High Genetic Diversity Detected in Olives beyond the Boundaries of the Mediterranean Sea

Background

Olive trees (Olea europaea subsp. europaea var. europaea) naturally grow in areas spanning the Mediterranean basin and towards the East, including the Middle East. In the Iranian plateau, the presence of olives has been documented since very ancient times, though the early history of the crop in this area is shrouded in uncertainty.

Methods

The varieties presently cultivated in Iran and trees of an unknown cultivation status, surviving under extreme climate and soil conditions, were sampled from different provinces and compared with a set of Mediterranean cultivars. All samples were analyzed using SSR and chloroplast markers to establish the relationships between Iranian olives and Mediterranean varieties, to shed light on the origins of Iranian olives and to verify their contribution to the development of the current global olive variation.

Results

Iranian cultivars and ecotypes, when analyzed using SSR markers, clustered separately from Mediterranean cultivars and showed a high number of private alleles, on the contrary, they shared the same single chlorotype with the most widespread varieties cultivated in the Mediterranean.

Conclusion

We hypothesized that Iranian and Mediterranean olive trees may have had a common origin from a unique center in the Near East region, possibly including the western Iranian area. The present pattern of variation may have derived from different environmental conditions, distinct levels and selection criteria, and divergent breeding opportunities found by Mediterranean and Iranian olives.These unexpected findings emphasize the importance of studying the Iranian olive germplasm as a promising but endangered source of variation.     

Machine Learning Based Classification of Microsatellite Variation: An Effective Approach for Phylogeographic Characterization of Olive Populations

Finding efficient analytical techniques is overwhelmingly turning into a bottleneck for the effectiveness of large biological data. Machine learning offers a novel and powerful tool to advance classification and modeling solutions in molecular biology. However, these methods have been less frequently used with empirical population genetics data. In this study, we developed a new combined approach of data analysis using microsatellite marker data from our previous studies of olive populations using machine learning algorithms. Herein, 267 olive accessions of various origins including 21 reference cultivars, 132 local ecotypes, and 37 wild olive specimens from the Iranian plateau, together with 77 of the most represented Mediterranean varieties were investigated using a finely selected panel of 11 microsatellite markers. We organized data in two ‘4-targeted’ and ‘16-targeted’ experiments. A strategy of assaying different machine based analyses (i.e. data cleaning, feature selection, and machine learning classification) was devised to identify the most informative loci and the most diagnostic alleles to represent the population and the geography of each olive accession. These analyses revealed microsatellite markers with the highest differentiating capacity and proved efficiency for our method of clustering olive accessions to reflect upon their regions of origin. A distinguished highlight of this study was the discovery of the best combination of markers for better differentiating of populations via machine learning models, which can be exploited to distinguish among other biological populations.