Evaluation of Olives Cultivated in Southern Italy by Simple Sequence Repeat Markers

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  • 1 Consiglio per la Ricerca e la Sperimentazione in Agricoltura–Centro di Ricerca per l'olivicoltura e l'industria olearia (CRA-OLI), C. da Li Rocchi, 87036 Rende (CS), Italy; and Consiglio per la Ricerca e la Sperimentazione in Agricoltura–Centro di Ricerca per le produzioni foraggere e lattiero-casearie (CRA-FLC), Viale Piacenza, 26900 Lodi (LO), Italy
  • 2 Consiglio per la Ricerca e la Sperimentazione in Agricoltura–Centro di Ricerca per l'olivicoltura e l'industria olearia (CRA-OLI), C. da Li Rocchi, 87036 Rende (CS), Italy

Olive (Olea europaea L.) is a species of great economic importance in the Mediterranean basin. Italy is very important for the olive industry; in fact, olive's genetic patrimony is very rich and characterized by an abundance of cultivars. At present, the majority of ancient landraces are vegetatively propagated by farm. It is likely that the number of cultivars is underestimated because of inadequate information on minor local cultivars that are widespread in different olive-growing areas. The existence of many cultivars reinforces the need for a reliable identification method. It is important to improve the ex situ plant germplasm collection and fairly to characterize all cultivars for future breeding programs. In the present report, we used 11 loci microsatellites to characterize 211 olive cultivars of an olive collection cultivated in six regions of southern Italy. These regions represent the major area for olive cultivation in Italy and have a strategic geographical location in the Mediterranean basin. The dendrogram obtained, using the unweighted pair group method with arithmetic mean clustering algorithm, depicts the pattern of relationships between the studied cultivars. There is a clear structuring of the variability relative to the geographic origin of olive cultivars. This work, for the very high number of the Italian olive cultivars analyzed, highlights the degree and distribution of genetic diversity of this species for better exploitation of olive resources and for the design of plant breeding programs. Besides, the use of molecular markers, like simple sequence repeats, is imperative to build a database for cultivar analysis, for traceability of processed food, and for appropriate management of olive germplasm collections.

Abstract

Olive (Olea europaea L.) is a species of great economic importance in the Mediterranean basin. Italy is very important for the olive industry; in fact, olive's genetic patrimony is very rich and characterized by an abundance of cultivars. At present, the majority of ancient landraces are vegetatively propagated by farm. It is likely that the number of cultivars is underestimated because of inadequate information on minor local cultivars that are widespread in different olive-growing areas. The existence of many cultivars reinforces the need for a reliable identification method. It is important to improve the ex situ plant germplasm collection and fairly to characterize all cultivars for future breeding programs. In the present report, we used 11 loci microsatellites to characterize 211 olive cultivars of an olive collection cultivated in six regions of southern Italy. These regions represent the major area for olive cultivation in Italy and have a strategic geographical location in the Mediterranean basin. The dendrogram obtained, using the unweighted pair group method with arithmetic mean clustering algorithm, depicts the pattern of relationships between the studied cultivars. There is a clear structuring of the variability relative to the geographic origin of olive cultivars. This work, for the very high number of the Italian olive cultivars analyzed, highlights the degree and distribution of genetic diversity of this species for better exploitation of olive resources and for the design of plant breeding programs. Besides, the use of molecular markers, like simple sequence repeats, is imperative to build a database for cultivar analysis, for traceability of processed food, and for appropriate management of olive germplasm collections.

The olive tree (Olea europaea L. subsp. europaea) is one of the oldest cultivated plants, and its fruit has been used for nourishment for more than 5000 years in the Mediterranean regions where it originated. Olive is one of the Mediterranean basin's main crops with great cultural and economic importance. From a commercial perspective, in the Mediterranean basin grow many olive tree varieties and it produces 99% and consumes 87% of the world's olive oil. Among Mediterranean countries, Italy occupies a very important place in the olive industry being the main exporter of olive oil in the world (Muzzalupo and Perri, 2008).

