Genet Resour Crop Evol https://doi.org/10.1007/s10722-018-0725-3 (0123456789().,-volV) (0123456789().,-volV) RESEARCH ARTICLE Genetic diversity of Aegilops L. species from Azerbaijan and Georgia using SSR markers Mehraj Abbasov . Robert Brueggeman . John Raupp . Zeynal Akparov . Naib Aminov . David Bedoshvili . Thomas Gross . Patrick Gross . Sevda Babayeva . Vusala Izzatullayeva . Sevinj A. Mammadova . Elchin Hajiyev . Khanbala Rustamov . Bikram S. Gill Received: 18 May 2018 / Accepted: 19 November 2018 Ó Springer Nature B.V. 2018 Abstract Five microsatellite (SSR) markers were used to evaluate the genetic diversity of six Aegilops species from Azerbaijan and Georgia. A total of 39 alleles were generated with an average of 7.8 alleles per primer. Twenty markers were species-specific and 6 were accession-specific. The transferability of SSR markers across six species was 100%, with exception of gwm210. The mean polymorphism information content (PIC) and expected heterozygosity (He) values for the entire collection were 0.688 and 0.725, respectively. The average PIC value was the highest in Ae. biuncialis accessions (0.55). The genetic distance (GD) indices, based on five SSR markers, ranged from 0 to 0.83, with a mean value of 0.47. The highest genetic similarity was noted between Ae. neglecta and Ae. triuncialis (GD = 0.26), and the lowest between Ae. neglecta and Ae. tauschii (GD = 0.66). The dendrogram created based on SSR data grouped 72 Aegilops accessions into six clusters according to their taxonomic classification. The accessions from the same province were often placed in the same subclusters, indicating that grouping based on genetic parameters was closely related to the geographic region within countries. The PCoA analysis could differentiate Aegilops accessions according to their species and confirmed subgrouping obtained by cluster analysis. Keywords Aegilops
Species
SSR
Genetic diversity
Genetic relationship Introduction M. Abbasov (&)
Z. Akparov
N. Aminov
S. Babayeva
V. Izzatullayeva
S. A. Mammadova
E. Hajiyev
K. Rustamov Genetic Resources Institute of ANAS, Baku, Azerbaijan e-mail: mehraj_genetic@yahoo.com R. Brueggeman
T. Gross
P. Gross Plant Pathology Department, North Dakota State University, Fargo, ND, USA D. Bedoshvili Agricultural University of Georgia, Tbilisi, Georgia J. Raupp
B. S. Gill Department of Plant Pathology, Wheat Genetics Resource Center, Kansas State University, Manhattan, USA Aegilops L. is a major genus in the Triticeae tribe and comprises 23 annual species with different ploidy levels. Aegilops grows in both the Mediterranean and Irano-Turanian regions (Hedge et al. 2002), and in Cis- and Transcaucasia. Some Aegilops species are involved in wheat evolution and, thus, are of potential use in wheat improvement programs (Konstantinos and Bebeli 2010). Ae. tauschii Coss. is the D genome donor of bread wheat, Triticum aestivum L. (McFadden and Sears 1946). Most Aegilops species exhibit wide diversity for various desirable traits providing an invaluable gene pool for wheat breeding. Ae. 123 Genet Resour Crop Evol umbellulata Zhuk., for instance, was used to transfer leaf rust resistance to bread wheat (Sears 1956), whereas Ae. neglecta Req. ex Bertol., Ae. biuncialis Vis. and Ae. triuncialis L. have been used as sources of leaf rust, stripe rust and powdery mildew resistance genes (Aghaee-Sarbarzeh et al. 2001, 2002; Stoilova and Spetsov 2006; Kuraparthy et al. 2007a, b; Schneider et al. 2008; Chhuneja et al. 2008). The introduction of novel genes from these Aegilops species will help create new cultivars with a wider genetic base and adaptability. Modern molecular tools now extend our opportunities to study genetic diversity and facilitate genome-wide association studies of complex traits and select appropriate donors for breeding purposes (Aliyev et al. 2007; Hajiyev et al. 2015). Molecular techniques, such as isozymes (Karcicio and Izbirak 2003) and DNA-based markers (Lubbers et al. 1991; Chee et al. 1995; Dubcovsky and Dvorak 1995; Zhang et al. 1996), have been applied to assess the genetic diversity within the genus Aegilops. Among