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Genetic diversity was assessed in 48 popular varieties of tetraploid cotton from each cultivated zone of India using 68 SSR markers distributed across linkage groups. The markers produced a total of 144 alleles with an average of 2.19 per locus. The polymorphism information content (PIC) ranged from 0.04 to 0.57 with a mean of 0.23 indicating lesser diversity in the studied material. Jaccard’s similarity index based neighbourhood joining cluster analysis grouped the genotypes into three major clusters, each of which was further classified into sub-groups. Inconsistencies were observed between the clusters and known pedigree of the cultivars. A narrow genetic base was also revealed among the cotton cultivars. The SSR markers revealed a genetic similarity of 73% among the varieties studied. The DNA fingerprint developed using a selected set of 14 markers showed a probability of identical match of 2.47×10-3 with high goodness of fit (r2=0.86). The identified markers have great potential in DNA fingerprinting in cotton which in future could be integrated with DUS data descriptors for effective cultivar identification and differentiation.

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Morphological characterization of 47 tetraploid cotton varieties cultivated in different zones of India was carried out over two seasons. The lay out followed randomized block Design and evaluation was done using 36 DUS descriptors in two replications. The visual characters showed uniform expression within the variety for two consecutive years indicating that they were uniform and stable in expression. Eleven out of 37 traits were monomorphic among the varieties. The remaining 26 characters were used for Principal Component Analysis to find the contribution of traits towards total variability. The PCA identified a total of 10 Components with Eigen values more than 1 contributing to a cumulative 77.74 % variability. The first component (PC1) exhibited maximum variability and highly correlated with traits such as leaf shape and petal spot which are also included in the grouping characters of DUS test guideline. The scatter diagram drawn using first two principle components with highest variability as well as the hierarchical cluster analysis performed using all the ten components distinctly classified genotypes in a consistent manner. The grouping of genotypes was attributed to relatively high contribution from few characters or variables which had high positive loadings, distributed among first two components rather than small contribution from each character.