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Article

Intraspecific Genetic Variability of Brassica cretica Lam. (Brassicaceae) Using SSR Markers

by
Efthalia Stathi
1,
Evangelia V. Avramidou
2,
Panayiotis Trigas
3,
Anastasios Katsileros
1,
Ioannis Karavidas
4,
Theodora Ntanasi
4,
Georgia Ntatsi
4,
Penelope J. Bebeli
1 and
Eleni Tani
1,*
1
Laboratory of Plant Breeding and Biometry, Department of Crop Science, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
2
Laboratory of Forest Genetics and Biotechnology, Institute of Mediterranean Forest Ecosystems, Hellenic Agricultural Organization “ELGO-DIMITRA”, Terma Alkmanos, Ilisia, 11528 Athens, Greece
3
Laboratory of Systematic Botany, Department of Crop Science, School of Plant Sciences, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
4
Laboratory of Vegetable Production, Department of Crop Science, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(5), 1201; https://doi.org/10.3390/agronomy15051201
Submission received: 19 March 2025 / Revised: 12 May 2025 / Accepted: 12 May 2025 / Published: 15 May 2025
(This article belongs to the Section Crop Breeding and Genetics)
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Abstract

:
Unraveling the evolutionary history of Brassica L. crops and their wild relatives remains a key challenge in plant evolutionary biology. Brassica cretica is considered the closest living relative of the cultivated B. oleracea. It is mainly distributed in the Aegean Islands and the neighboring mainland regions of Greece and Anatolia, and exhibits extensive phenotypic variability, obscuring its infraspecific classification. In this study, we analyzed five Greek populations of B. cretica and one B. oleracea botanical variety using SSR markers to assess genetic diversity and differentiation. High genetic diversity was detected within natural populations, with a mean of 21.9 alleles per locus and an expected heterozygosity of 0.647. Significant genetic differentiation (Fst = 0.812) revealed the presence of four distinct gene pools, partly supporting the current infraspecific classification of B. cretica. The cultivated plants cluster closely with B. cretica subsp. cretica, supporting the hypothesis of an Eastern Mediterranean origin. Our findings suggest that B. cretica subsp. cretica may have been introduced to suitable habitats or that cultivated plants may have reverted to a feral state in the Peloponnese, given the genetic similarity between populations from Crete and northern Peloponnese. The identified genetic diversity underscores the importance of B. cretica as a genetic resource for breeding programs and highlights the need for conservation, particularly for populations exhibited unique genetic traits.