The genetic diversity existing in the cultivated olive trees is enormous (over 650 cultivars and over 1300 synonyms; Bartolini et al., 2005). It is also likely that this number is underestimated because of inadequate information on minor local cultivars that are widespread in different olive-growing areas. At present, 2600 different olive cultivars have been described (Bartolini et al., 2005). The olive germplasm therefore represents an important reserve of genetic diversity for the Mediterranean basin ecosystem. This undefined genetic nature gives rise to several problems, both for olive nurseries and for the correct estimation of the platforms needed to classify and properly exploit olive products like canned olives and olive oil.

Olive tree germplasm was traditionally evaluated by morphological and phenological parameters. Polymerase chain reaction (PCR) based on DNA markers is a powerful tool for genetic analysis because of its simplicity and ease of handling (Kojima et al., 1998) providing an opportunity for direct comparison and identification of olive tree material independently from environment and/or developmental stages (Martins-Lopes et al., 2007). An ideal molecular marker technique should have the following criteria: 1) to be polymorphic and evenly distributed throughout the genome; 2) to provide adequate resolution of genetic differences; 3) to be simple, quick, and inexpensive; 4) to need small amounts of tissue and DNA samples; and 5) to have linkage to distinct phenotypes (Agarwal et al., 2008). Microsatellites are highly popular genetic markers because of their codominant inheritance, high abundance, enormous extent of allelic diversity, and the ease of assessing simple sequence repeat (SSR) size variation by PCR with pairs of flanking primers (Agarwal et al., 2008; Muzzalupo et al., 2006, 2008; Rekik et al., 2008). Recently, many efforts have been made to develop good and reliable molecular tools such as microsatellite markers that could shed light on olive tree genetics (Carriero et al., 2002; Cipriani et al., 2002; De la Rosa et al., 2002; Diaz et al., 2006; Rallo et al., 2000; Sabino Gil et al., 2006; Sefc et al., 2000). They have been used for cultivar identification for elucidating genetic relationships between several olive cultivars (Muzzalupo et al., 2006, 2008). Microsatellites have revealed themselves to be very useful markers for olive's progenies parentage checking from controlled crossings (Diaz et al., 2006; Rekik et al., 2008).

The systematic collection of olive cultivars in specific catalog fields began in 1997 by CRA–Centro di Ricerca per l'olivicoltura e l'industria olearia (CRA-OLI) of Rende (CS), Italy. To date, more than 400 cultivars have been introduced into the CRA-OLI collection. The list of cultivars has already been published on FAO's web site (http://apps3.fao.org/wiews/olive/oliv.jsp). The goal of such collections is to safeguard all cultivars, and particularly the minor ones, to avoid a loss in genetic diversity and to offer an interesting genetic basis for breeding programs. Apulia, Basilicata, Calabria, Campania, Sardinia, and Sicily are the regions in Italy with the greatest areas of olive growing and have an extremely complex varietal assortment (Muzzalupo and Perri, 2008). These regions are a major area for olive cultivation in Italy and have a strategic geographical location in the Mediterranean basin. In the present report, we used 11 loci microsatellite to characterize 211 olive cultivars present in the CRA-OLI germplasm collection. The present study is an attempt, using SSR markers, to identify, characterize, and establish relationships between geographically related olive tree cultivars.

Materials and Methods

Plant materials.

Young leaves were harvested from 211 plants growing in the CRA-OLI olive germplasm collection in Cosenza, Italy. The olive plants analyzed are autochthonous of six regions of southern Italy: 35 cultivars from Apulia, 28 cultivars from Basilicata, 30 cultivars from Calabria, 43 cultivars from Campania, 20 cultivars from Sardinia, and 55 cultivars from Sicily (Table 1).

Table 1.

Codes of 211 olive cultivars growing in the CRA-OLI olive germplasm collection at Cosenza (Italy).z

Table 1.

DNA extraction.