molecular markers, SSRs proved more effective, due to high levels of genetic polymorphism, and were used successfully to detect genetic diversity and phylogenetic relationships in many crops, including wheat and its wild relatives (Pestsova et al. 2000; Naghavi et al. 2007; Babayeva et al. 2009; Henkrar et al. 2016; Abbasov et al. 2018). Nevertheless, in spite of many years of diversity exploration, our knowledge on the genus Aegilops is far from complete. Additional information is needed for each species dealing with their distribution and diversity in different regions, as well as in the centers of origin. Azerbaijan is a Transcaucasian country and considered to be the center of origin for many plants, including some Aegilops species. Eleven species of Aegilops can be found within the borders of the Republic (Eldarov et al. 2015). According to Hammer (1980), the origin of Aegilops can be found in the Transcaucasian area, from which diploid species migrated in western and south-western directions. Van Slageren (1994) determined that northwest Iran, where it borders Azerbaijan, as one of the areas with the largest diversity of genus Aegilops. The center of distribution of Ae. tauschii is reported to be along the southern shores of the Caspian Sea and in Azerbaijan (Kilian et al. 2011). Taking these facts into account, much attention should be given to this area as a potential source of useful alleles. Despite a number of investigations dealing with the diversity of different 123 Aegilops species from different regions, the Transcaucasian area has been overlooked. Previous investigations are only limited to Ae. tauschii species from Azerbaijan and Georgia (Pestsova et al. 2000; Lelley et al. 2000; Naghavi et al. 2007). Our aim was to evaluate the genetic diversity of different Aegilops species from Azerbaijan and Georgia using SSR markers. This is the first report on the use of SSR markers in analyzing six Aegilops species of Azerbaijan and Georgian origin. Materials and methods Seventy-two accessions representing six Aegilops species from Azerbaijan and Georgia were used as research materials. The accessions were collected by authors during an expedition in 2012. Of the 72 accessions, 34 belonged to Ae. tauschii, 12 to Ae. triuncialis, 11 to Ae. cylindrica Host, 8 to Ae. biuncialis, 5 to Ae. neglecta and 2 to Ae. umbellulata. Accession number, species and country of origin are given in Table 1. Genomic DNA isolation was conducted following the CTAB DNA extracted protocol as previously described in Abbasov et al. (2018). Seven SSR markers previously mapped to the A and B genomes (Table 2) were used for genotyping. PCR conditions, using fluorescent-dye labeled primers, was as follows: initial denaturation at 95 °C for 3 min; 40 cycles of denaturation at 95 °C for 1 min, annealing at 50 °C for 1 min and elongation at 72 °C for 2 min; final elongation at 72 °C for 10 min. DNA fragments were separated on an ABI 3130xl Genetic Analyzer (Applied Biosystems/Thermo Fisher Scientifics) (Chao et al. 2007). Fragment analysis and allele calling were performed using GeneMapper software v.3.7 (Applied Biosystems, Foster City, CA). Measures of genetic diversity, i.e., a total number of alleles, expected heterozygosity (He), observed heterozygosity (Ho) and polymorphism information content (PIC), were estimated using PowerMarker v. 3.51 (Liu and Muse 2005). PowerMarker software was also used to calculate allele frequencies and distances based on frequencies among different species. Cluster analysis, PCoA analysis and the Unweighted Neighbor-joining tree were constructed using the DARwin 6.0 software package (Gascuel 1997; Perrier and Jacquemoud-Collet 2006). Genet Resour Crop Evol Table 1 Sample and accessiona numbers, and country of origin of the species used in this study # KSU Accession number Country of origin Aegilops species # KSU Accession number Country of origin Aegilops species 1 TA10918 Georgia tauschii 37 TA10964 Georgia cylindrica 2 TA10919 Georgia tauschii 38 TA10965 Georgia cylindrica 3 TA10920 Georgia tauschii 39 TA10966 Azerbaijan cylindrica 4 TA10921 Georgia tauschii 40 TA10967 