1. Introduction

Resolving the evolution of the Brassica L. (Brassicaceae) crops and their wild relatives probably represents one of the most fascinating tasks in plant evolutionary biology [1,2,3,4]. Eiseley and Grote [5] already highlighted the parallels between his theory of natural selection and the cultivation practices that shaped the diverse forms of Brassica oleracea L. crops. The domesticated forms of B. oleracea include six major botanical varieties [broccoli (var. italica), brussels sprouts (var. gemmifera), cabbage (var. capitata), cauliflower (var. botrytis), kale (var. acephala), kohlrabi (var. gongylodes)], and 12 additional cultivated crop types [6], representing a unique example of diversification through domestication in the plant kingdom. The global production of B. oleracea crops exceeded 100 million metric tons in 2023, indicating its agricultural and economic significance [7].
The generic delimitation of Brassica remains unresolved. Brassica oleracea crops and their wild relatives, however, constitute the monophyletic B. sect. Brassica within the polyphyletic genus Brassica [1]. The section includes about 13 species and several infraspecific taxa distributed across the Mediterranean Basin and the Atlantic coasts of Europe [8]. Several hypotheses on the progenitors of Brassica oleracea crops have been proposed. Brassica Montana Pourr., B. rupestris Raf., B. cretica Lam., B. incana Ten., and wild B.oleracea have been nominated as single or multiple progenitor species [9,10,11]. Mabry et al. [6], using RNA-seq data of 224 accessions representing 14 B. oleracea crop types and nine potential wild progenitor species concluded that the Aegean endemic B. cretica is the closest living relative of cultivated B. oleracea, supporting an Eastern Mediterranean origin of cultivation. This conclusion is further supported by molecular phylogenetic analyses conducted by Arias and Pires [1] that highlighted the close evolutionary relationship between B. cretica and cultivated B. oleracea, pointing to the Eastern Mediterranean as a center of early domestication and diversification. More recently, the findings of Castillo-Lorenzo et al. [12] have reinforced this hypothesis, providing additional genomic evidence that aligns with the placement of B. cretica as the closest extant wild relative of cultivated B. oleracea, thereby supporting the Aegean region as a primary locus of origin and early selection. Marby et al.also suggested that cultivated plants of this species can revert to a wild-like (feral) state with relative ease. A combination of those results with the evolutionary history of B. oleracea may contribute to a growing body of knowledge on B. oleracea crop domestication that will facilitate continued breeding efforts including adaptation to changing environmental conditions.
B. cretica is distributed in the Eastern Mediterranean (Albania, Greece, Türkiye, Lebanon, and Israel), with most of its populations concentrated in the Aegean Islands and the coastal regions of mainland Greece and Anatolia. The extensive phenotypic variability of B. cretica has led to the classification of multiple subspecies and varieties; however, there is no consensus regarding their taxonomic status. Gustafsson et al. [13] identify two subspecies: B. cretica subsp. cretica and subsp. nivea. In contrast, Snogerup et al. [11] recognize three subspecies: B. cretica subsp. aegaea subsp. cretica, and subsp. laconica. The disjunct distribution of B. cretica in the Middle East has been attributed either to early East Mediterranean trade [14] or to introductions into suitable natural habitats without cultivation [11]. Some populations, within the natural range of the species in the Aegean, may have also resulted from human-mediated dispersal. According to Mabry et al. [6], early forms of B. cretica may have played underappreciated roles in the domestication of B. oleracea crops. Therefore, resolving the domestication story of B. cretica is fundamental for fully understanding the evolutionary history of B. oleracea.
Brassica cretica is an extremely diverse wild relative of cultivated cabbage (B. oleracea), with genetic distances between its populations often exceeding those found between distinct species in the B. villosa/B. rupestris/B. macrocarpa group [15]. This diversity likely results from ‘isolation by distance’ processes, which have shaped significant morphological differences among local populations [13,14,15,16]. As it is a diploid (2n = 18) species, partially self-incompatible, hermaphroditic, and insect-pollinated, the geographical isolation of its populations, along with potential pollinator-driven selection based on flower color differences [17], may contribute to its high genetic diversity. Understanding this diversity is crucial for assessing the species’ adaptability and its potential use in breeding programs aiming at enhancing crop resilience [18].
Βreeders of cole crops (B. oleracea L.) have an interest in utilizing current and emerging PCR-based marker systems to differentiate elite germplasm. Among various systems available for genetic analysis in plants, molecular markers are more efficient, precise, and reliable in discriminating closely related species or cultivars [19,20,21]. SSRs are co-dominant, highly polymorphic PCR-based markers and thus expected to be a very powerful tool in cultivar discrimination. Polymorphic SSR markers have been recognized as a reliable tool for assessing genetic diversity and population structure in Brassica species in many cases [22,23,24] as well as identifying salinity tolerant genotypes [25]. Furthermore, SSR markers identified in cabbage can be applied to ten other wild and cultivated species within the Brassicaceae family. This highlights their potential value in advancing Brassica oleracea research, including genetic mapping, marker-assisted selection (MAS), and comparative genome analyses [21].
The genus Brassica includes three basic genomes—A, B, and C—forming three diploid species: B. rapa (AA), B. nigra (BB), and B. oleracea (CC). These hybridize to produce three allopolyploid species: B. napus (AACC), B. juncea (AABB), and B. carinata (BBCC), as outlined in the Triangle of U model [26]. This study focuses on evaluating the genetic diversity and population structure of five wild B. cretica populations and one B. oleracea variety using ten SSR markers. Unlike previous studies that examined multiple Brassica genomes, this research limits its scope to the C genome, specifically exploring the genetic distance and potential evolutionary connection between B. cretica and cultivated cabbage. The findings aim to identify the most genetically diverse and agriculturally important wild populations, providing insights into future conservation and breeding efforts within the C genome of Brassica crops.

2. Materials and Methods

2.1. Plant Material and DNAExtraction

This study includes five natural B. cretica populations (two B. cretica subsp. aegaea populations originated from Evia and Ymittos, two B. cretica subsp. cretica populations originated from Akrokorinthos and Crete, and one B. cretica subsp. laconica population from Leonidio) from different regions of Greece and one commercial botanical variety of B.oleracea (population F) (Table 1; Figure S1). Ripe seeds from each population were collected in their natural habitats (Table 1; Figure 1) during the physiological ripening period. Seedling preparation was conducted at the greenhouse facilities of the Laboratory of Vegetable Production at Agricultural University of Athens. Specifically, 20 ripe seeds per population were sown on rockwool sheets (AO Plug, Grodan, Roermond, The Netherlands), covered with a thin layer of vermiculite. During germination, seedlings were exposed to ambient light conditions and temperatures ranging from 25 °C to 27 °C during the day, and 17 °C to 19 °C during the night. Additionally, the seedlings were irrigated regularly with a nutrient solution containing 16.4 mM N, 6.6 mM K+, 4.4 mM Ca2+, 2.8 mM Mg2+, 2.75 mM SO42−, 1.3 mM H2PO4−, 20 μΜ Fe (EDDHA), 10 μΜ Mn2+, 6 μΜ Zn2+, 0.8 μΜ Cu2+, 30 μΜ B, 0.7 μΜΜο, resulting in an electrical conductivity and pH of 2.4 dS/m and 5.6, respectively. One hundred and twenty (120) newly developed seedlings (20 per population) were analyzed in total.
Total genomic DNA was extracted from fresh plant tissue which was stored in deep freeze (−80°C) before DNA isolation. DNA was extracted according to classical CTAB protocol [27] with minor technical modifications. DNA concentrations and purity ratios were measured with Nanodrop ND-1000 Spectrophotometer (NanoDrop Technologies, Inc., Wilmington, DE, USA). Then, all individual DNA samples were diluted at the final concentration 50 ng/µL with molecular grade water and stored at freeze −20 °C for PCR analysis.