Young olive leaves were washed by 4% sodium hypochlorite and 0.2 g of plant tissue was ground into liquid nitrogen and DNA extraction was carried out as described by Muzzalupo and Perri (2002). DNA was quantified by H33258 dye incorporation detected by a Hoefer DyNA Quant® 200 fluorometer (Amersham Pharmacia Biotech, Milan, Italy) following the method described by Muzzalupo et al. (2007a).

Microsatellite markers.

Eleven published SSR markers were preselected for their high level of polymorphism and easily scorable patterns. SSRs used are reported in Table 2. SSR amplification was carried out as described by Rekik et al. (2008). PCR products were analyzed using a Bioanalyzer 2100 (Agilent Tecnologies, Waldbronn, Germany) on a DNA 500 LabChip (Muzzalupo et al., 2007b).

Table 2.

Simple sequence repeat amplification products observed among the 211 olive cultivars.z

Table 2.

Data analysis

Data were processed using POPGENE 32 software (Yeh and Boyle, 1997). The software allowed calculation of the number of alleles and their frequency. The heterozygosity, both observed (Ho) and expected (He), was calculated using the same software. The expected heterozygosity (He) of each locus was calculated according to the formula He = n(1 – Σpi2)/(n-1), where pi is the frequency of the ith allele and n is the number of gene copies in the sample for the given locus (Nei and Roychoudhury, 1974). Deviations of observed heterozygosity values from Hardy-Weinberg expectations were analyzed using the program Genepop 3.4 (Raymond and Rousset, 2003). To determine significance levels for this test, sequential Bonferroni adjustments were used. The probability of null alleles was estimated according to the formula of Brookfield (1996): r = (HeHo)/(1 + He). The SSR loci discrimination power was calculated according to Brenner and Morris (1990).

The alleles detected for each microsatellite were recorded into a data matrix of presence (1) and absence (0) of bands (each allele representing a band). The estimations of genetic similarity based on calculation of Jaccard's similarity coefficients among 211 olive cultivars are analyzed using NTSysPc program version 2.02 (Rohlf, 1998). Finally, a tree was inferred using the unweighted pair group method using an arithmetic average (UPGMA) clustering algorithm. The cophenetic correlation coefficient was calculated, and Mantel's test (Mantel, 1967) was performed to control the goodness of fit of a cluster analysis for the matrix on which it was based.

A likelihood-based parentage analysis was performed using FaMoz software (Di Vecchi Staraz et al., 2007; Gerber et al., 2003) to find potential parent–offspring or sibling relationships among cultivars. We calculated exclusion and identity probabilities based on the allele frequencies of the 11 microsatellites. Simulations, calculations, and tests were done considering no genotyping errors. Because genotypes represent adult trees growing in different regions, the parent–offspring relationship can be difficult to distinguish from sibship.

Results and Discussion

Molecular characterization and discriminating capacity.

A total of 211 olive cultivars, chosen from the six regions of southern Italy (Apulia, Basilicata, Calabria, Campania, Sardinia, and Sicily), were genotyped with 11 SSR loci. A total of 75 alleles over 11 loci were produced, ranging from three at UDO01 to 12 alleles at UDO39, with an average of 6.82 alleles per locus (Table 2). This is comparable to the number of alleles among olive cultivars reported by Charafi et al. (2008), but somewhat lower than that published by Sarri et al. (2006), probably because it includes a large number of foreign cultivars and for a different set of SSR loci.

The observed heterozygosity for the 211 olive cultivars ranged from 0.085 at UDO03 to 0.901 at GAPU71B with a mean value of 0.590. The expected heterozygosity ranged from 0.568 at UDO01 to 0.840 at DCA09 with a mean value of 0.741. The probability of occurrence of null alleles values ranged from 0 to 0.33 at UDO03. At four of the 11 loci, the observed heterozygosity was higher than the expected values under “Hardy Weinberg” equilibrium. Although loci GAPU59, GAPU71A, UDO028, and DCA18 presented a lower heterozygosity value than expected, the differences between theoretical and observed values were not significant. In contrast, the level of heterozygosity was significantly lower than expected at the UDO001, UDO003, and UDO39 loci (P < 0.001). A possible explanation of such a deficit is the occurrence of null alleles at these loci because the corresponding probability is highly significant (Table 2).

