Genetic Diversity Assessment of Soybean Genotypes Using D² and Principal Component Analysis for Breeding Advancements

Abstract

Soybean (Glycine max L. Merrill), major leguminous crop rich in protein and oil, plays a critical role in human nutrition and sustainable agriculture. Genetic diversity analysis is vibrant to identify better parents for developing high-yielding cultivars with improved agronomic traits. The present investigation was undertaken during Kharif 2024 at the Zonal Agricultural Research Station, Morena, RVSKVV, Gwalior, M.P., India to evaluate genetic divergence among 60 elite soybean genotypes. The experiment was laid out in a Randomized Block Design with two replications, and observations were recorded on 13 quantitative traits. Mahalanobis’ D² statistic and Tocher’s clustering method were employed to estimate genetic divergence, while Principal Component Analysis (PCA) was used to identify main contributors of traits to create variability. Cluster analysis grouped the genotypes into 14 distinct clusters, with maximum intra-cluster distance observed in Cluster IV (139.66) while highest inter-cluster divergence (D² = 456.59) between Clusters XIII and VIII, suggesting potential for creating superior recombinants through hybridization. Cluster VIII displayed the highest mean seed yield per plant (19.56 g), along with higher values for numbers of pods per plant and seeds per plant, and harvest index. PCA revealed five principal components with eigenvalues >1.0, collectively accounting for 77.39% of the total variation. Genotypes like JS-21-17, Cat492A, NRC-142, and AUKS-21-5 were identified as promising based on high PC scores and desirable agronomic traits. The study emphasizes the utility of multivariate analysis in identifying genetically diverse and agronomically superior soybean genotypes. These genotypes hold significant promise as parental lines in future breeding programmes aimed to enhance yield potential, stress tolerance, and overall productivity in soybean.