2.2. Molecular Analysis; PCR Amplification and SSRMarkers

The present study used ten SSR markers (Table 2) to analyze genetic variation namely, Ol10B11, Ol10B01, Ol09A01, Ol10F11, Ni4-B10, sORA26, BN12A, Na10-F06, nga111, MB4 studied/described by [24,28].
PCR parameters were identical to previous studies with some technical modifications. A selection of those primers was based after an initial screening of a larger number of 14 primers during preparative experiments standardizing PCR conditions according to previously used protocols [24,28].
PCR reactions were performed in 96-well plates Nippon Genetics thermal cycler (FastGene Ultra Cycler Gradient, Dueren, Germany) where different PCRs could be performed at the same time in the same gradient machine using different SSR loci with different Tm (Table 2). The conditions applied for PCRs agreed with the corresponding literature [24,28]. The annealing temperature (Ta) was kept 2–3 °C below the melting temperature (Tm) of that primer sequence.
For SSR genetic analysis, PCR amplifications were performed in a total volume of 20 µL for each molecular marker (Eurofins Genomics Custom DNA oligos, (Ebersberg, Germany) separately containing 50 ng/µL of DNA template. Τhe PCR master mix contained1X reaction buffer (5X KAPA Taq reaction buffer with no magnesium), 2 mM of MgCl2 (Kapa Biosystems, Cape Town, South Africa), 0.25 μM of each dNTP (Enzyquest dNTPs mix, 10 mM each), 0.4 μM each of the reverse and forward primer, except for Na10-F06 and nga111 where 0.6 μM of each was added (Eurofins Genomics Custom DNA oligos), and a 0.5 unit KAPA Taq DNA polymerase (Kapa Biosystems, 5 u/μL).
In the case of Ol10B11, Ol10B01, and Na10-F06, the PCR master mix contained 1X reaction buffer (5X KAPA Taq reaction buffer with no magnesium), 2.5 mM of MgCl2, 0.25 μM of each dNTP (Enzyquest dNTPs mix, 10 mM each), 0.4 μM each of the reverse and forward primer, except for Na10-F06 where 0.6 μM of each was added (Eurofins Genomics Custom DNA oligos), and a 0.5 unit KAPA Taq DNA polymerase (Kapa Biosystems, 5 u/μL).
The PCR program included a first amplification step which consisted of initial denaturation at 95 °C for 5 min followed by 35 cycles of denaturation at 94 °C for 35 s, primer annealing at 50–59 °C (varying with each SSR primer pair) for 35 s, primer extension at 72 °C for 35 s and a final extension step at 72 °C for 7 min.
Three technical independent PCR reactions were performed for each SSR marker. The amplified fragments were resolved on 2.2% agarose gel running in 0.5X TBE electrophoresis buffer on device Mupid One Electrophoresis System using 50 bp DNA step ladder (Sigma Aldrich, Saint Louis, MO, USA) as the size standard reference. DNA bands were visualized under UV light using the gel illumination system Mini Bis Pro (DNRBio-Imaging, Systems, Jerusalem, Israel) connected with software DnR GelCapture 7.5.2 (Supplementary Materials shows some representative examples of raw electrophoresis material).

2.3. Data Statistical Analysis

Genetic diversity analysis was performed using binary SSR data in GenAlex v. 6.5 software [29].

2.3.1. Variation Within Population

The effective numbers of alleles (Ne), observed heterozygosity (Ho), expected heterozygosity (He), total expected heterozygosity (Ht), and Shannon’s information index (I) were determined using GenAlex software [29].