For each SSR locus, discrimination power (PD) values ranged from 0.941 at DCA09 to 0.638 at UDO001 (Table 3). To assess the ability of the SSR markers to discriminate among cultivars, the combined PD was calculated first of all 22 markers and then the PD of all 10 marker combinations (removing locus of low PD, one at a time), and so on. The combined PD of all loci is 0.9999999998, which means that the probability of finding two cultivars with the same genotype combination for the 11 SSR markers is one over one billion, indicating the very high discrimination of the marker system used. To get an overall picture of discrimination power of combined loci, the PD of all the possible combinations of two and three loci were determined. The DCA09-UDO28 pairs were the most discriminating (0.9960); the other 10 best pair combinations were all higher than 0.99. The PD values of the best three-locus combinations were all higher than 0.999; DCA09-UDO28-GAPU103A was the most discriminative (0.9997).

Table 3.

The combined discriminating power (PD) of all loci analyzed and for their best combinations.

Table 3.

The lowest allelic frequency (0.0024) was observed for alleles 228 bp of GAPU71a in ‘Cellina di Nardò’ (Apulia region), 259 bp of GAPU71a in ‘Bottone di Gallo’ (Sicily region), 202 bp at UDO03 in ‘Moresca’ (Sicily region), and 243 bp at UDO39 in ‘Cornula’ (Apulia region) (Table 4). These four alleles were observed in one copy in the whole set of studied cultivars analyzed. Alleles 143 bp of UDO01 showed the relatively highest frequency (0.5877). The shortest allele among the 11 polymorphic loci was allele 108 bp at UDO39, whereas the longest was 259 bp at GAPU71A.

Table 4.

Allele size (bp) and frequencies (in italics) for each simple sequence repeat locus in 211 olive genotypes.

Table 4.

Identifying molecular profiles and genetic diversity.

Using 11 microsatellite loci, four cases of homonymy were identified. Olive trees under the ‘Faresana 1’ and ‘Faresana 2’ denomination (Basilicata region) were classified into two molecular profiles, which were differentiated by six SSR alleles. ‘Ogliarola del Bradano 1’ and ‘Ogliarola del Bradano 2’ (Basilicata region) were differentiated by 10 SSR alleles; ‘Racioppa 1’ and ‘Racioppa 2’ (Basilicata and Campania regions, respectively) were classified into two molecular profiles, which were differentiated by 13 SSR alleles; finally, ‘Romanella 1’ and ‘Romanella 2’ (Basilicata and Calabria regions, respectively) were differentiated by 15 SSR alleles.

Synonyms included cultivars with the same profile for all SSR examined and cultivar pairs different from each other for one or two alleles (La Mantia et al., 2005). In the case of slightly different molecular patterns, we considered that such differences were too few to have originated through sexual reproduction, olive being an outcrossing species with a highly heterozygous genome. Given the high number of cultivars (211 cultivars) and high number of molecular markers (75 allele SSRs), we decided to treat the genotype with highest values of similarity (≥ 0.941, different for one allele) as possible cases of synonyms. Parent–offspring relations were found for ‘Giarraffa’ and ‘Pizzo di Corvo’, ‘Nera di Oliena’ and ‘Paschixedda’, and ‘Ogliarola Messinese’ and ‘Iacona’; and these three cultivar pairs differ by only one allele (Table 5). However, given the high polymorphism of our markers and the reproductive biology of the olive tree, rather than parent–offspring, it is far more probable that those individuals are clones that have recently diverged by mutation or that the divergent allele is the result of genotyping error.

Table 5.

Synonyms identified by simple sequence repeat fingerprinting.

Table 5.

