The combination of data as a strategy to determine the diversity of tomato subsamples

The estimation of genetic diversity by qualitative, quantitative, and molecular data and their combination are important in characterizing germplasm collections for pre-breeding purposes, mainly for the identification of divergent parents. For this purpose, we assessed a population of 94 tomato subsamples from UFV Vegetable Germplasm Bank (BGH-UFV) using 10 ISSR markers and agronomic data (three qualitative and six quantitative traits). Data revealed the existence of genetic diversity in germplasm considering the three data classes. Principal coordinates analysis (PCoA) confirmed the genetic variability of the subsamples, explaining 27% of the variability in the first two PCoAs. The Bayesian based clustering analyses using the STRUTURE software verified the existence of a structured population, with three populations. The mantel test for the correlation produced by the three data classes showed highly significant correlation (r = 0.31, P<0.001) among quantitative and molecular data. The Tocher method of clustering for each dissimilarity matrices showed that the clustering patterns were dependent on the data classes. According to the results we found, it is possible to predict the best combinations of parents that can provide maximum gain in a breeding program. Besides the combine use of the quantitative, qualitative and molecular data, using multivariate and Bayesian method of clustering is an efficient method to study the genetic diversity of tomato plants in the germplasm bank.