2.3.2. Variation Among Populations

To estimate among-populations genetic variation, Fst (fixation index) was calculated to measure the genetic differentiation between the populations based on allele frequencies. For allelic richness (AR) and private allelic richness (pAR) calculations software HP-RARE was used [30]. Means and significant values over the loci and populations were obtained by bootstrapping to assess the variability and accuracy of the estimates. The hierarchical distribution of genetic variation (AMOVA) among and within the populations for the ten markers was assessed with GenAlEx software [29], allowing for a partition of the total genetic variation into among-group and within-group components. The effective size of each population (Ne) can be used in the calculation of the Shannon indices (Shannon’s diversity index) (I) to quantify the richness and evenness of genetic diversity, as well as in the estimation of the effective number of migrants (Nm), which provides insights into gene flow between populations. Nei’s unbiased genetic distance and significance among populations were also estimated, providing a measure of genetic divergence based on allele frequencies. Moreover, genetic distance for each pair of populations was calculated to investigate the relatedness between populations. FreeNA software [31] was used to estimate Nei’s unbiased genetic distance and significance among populations [32], incorporating methods for correcting null alleles. Cavalli-Sforza and Edward’s chord genetic distances (DC) [33], were also estimated for each pair of populations, utilizing the computed methods ENA (DCENA) and INA (DCINA) [31].
The most likely number of clusters (K) was defined using the ΔK method of Evanno et al. [34], which evaluates the most likely number of genetic clusters based on a model of population structure, using the second-order rate of change of the likelihood function with respect to K.

3. Results

3.1. Genetic Variation Within Populations

A total of 219 alleles were obtained from the ten SSR loci analyzed (Table 3). SSRs, Ol10B11, Ol10B01and Ol09A01 detected more than 25 alleles. The (total) number of alleles per locus (Na) varied from 16 (minimum; Na10-F06) to 32 (maximum; Ol09A01). Respectively, the effective number of alleles (Ne) ranged from minimum 2.413 (Na10-F06) to maximum 4.107 (Ol09A01), with a mean value of 3.049 per locus. Expected heterozygosity (He) within the population for all SSR loci studied was 0.65.

3.2. Genetic Variation Between and Within Populations

The average number of alleles (Na) per population ranged from 3.200 (population C) to 4.400 (population A), with a mean of 3.650 alleles per individual population. The effective number of alleles (Ne) ranged between 2.747 in population E and 3.519 in population A, with a mean of 3.050 alleles per individual population. Population A, obviously, indicates a higher genetic stability. The expected heterozygosity (He) varied between 0.617 (minimum) (population F) and 0.678 (maximum) (population B). Observed heterozygosity (Ho) was generally noticed in low levels, ranging from 0.060 (population C) to 0.220 (population F). The overall average allelic richness (AR) was 3.26; the highest (3.69) was noticed in population A, whereas the lowest (3.05) was in populations C and E. Private allelic richness (pAR) varied between 0.09 (population D) and 0.86 (population B) with a mean value 0.62 (Table 4).
The genetic differentiation between populations was high (F = 0.812; p > 0.05). AMOVA test proved that differentiation within-population was 80% and among populations was 20% (Figure 2).
The DELTA method of Evanno [34] was employed to determine the optimal number of clusters (K) (Figure 2), which revealed the presence of four distinct gene pools (K = 4) (Figure 3) in the Structure analysis performed using Clumpak software [35].
The STRUCTURE analysis results revealed (optimum) four gene pools in Greece (Figure 3). Group I included B. cretica subsp. aegaea populations from Evia and Ymittos. At group II, populations of B. cretica subsp. cretica from Crete and northern Peloponnese were included. Groups III and IV were formed by B. cretica subsp. laconica and B. oleracea, respectively (Figure 4).
To further examine relationships among populations, the INA and ENA methods were used to calculate Pairwise populations Fst Values. (Table 5). The greatest genetic distance was observed between populations C and F (Fst = 0.236) indicating a high genetic divergence, while the smallest genetic distance between populations C and D (Fst = 0.106), indicating their close genetic relatedness.
A UPGMA dendrogram (Figure 5), based on Nei’s (unbiased) genetic distance matrix [32], was constructed using R software and the Ape and Phangorn packages [36,37], to illustrate the genetic relationships among the six studied populations. Populations of B. creticasubsp. aegaea and B. cretica subsp. cretica, form two distinct clades. The B. oleracea cultivar forms a clade that is sister to the clade comprising these two subspecies. Notably, B. cretica subsp. laconica appears genetically distinct from the other B. cretica subspecies, forming an entirely separate clade. These findings are largely consistent with the results of the STRUCTURE analysis.