Ten different olive cultivar pairs are genetically indistinguishable from one another (Table 5), potentially representing cases of synonymy. Many cultivars were identified as possible cases of synonyms and are reported to Table 5; the remainder are clearly separated from each other. These possible cases of synonymy are in agreement with previously studies based on morphological descriptors and molecular marker systems (Lombardo et al., 2004; Muzzalupo et al., 2006, 2007a, 2008; Sarri et al., 2006). Consequently, it is possible to say that the SSRs used in this study are able to distinguish 199 unique genotypes (94% of the cultivars analyzed). Therefore, synonyms characterization is very important to avoid genotype redundancy to maximize genetic diversity in the Italian olive germplasm collection.

Phenetic analysis of the genetic polymorphism carried out by means of the UPGMA clustering algorithm helped us to clarify the genetic relationships among 211 olive cultivars. The phenogram reported in Figure 1 depicts the pattern of relationships between the studied cultivars. The first cluster includes 46 olive cultivars and it is composed of: 38 cultivars from the Sicily region, three from the Apulia region, three from the Basilicata region, one from the Calabria region, and one from the Campania region. The second cluster of 16 olive cultivars did not show a clear structure for the Italian geographic origin and was composed of: eight from the Apulia region, six from the Sicily region, and one from the Calabria region. The third cluster includes 26 olive cultivars and it is composed of: 11 from the Basilicata region, seven from the Sicily region, three from the Calabria region, three from the Campania region, one from the Apulia region, and one from the Sardinia region. The fourth cluster includes 20 olive cultivars and it is composed of: 15 from the Apulia region, four from the Basilicata region, and one from the Sicily region. The fifth cluster includes eight olive cultivars, it does not show a clear structure for the Italian geographic origin; it is composed of: three from the Basilicata region, one from the Apulia region, one from the Calabria region, one from the Campania region, one from the Sardinia region and finally, one from the Sicily region. The sixth cluster includes 31 olive cultivars prevalently from the Calabria region. In fact, it is composed of: 24 from the Calabria region, three from the Basilicata region, two from the Apulia region, and two from the Sicily region. The seventh cluster containing 10 olive cultivars is composed of: five from the Apulia region, four from the Campania region, and one from the Basilicata region. The eighth cluster includes 39 olive cultivars prevalently from the Campania region. In fact, it is composed of: 29 from the Campania region, seven from the Sardinia region, and three from the Basilicata region. The ninth cluster containing 16 olive cultivars is composed of: 11 from the Sardinia region and five from the Campania region.

Fig. 1.
Fig. 1.

Unweighted pair group method with arithmetic mean dendrogram obtained from simple sequence repeat data for 211 olive genotypes.

Citation: HortScience horts 44, 3; 10.21273/HORTSCI.44.3.582

There is a clear structuring of the variability relative to the geographic origin of olive cultivars. Nine distinct clusters of olive cultivars were clearly recognizable and seven of these were clearly link to a geographically defined area. In fact, 38 (69%) cultivars of the 55 total autochthonous of the Sicily region were contained in Cluster 1; 24 of 30 (80%) olive cultivars autochthonous of the Calabria region were contained in Cluster 5; 29 of 43 (67%) cultivars from the Campania region were contained in Cluster 8; 22 of 35 (63%) cultivars from the Apulia region were contained in Clusters 4 and 7; 11 of 20 (55%) cultivars from the Sardinia region were contained in Cluster 9; and finally, 11 (39%) cultivars of the 28 total autochthonous of the the Basilicata region were contained in Cluster 3. The two clusters of cultivars that clustered independently (Clusters 2 and 6) did not show a clear structure for the Italian geographic origin.

