypes. Phylogenetic analysis delineated the germplasm into six distinct groups, indicating substantial genetic differentiation. Genotypes NRC 181, VLS 89, JS 20-34 and NRC 138 formed an independent cluster, suggesting unique allelic variation. Hierarchical clustering established this genetic stratification, identifying distinct sub-clusters associated with differential morphological traits. PCA revealed five principal components explaining 55.85 % of the total phenotypic variation, with PC1 contributing 17.3 %, predominantly associated with flower colour and hypocotyl anthocyanin pigmentation. Genotypes such as EC 313915, GW 89, CAT 489A and GW 108 exhibited stable performance and significant contributions to multiple trait expressions. The PCA-biplot, based on the first two principal components and overlaid with six clusters, effectively distinguished genotype groups, confirming the robustness of classification. The Mantel test showed a strong correlation (r = 0.6161, p = 0.001) between distance matrices from PCA and hierarchical clustering, validating the consistency of the analysis. The investigation underscores the genetic variability within the evaluated soybean germplasm. It reinforces the importance of integrating morphological characterization with multivariate statistical approaches for strategic genotype selection in soybean improvement programs.