4. Discussion

In the Mediterranean basin, the fragmentation of species into genetically isolated populations has significant implications for diversification, adaptation, and speciation. The rich Aegean flora is a product of the region’s complex geological history, which has shaped a fragmented insular landscape. Many plant species exist in small, isolated populations with limited migration [38,39,40], contributing to the characteristic mosaic variation in Aegean plant taxa [11,41,42]. Herein, ten SSR markers were used to assess the genetic diversity and population structure of five B. cretica populations and one B. oleracea botanical variety. This study aimed to evaluate genetic diversity within and genetic differentiation among B. cretica populations, examine the distribution of genetic variation, and identify the closest relative to the cultivated plants. We found high genetic diversity, with the number of alleles per locus (Na) ranging from 18 (sORA26 and BN12A) to 32 (Ol09A01), with a mean value of 20. Notably, the observed Na washigher than that reported by [26] (Na = 5.4) for several shared loci (Ol10-F11, Ni4-B10, sORA26, BN12A, Na10-F06, nga111 and MB4). Furthermore, our study recorded a mean number of private alleles of 0.62, significantly higher than the 0.141 reported in the previous study, indicating a unique gene pool to our study populations.
The average number of alleles per population (Na) in our study was 3.65, which is higher than the 1.9 reported for seven B. cretica populations from Crete [26] and greater than the values found in 25 B. oleracea accessions from Ireland using similar molecular markers [43]. This higher allele number may be attributed to the isolation by distance among our populations compared to the cultivated species studied by El-Esawi et al. [43]. Additionally, the wider geographical distribution of our sampled populations across long-established biogeographical barriers, compared to the more geographically restricted Cretan populations examined by [28], may further account for the observed increase in genetic diversity.
The populations of all B. cretica subspecies showed similar heterozygosity (He) values. The average expected heterozygosity (He) across the studied populations was 0.647, significantly higher than the values reported in previous studies. Edh et al. [28] and Maggioni et al. [44] found a He of 0.211, while Watson-Jones et al. [45] reported a He of 0.26 in their analyses of nine wild Brassica populations, respectively. Allelic richness was 3.26 and private allelic richness was pAR (0.62) higher than the value (0.141) reported by Edh et al. [28]. This higher number of heterozygosity and private allelic richness can be partly attributed to outcrossing breeding systems or the methodology used.
The mean Fst value for our studied populations was 0.81, indicating a high level of differentiation among them. Similar results were reported by Edh et al. [28] using nuclear and chloroplast markers, which revealed exceptionally high levels of population diversity (overall Fst = 0.628 and 1.000, respectively). In addition, although Watson-Jones [45] reported lower Fst values (0.2257), they also detected considerable differentiation among populations. These results suggest that Brassica species, particularly B. cretica populations, experienced long-termgeographic isolation, limited gene flow, and “isolation by distance” in the fragmented Aegean landscape [22]. They also have adapted to varying environmental conditions, including soil type, temperature, and moisture levels. Such environmental differences may drive local adaptation and contribute to genetic divergence between populations [28]. Such strong population structure suggests high evolutionary resilience, with each population representing a unique gene pool of significant conservation and breeding value.
Genetic distance analysis and UPGMA dendrograms consistently reveal genetic divergence among Brassica samples, classifying them into four major genetic groups. This grouping is further supported by population structure analysis. Three key conclusions emerge from these findings. B. cretica subsp. cretica and subsp. aegaea are genetically distinct but monophyletic, partly supporting the intraspecific classification proposed by Snogerup et al. [11]. While this classification is widely accepted by sources like POWO [46], Euro+Med [47], and the Flora of Greece web (2025), https://portal.cybertaxonomy.org/flora-greece/intro (accessed on 12 May 2025), among others, the classification into two subspecies (i.e., B. cretica subsp. cretica and subsp. nivea) proposed by Gustafsson et al. [13] is still followed in some studies, e.g., [48,49]. Our results indicate that the population from southern Peloponnese (Leonidio), classified as B. cretica subsp. laconica, significantly deviates from the other two subspecies of B. cretica. The flavonoid composition of B. cretica subsp. laconica also contrasts to that of the other two subspecies [50], suggesting a distinct differentiation in southern Peloponnesian populations. Their taxonomic re-evaluation could provide insights into the evolutionary history of B. oleracea/B. cretica complex, advancing our understanding on the domestication of B. oleracea crops.