Azerbaijan cylindrica 5 TA10922 Georgia tauschii 41 TA10968 Azerbaijan cylindrica 6 TA10923 Georgia tauschii 42 TA10970 Azerbaijan cylindrica 7 TA10924 Georgia tauschii 43 TA10971 Azerbaijan cylindrica 8 TA10926 Georgia tauschii 44 TA10972 Azerbaijan cylindrica 9 TA10927 Georgia tauschii 45 TA10973 Azerbaijan cylindrica 10 TA10930 Georgia tauschii 46 TA10975 Georgia triuncialis 11 TA10931 Georgia tauschii 47 TA10976 Georgia triuncialis 12 TA10933 Azerbaijan tauschii 48 TA10977 Georgia triuncialis 13 TA10934 Azerbaijan tauschii 49 TA10978 Azerbaijan triuncialis 14 TA10935 Azerbaijan tauschii 50 TA10979 Azerbaijan triuncialis 15 16 TA10936 TA10939 Azerbaijan Azerbaijan tauschii tauschii 51 52 TA10980 TA10981 Azerbaijan Azerbaijan triuncialis triuncialis 17 TA10940 Azerbaijan tauschii 53 TA10982 Azerbaijan triuncialis 18 TA10941 Azerbaijan tauschii 54 TA10984 Azerbaijan triuncialis 19 TA10942 Azerbaijan tauschii 55 TA10987 Azerbaijan triuncialis 20 TA10943 Azerbaijan tauschii 56 TA10988 Azerbaijan triuncialis 21 TA10944 Azerbaijan tauschii 57 TA10989 Azerbaijan triuncialis 22 TA10946 Azerbaijan tauschii 58 TA10990 Georgia biuncialis 23 TA10948 Azerbaijan tauschii 59 TA10991 Azerbaijan biuncialis 24 TA10949 Azerbaijan tauschii 60 TA10992 Azerbaijan biuncialis 25 TA10951 Azerbaijan tauschii 61 TA10993 Azerbaijan biuncialis 26 TA10952 Azerbaijan tauschii 62 TA10994 Azerbaijan biuncialis 27 TA10953 Azerbaijan tauschii 63 TA10995 Azerbaijan biuncialis 28 TA10954 Azerbaijan tauschii 64 TA10996 Azerbaijan biuncialis 29 TA10955 Azerbaijan tauschii 65 TA10997 Azerbaijan biuncialis 30 31 TA10956 TA10957 Azerbaijan Azerbaijan tauschii tauschii 66 67 TA10998 TA10999 Azerbaijan Azerbaijan umbellulata umbellulata 32 TA10959 Azerbaijan tauschii 68 TA11000 Georgia neglecta 33 TA10960 Azerbaijan tauschii 69 TA11003 Azerbaijan neglecta 34 TA10961 Azerbaijan tauschii 70 TA11004 Azerbaijan neglecta 35 TA10962 Georgia cylindrica 71 TA11005 Azerbaijan neglecta 36 TA10963 Georgia cylindrica 72 TA11006 Azerbaijan neglecta a All accessions are maintained by the Wheat Genetics Resource Center, Kansas State University, Manhattan, KS, USA Results Genetic diversity Out of seven SSR markers used in the present study two (gwm508, wPt7004) were monomorphic, however, they gave amplification products in all Aegilops species. The remaining five primers which provided polymorphic products were used for further analysis. A total of 39 alleles were generated for 72 Aegilops genotypes using five SSR loci (Table 2). The number of alleles per locus ranged from 5 (gwm633; 123 Genet Resour Crop Evol Table 2 Summary statistics of the SSR markers used in the study Marker Allele No. Ae. biuncialis Ae. cylindrica Ae. neglecta Ae. tauschii Ae. triuncialis Ae. umbellulata HO HE PIC gwm210 12 0 1 1 8 2 0 0.000 0.871 0.859 wmc179 9 4 1 3 3 2 1 0.236 0.842 0.825 gwm633 5 3 1 2 3 4 1 0.122 0.684 0.632 gpw4165 8 2 2 2 5 4 1 0.016 0.729 0.685 IB-267 5 2 1 2 3 2 1 0.169 0.497 0.440 Mean 7.8 0.109 0.725 0.688 Total 39 IB-267) to 12 (gwm210), with an average of 7.8 alleles per locus. Loci with the lowest major allele frequency (gwm210, wmc179) had the highest number of alleles, expected heterozygosity (He) and polymorphism information content (PIC). PIC values of each marker locus ranged from 0.440 to 0.859. The mean PIC and He values scored across all SSR alleles for the entire collection were 0.688 and 0.725, respectively. All loci, except IB-267 had a PIC value above 0.5. The specific alleles were determined at two levels: accession and species. All SSR primers produced species-specific amplicons. The total number of species-specific amplicons was 20, with a maximum of 13 alleles in Ae. tauschii. Three specific alleles were noted for Ae. biuncialis, two for Ae. cylindrica, one for Ae. triuncialis and one for Ae. neglecta. All 11 Ae. cylindrica accessions were fully distinguished by just two independent alleles. Despite primer gwm210 had maximum allele number and distinguished four species, it gave no amplification products in Ae. biuncialis and Ae. umbellulata (Table 2). The number of accession-specific alleles was 6, out of