Conclusions

This study shows that the use of molecular markers like SSRs is a very useful tool to build a database available for cultivar analysis and for olive germplasm collection management. An important question affecting the management of the cultivated olive germplasm is related to the possibility of distinguishing the area of origin of each cultivar. Such information could help safeguard local cultivars and could be used for tracing purposes to select genotypes that are better adapted to specific environmental conditions. Thus, the obtained data allow us, for the first time, a proper evaluation of the 211 olive cultivated in six different regions of southern Italy by 11 loci SSRs and attempt to assign a correlation between the olive cultivars and their geographic origin. The presence of such cases of identity among cultivars might be attributable to their ancient diffusion into different cultivation areas where growers might have given them different local names. The dendrogram based on UPGMA cluster analysis depicts the pattern of relationships between the studied cultivars based on SSR markers.

Analysis of SSRs in cultivars of the Italian germplasm collection allowed us to construct a molecular catalog that can be used to compare the molecular pattern of the various cultivars as well as other samples of unknown origin, avoiding the collection of redundant genetic entities.

This work, for the very high number of the Italian olive cultivars analyzed, highlights the degree and distribution of genetic diversity for a better exploitation of olive resources and for the design of plant breeding programs. The use of molecular markers like SSRs, in addition to other information, is imperative to build a database for cultivar analysis, for traceability of processed food (olive oil), and for appropriate management of olive germplasm collections.

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Contributor Notes

This research was supported by the Italian “OLVIVA,” “RGV FAO,” and “RIOM” Projects granted by MIPAAF.

We thank N. Lombardo for his endless efforts in the individuation and collection of olive germplasms. We also thank M. Pellegrino, G. Godino, A. Saijad, M. A. Caravita, A. Salimonti, and R. Falabella, who helped with DNA extractions and with molecular techniques. We thank M. Pellegrino, G. Godino, A. Ciliberti, and A. Madeo for their help with collecting plant material in the CRA-OLI olive collection.

To whom reprint requests should be addressed; e-mail innocenzo.muzzalupo@entecra.it.

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    Unweighted pair group method with arithmetic mean dendrogram obtained from simple sequence repeat data for 211 olive genotypes.

  • Agarwal, M., Shrivastava, N. & Padh, H. 2008 Advances in molecular marker techniques and their applications in plant sciences Plant Cell Rpt. 27 617 631

    • Search Google Scholar
    • Export Citation
  • Bartolini, G., Prevost, G., Messeri, C. & Carignani, G. 2005 Olive germplasm: Cultivars and world-wide collections Dec. 2008 <http://www.apps3.fao.org/wiews/olive/oliv.jsp>.

    • Export Citation
  • Brenner, C. & Morris, J. 1990 Paternity index calculations in single locus hypervariable DNA probes: Validation and other studies 21 53 Proceedings for the International Symposium on Human Identification Promega Corporation Madison, WI

    • Search Google Scholar
    • Export Citation
  • Brookfield, J.F.Y. 1996 A simple new method for estimating null allele frequency from heterozygote deficiency Mol. Ecol. 5 453 455

  • Carriero, F., Fontanazza, G., Cellini, F. & Giorio, G. 2002 Identification of simple sequence repeats (SSRs) in olive (Olea europea L.) Theor. Appl. Genet. 104 301 307

    • Search Google Scholar
    • Export Citation
  • Charafi, J., El Meziane, A., Moukhli, A., Boulouha, B., El Modafar, C. & Khadari, B. 2008 Menara gardens: A Moroccan olive germplasm collection identified by SSR locus-based genetic study Genet. Resources Crop Evol. 55 893 900

    • Search Google Scholar
    • Export Citation
  • Cipriani, G., Marrazzo, M.T., Marconi, R., Cimato, A. & Testolin, R. 2002 Microsatellite markers isolated in olive (Olea europaea L.) are suitable for individual fingerprinting and reveal polymorphism within ancient cultivars Theor. Appl. Genet. 104 223 228

    • Search Google Scholar
    • Export Citation
  • De la Rosa, R., James, C.M. & Tobutt, K.R. 2002 Isolation and characterization of polymorphic microsatellites in olive (Olea europaea L.) and their transferability to other genera in the Oleaceae Mol. Ecol. Notes 2 265 267

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