The STRUCTURE analysis reveals a high degree of genetic similarity between the populations from northern Peloponnese (Akrokorinthos Castle) and eastern Crete. According to Snogerup et al. [11], populations from both regions belong to B. cretica subsp. cretica. Notably, the Akrokorinthos Castle population, located at the acropolis of ancient Corinth with a continuous human presence for millennia, was designated as the type locality for this subspecies [11]. These authors also suggested that the disjunct distribution of B. creticasubsp. cretica in Crete and northern Peloponnese likely reflects remnants of a once more continuous range rather than human introduction. This conclusion was supported by observed chromosomal rearrangements and reduced interfertility between populations. However, our analyses indicate high genetic similarity between the Akrokorinthos and the Cretan populations and reduced genetic variability in the Akrokorinthos population (Ho = 0.060), suggesting that an early human introduction to northern Peloponnese cannot be ruled out. Chromosomal rearrangements and reduced interfertility among populations may have resulted from a founder effect, leading to reduced genetic diversity and the fixation of chromosomal mutations, as well as genetic drift, selection, or local adaptation. Interestingly, the Akrokorinthos population has rather low genetic similarity to B. oleracea cultivar, possibly indicating an introduction to suitable habitats without cultivation, as suggested by Snogerup et al. [11]. The cultivation of B. cretica was likely widespread in the Aegean region. B. cretica subsp. aegaea is still widely cultivated as a landrace in the villages of Amorgos Island (R. Thanopoulos, pers. comm.).
The Brassica oleracea cultivar clusters closely with B. cretica subsp. cretica, particularly with the Cretan populations, which exhibit the highest genetic similarity. This observation agrees with previous findings by Mabry et al. [6], who identified B. cretica as the closest extant relative of domesticated B. oleracea, lending further support to the hypothesis of an eastern Mediterranean origin for cultivated cabbages and kales. Historical evidence also suggests an eastern Mediterranean origin for B. oleracea. Descriptions of kales, a form of B. oleracea, first appear in the works of the Greek scholar Theophrastus (370–285 BC), whereas earlier texts from 800 to 600 BC make no mention of such plants. This temporal context implies that domestication likely took place in this region during that period [51].
Using SSR markers, we were able to detect the genetic diversity in five B. cretica populations. Our findings indicate the need for additional molecular and phenotypic studies to clarify the taxonomic classification of B. cretica in the Aegean region. These investigations should include a broader range of populations from different regions of Greece and the eastern Mediterranean. Our results also revealed high genetic diversity and allelic richness in B. cretica populations, making them a valuable genetic resource for breeding with Brassica crops.
Climate change threatens both the survival of native Brassica populations and the sustainability of Brassica crop production. Rising temperatures disrupt flowering times, pollination, and seed viability, while extreme heat stress reduces germination and biomass production [51]. Drought and water stress further hinder seed germination, early development, and overall yield, with wild populations facing habitat loss due to desertification [52]. Extreme weather events further damage plants, displace seeds, and accelerate soil erosion, while habitat fragmentation and genetic erosion reduce adaptive potential. Climate-induced pollinator declines and shifts in flowering periods disrupt reproduction [53,54]. To mitigate these threats, conservation efforts must focus on preserving genetic diversity, protecting natural habitats, and implementing sustainable agricultural practices. To our knowledge, very few accessions of B. cretica currently exist in genetic banks in Greece. Therefore, establishing ex situ conservation measures for Greek populations, including seed collection and vegetative propagation, should be a high priority. Moreover, breeding resilient Brassica varieties is crucial for sustainability under climate change and rising pest and disease pressures, with diversity maintenance being key to long-term crop improvement. However, utilizing B. cretica populations as reservoirs of adaptive genetic variation in breeding programs presents several challenges, apart from being often underrepresented in seed banks, such as the lack of comprehensive genetic data, the limited understanding of their phylogenetic relationships, and hybridization potential with cultivated Brassica crops. Integrating available phenotypic and geographical data with genomic sequence information—potentially through a universal molecular passport—could enhance the identification of valuable genetic variation and their utilization in breeding programs [55,56].

