Principal Component and Cluster Analyses as Tools in the Assessment of Genetic Diversity for Late Season Cauliflower Genotypes
Saba Aleem1*, Mehvish Tahir2, Iram Sharif3, Muqadas Aleem4, Muhammad Najeebullah2, Ali Nawaz1, Amina Batool1, Muhammad Imran Khan1 and Waheed Arshad1
Genetic diversity is the baseline of any breeding program as determining the variability of germplasm is an indispensable assist for crop improvement strategies. Multivariate analyses such as cluster and principal component analysis measures the amount of genetic diversity in respect of several charaters and asseses the relative contribution of different traits to the total variation. In the present study, nineteen genotypes were evaluated for presence of genetic diversity through principal component and cluster analyses. The experiment was designed in randomized complete block design with two replications at the experimental area of Vegetable Research Institute, Faisalabad, Pakistan in 2019. In the principal component analysis, first three principal components had eigenvalue >1 and contributed 80.86% of the variation among the genotypes. Traits i.e. plant weight, curd weight, curd yield, leaf length, plant height to extreme, and the number of leaves contributed significant positive component loading to these principal components (PCs). Biplot analysis among the first two PCs found the Casper RZ, AA Cauli-08, and Snow queen as diverse genotypes. Cluster analysis based on Euclidian distance grouped the genotypes into 3 distinct clusters. Some of the genotypes that have narrow genetic base were grouped into a similar cluster. Based on these results, it may be concluded that some of the genotypes are higly diverse while most of the genotypes are similar in nature. Genotypes from the distinct cluster should be used for obtaining diverse recombinants in segregating generations, exploiting heterosis, and broaden the genetic base of the cauliflower germplasm.