Classification Binary Trees with SSR Allelic Sizes: Combining Regression Trees with Genetic Molecular Data in Order to Characterize Genetic Diversity between Cultivars of Olea europaea L.

Abstract

During recent centuries, cultivated olive has evolved to one of the major tree crops in the Mediterranean Basin and lately expanded to America, Australia, and Asia producing an estimated global average value of over USD 18 billion. A long-term research effort has been established with the long-term goal to preserve biodiversity, characterize agronomic behavior, and ultimately utilize genotypes suitable for cultivation in areas of unfavorable environmental conditions. In the present study, a combination of 10 simple sequence repeat (SSR) markers with the classification binary tree (CBT) analysis was evaluated as a method for discriminating genotypes within cultivated olive trees, while Olea europaea subsp. cuspidata was also used as an outgroup. The 10 SSR loci employed in this study, were highly polymorphic and gave reproducible amplification patterns for all accessions analyzed. Genetic analysis indicated that the group of SSR loci employed was highly informative. A further analysis revealed that two sub populations and pairwise relatedness gave insight about synonymies. In conclusion, the CBT method which employed SSR allelic sizes proved to be a valuable tool in order to distinguish olive cultivars over the traditional unweighted pair group method with the arithmetic mean (UPGMA) algorithm. Further research which will combine phenotyping characterization of olive germplasm will have the potential to enable the utilization of existing, and breeding of new, superior cultivars.