5. Conclusions

This study reveals high genetic diversity and significant differentiation among B. cretica populations in the Aegean, shaped by geographic isolation and local adaptation. SSR marker analysis showed high allelic richness and private variation, highlighting the unique gene pools within these populations. The close genetic relationship between B. cretica and cultivated B. oleracea supports an eastern Mediterranean origin for domesticated forms and calls for a taxonomic re-evaluation, particularly of B. cretica subsp. laconica. Given the threats posed by climate change and habitat loss, both in situ and ex situ conservation are urgent. Preserving and utilizing the genetic diversity of B. cretica in breeding programs could enhance the resilience of Brassica crops, especially if supported by integrated phenotypic, geographical, and genomic data.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy15051201/s1, Figure S1: Brassica cretica subsp. cretica in its natural habitat at Akrokorinthos (N Peloponnese); Figure S2: Brassica cretica subsp. laconica in SE Peloponnese; Figure S3: Brassica cretica subsp. aegaea on Mt. Ymittos, Attiki.

Author Contributions

Conceptualization, E.T., P.T. and E.V.A.; methodology, E.S., G.N., T.N. and I.K.; software, A.K.; validation, A.K., E.V.A. and P.T.; formal analysis, A.K. and E.V.A.; investigation, E.S.; resources, E.T. and P.J.B.; data curation, E.S.; writing—original draft preparation, E.S., E.T., E.V.A., A.K., P.T. and P.J.B.; writing—review and editing, E.S., E.T., E.V.A., A.K., P.T. and P.J.B.; visualization. E.S.; supervision, E.T.; project administration, E.T.; funding acquisition, P.J.B. All authors have read and agreed to the published version of the manuscript.

Funding

We thank Emmanouil Avramakis (Natural History Museum of Crete) for providing seeds and specimens from Crete. This research was supported by the Hellenic Foundation for Research and Innovation (HFRI) under the 3rd Call for HFRI PhD Fellowships (Fellowship Number: 5666).

Data Availability Statement

All data are available upon request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Agronomy 15 01201 g001
Figure 1. Geographical locations of the five natural populations of B. cretica included in this study.
Figure 1. Geographical locations of the five natural populations of B. cretica included in this study.
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Figure 2. Partitioning of genetic variation among and within five populations of B. creticabased on AMOVA.
Figure 2. Partitioning of genetic variation among and within five populations of B. creticabased on AMOVA.
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Figure 3. Genetic structure of B. cretica populations inferred from STRUCTURE analysis at K = 4.
Figure 3. Genetic structure of B. cretica populations inferred from STRUCTURE analysis at K = 4.
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Figure 4. Visualization of population structure in B. cretica based on STRUCTURE analysis at K = 4, processed with Clumpak software. Population codes according to Table 1.
Figure 4. Visualization of population structure in B. cretica based on STRUCTURE analysis at K = 4, processed with Clumpak software. Population codes according to Table 1.
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Figure 5. UPGMA tree of Brassica cretica populations based on Nei’s genetic distance. Bootstrap values are shown at branch nodes.
Figure 5. UPGMA tree of Brassica cretica populations based on Nei’s genetic distance. Bootstrap values are shown at branch nodes.
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Table 1. Sampling details of B. cretica populations; collection localities, seed collection dates and geographic coordinates (latitude/longitude).
Table 1. Sampling details of B. cretica populations; collection localities, seed collection dates and geographic coordinates (latitude/longitude).
CODETaxonLocalityCountrySeed Collection DateLatitudeLongitude
ABrassica cretica subsp. aegaeaManikia, EviaGreeceJune 202238°32.582′24°1.052′
BBrassica cretica subsp. aegaeaYmittos, AttikiGreeceJune 202237°56.671′23°48.030′
CBrassica cretica subsp. cretica or niveaAkrokorinthos, KorinthosGreeceMay 202237°53.363′22°52.138′
DBrassica cretica subsp. creticaLasithi, CreteGreeceMay 202135°19.772′25°41.458′
EBrassica cretica subsp. laconicaLeonidio, ArkadiaGreeceJune 202237°9.940′22°52.255′
FBrassica oleracea var Rubracommercial variety of cabbage as outgroupSeeds produced in E.U.
Table 2. Characteristics of 10 SSR loci analyzed in B. cretica Lam. populations; primer sequences, repeat motifs, expected amplicon size range (bp), and optimized annealing temperatures (Ta as well as the source of the primers used). Details of the selected SSR markers, such as map position, repeat motif, repeat count, and size range, can be found in the website database www.brassica.info, accessed on 15 March 2023.
