Legume Genomics and Genetics 2026, Vol.17, No.1, 1-13 http://cropscipublisher.com/index.php/lgg 10 imputation of missing data, the LD-kNNi algorithm was used (Money et al., 2015). After filtering and imputation, SNPs were distributed across 20 soybean chromosomes (Figure 3), with positions shown equidistantly, not reflecting actual chromosome size. Figure 3 Number and equidistance of SNPs across the 20 soybean chromosomes 4.6 Population analyses Genetic diversity was estimated using expected heterozygosity (He) and observed heterozygosity (Ho) across loci. The genetic distance between populations was calculated using the Prevosti (1975) (Prevosti, Ocaña and Alonso, 1975) method and the resulting distance matrix was employed in a hierarchical clustering analysis using the unweighted pair group method with arithmetic mean (UPGMA). The starting point for the clustering was the smallest distance and this method was chosen due its cophenetic correlation, estimated by the Mantel test with 10,000 permutations to construct a dendrogram. The Mantel test was used to assess the fit between the hierarchical clustering and the original dissimilarity matrix. After clustering, the cutoff point for group formation was determined using the Mojena method. To evaluate the genetic relationships among soybean lines and the variation within populations, a pairwise distance matrix was calculated using the Pairwise method (Paradis, 2011). The resulting genetic distance matrix was then used to conduct a principal component analysis (PCA) to visualize the genetic variation. The PCA was plotted on a Cartesian plane, considering the first two principal components, where the specific explanatory capacity was determined by the eigenvalues. Confidence ellipses were added to the PCA plot, assuming a multivariate t-distribution at a 0.05 probability level. 5 Conclusions This study reveals the existence of both phenotypic and genotypic intracultivar variation among the assessed soybean cultivars. The degree of variation observed differs, with cultivars P98Y11 and NA5909 exhibiting higher levels of diversity, while NS7000 presents a lower level of variation. This reflects the fact that the genome is not static but rather dynamic, constantly subject to genetic and environmental influences that shape diversity. In addition, these results strongly demonstrated that intra-cultivar variations offer valuable opportunities in soybean plant breeding programs as a breeding tool.
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