Table 2. Characteristics of 10 SSR loci analyzed in B. cretica Lam. populations; primer sequences, repeat motifs, expected amplicon size range (bp), and optimized annealing temperatures (Ta as well as the source of the primers used). Details of the selected SSR markers, such as map position, repeat motif, repeat count, and size range, can be found in the website database www.brassica.info, accessed on 15 March 2023.
LocusForward and Reverse Primer Sequences (5′→3′)Repeat MotifSize Range (bp)Ta (°C)Citation
Ol10B11AAAATGTGAGGCTGTTTGGG
TTTCGCAGCAGTAAACATGG		(GA)2576–18052.5[24]
Ol10B01CCTCTTCAGTCGAGGTCTGG
AATTTGGAAACAGAGTCGCC		(GA)20160–28056[24]
Ol09A01TTCGAAGCTCATTATCGCAG
CCGGGCTCTCTCTCTCTCTC		(GA)75120–34056.5[24]
Ol10F11TTTGGAACGTCCGTAGAAGG
CAGCTGACTTCGAAAGGTCC		(GGC)7139–18456[24,28]
Ni4-B10GTCCTTGAGAAACTCCACCG
CCGATCCCATTTCTAATCCC		(CT)20170–20056[26]
sORA26TGTTTACCTGTTGGAGAT
AACCCTAAGCATCTGCGA		(GA)562–7649[24,28]
BN12AGCCGTTCTAGGGTTTGTGGGA
GCCGTTCTAGGGTTTGTGGGA		(GA)11(AAG)4250–33059[28]
Na10-F06CTCTTCGGTTCGATCCTCG
TTTTTAACAGGAACGGTGGC		(CCG)684–12654.5[28]
nga111TGTTTTTTAGGACAAATGGCG
CTCCAGTTGGAAGCTAAAGGG		(GA)16120–15054.5[28]
MB4TGTTTTGATGTTTCCTACTG
GAACCTGTGGCTTTTATTAC		(TG)1057–6950[28]
Table 3. Genetic diversity indices measured for ten SSR loci across studied populations of B. cretica.
Table 3. Genetic diversity indices measured for ten SSR loci across studied populations of B. cretica.
LocusOl10B11Ol10B01Ol09A01Ol10F11Ni4-B10sORA26BN12ANa10-F06nga111MB4MEAN
Na2730321919181816202021.9
Ne3.6063.9854.1072.4892.8442.7592.6292.4132.8052.8593.049
I1.3741.4381.4180.9771.0811.0521.0050.9071.0821.1141.145
Ho0.0750.350.3080.3080.175000.050.01700.128
He0.7200.7340.7050.5830.6380.6340.610.5680.6310.6450.647
Na = (total) number of alleles, Ne = number of effective alleles, I = Shannon’s information index, Ho = observed heterozygosity, He = Nei’s expected heterozygosity, Mean F-Statistics represent arithmetic averages. p < 0.05.
Table 4. Genetic diversity parameters at the population level in B. cretica based on ten SSR loci.
Table 4. Genetic diversity parameters at the population level in B. cretica based on ten SSR loci.
PopulationNaNeIHeHoARpARFst
A4.4003.5191.2790.67701403.690.820.808
B38003.1911.21806780.0953.440.860.862
C32002.9171.09106450.0603.050.620.886
D37003.0431.1310.6360.1253.250.090.840
E3.3002.7471.0750.6290.1303.050.600.792
F35002.8791.0750.6170.2203.090.720.682
Mean3.6503.0501.1450.6470.1283.260.620.812
Na = (mean) number of alleles, Ne = (mean) number of effective alleles, I = Shannon’s information index, He = expected heterozygosity, Ho = observed heterozygosity, AR = allelic richness, pAR = private allelic richness, F = Wright’s fixation index. Mean F-Statistics represent arithmetic averages.
Table 5. Pairwise genetic differentiation (Fst) between populations of B. cretica based on ten SSR loci, with INA method (above the diagonal; 0.000) and ENA method (below the diagonal) [29].
Table 5. Pairwise genetic differentiation (Fst) between populations of B. cretica based on ten SSR loci, with INA method (above the diagonal; 0.000) and ENA method (below the diagonal) [29].
ABCDEF
0.0000.0810.1470.0940.1450.125A
0.1220.0000.1280.1230.1560.148B
0.2030.1720.0000.0740. 1790,177C
0.1310.1750.1060.0000.1510.129D
0.2000.2120.2340.2120.0000.154E
0.1720.1990.2360.1790.2150.000F
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Stathi, E.; Avramidou, E.V.; Trigas, P.; Katsileros, A.; Karavidas, I.; Ntanasi, T.; Ntatsi, G.; Bebeli, P.J.; Tani, E. Intraspecific Genetic Variability of Brassica cretica Lam. (Brassicaceae) Using SSR Markers. Agronomy 2025, 15, 1201. https://doi.org/10.3390/agronomy15051201

AMA Style

Stathi E, Avramidou EV, Trigas P, Katsileros A, Karavidas I, Ntanasi T, Ntatsi G, Bebeli PJ, Tani E. Intraspecific Genetic Variability of Brassica cretica Lam. (Brassicaceae) Using SSR Markers. Agronomy. 2025; 15(5):1201. https://doi.org/10.3390/agronomy15051201

Chicago/Turabian Style

Stathi, Efthalia, Evangelia V. Avramidou, Panayiotis Trigas, Anastasios Katsileros, Ioannis Karavidas, Theodora Ntanasi, Georgia Ntatsi, Penelope J. Bebeli, and Eleni Tani. 2025. "Intraspecific Genetic Variability of Brassica cretica Lam. (Brassicaceae) Using SSR Markers" Agronomy 15, no. 5: 1201. https://doi.org/10.3390/agronomy15051201

APA Style

Stathi, E., Avramidou, E. V., Trigas, P., Katsileros, A., Karavidas, I., Ntanasi, T., Ntatsi, G., Bebeli, P. J., & Tani, E. (2025). Intraspecific Genetic Variability of Brassica cretica Lam. (Brassicaceae) Using SSR Markers. Agronomy, 15(5), 1201. https://doi.org/10.3390/agronomy15051201

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