which four were found in Ae. tauschii and two in Ae. cylindrica. Genotype sample 45 had two accessionspecific alleles, amplified by gwm210 and gwm633. Among the five compared Aegilops species, the PIC value was the highest in Ae. biuncialis (0.55), and was the lowest in Ae. cylindrica (0.06) (Table 3). Cluster analysis The genetic distance (GD) indices based on SSR markers among all the pair-wise combinations of genotypes ranged from 0 to 0.83, with a mean value of 0.47. The highest distance value was obtained between 123 genotypes 33 (Ae. tauschii) and 50 (Ae. triuncialis) and genotypes 33 (Ae. tauschii) and 53 (Ae. triuncialis), while the lowest value was noted between many pairs of genotypes. Among species, the highest genetic similarity was noted between Ae. neglecta and Ae. triuncialis (GD = 0.26), and the lowest between Ae. neglecta and Ae. tauschii (GD = 0.66) (Table 4). The UNJ clustering algorithm based on SSR data grouped 72 accessions into six clusters (Fig. 1). All species were clearly distinguished and formed independent clusters or subclusters, with some exceptions found for a few samples. The cluster I contained four species, while the remaining clusters were homogeneous. All Ae. cylindrica accessions were in cluster II. Ae. tauschii genotypes were shared among homogeneous clusters III, IV, and V. Ae. biuncialis genotypes divided into two groups, of which one group constituted cluster VI and the other group formed homogeneous subcluster within cluster I. In addition, three species also fell into cluster I; Ae. umbellulata and Ae. Table 3 Summary statistics for different Aegilops species Species* Sample size** Allele number HE PIC Ae. biuncialis 8 (7;1) 11 0.62 0.55 Ae. cylindrica 11 (7;4) 6 0.08 0.06 Ae. neglecta 5 (4;1) 10 0.30 0.25 Ae. tauschii 34 (23;11) 22 0.50 0.45 Ae. triuncialis 12 (9;3) 14 0.46 0.39 *Ae. umbellulata was not included, because of low (2) sample size **The numbers in the parenthesis indicate sample size from Azerbaijan and Georgia, respectively Genet Resour Crop Evol Table 4 Genetic distance among studied Aegilops species OTU biuncialis cylindrica 0.3108 neglecta 0.3561 0.5133 tauschii triuncialis 0.3527 0.2869 0.5025 0.5139 cylindrica neglecta tauschii 0.6598 0.2599 0.6389 triuncialis genotypes formed uniform groups, whereas Ae. neglecta was distributed among subclusters. The analysis did not reveal any relationship between the grouping within species and the origin countries. However, the genotypes from the same province within the countries were tended to group jointly. Principal coordinate analysis (PCoA) was used to illustrate the distribution of the Aegilops genotypes in a scatter-plot (Fig. 2). The first two coordinate axes accounted for 41.6% of the total variation observed. In general, the PCoA analysis revealed that genotypes belonging to a particular cluster were also grouped together in the scatter plot. Fig. 1 Dendrogram showing the genetic relationship among 72 Aegilops accessions based on Gascuel’s genetic distance. Different colors indicate different species (Ae. umbellulata Discussion The Aegilops species are considered to belong to the primary, secondary or tertiary gene pool of wheat, thus, their genetic diversity is of great interest (Qi et al. 2007). Studying collections of different species from native countries can widen our current knowledge on Aegilops diversity and accelerate their utilization in breeding programs. The current research investigated diversity using seven microsatellite loci among six Aegilops species from Azerbaijan and Georgia. Two SSR primers produced monomorphic bands in all Aegilops species, which indicates the high conservation and the low mutation rate of microsatellites in these species. Monomorphic markers are usually eliminated from the further genetic analysis, however, in several investigations, the possibility of converting monomorphic microsatellite markers to polymorphic single nucleotide polymorphism (SNP) markers were successfully demonstrated (Nazareno and dos Reis 2011; Karaca and Ince 2011; Dadzie et al. 2013). The remaining primers were polymorphic and amplified a total of 39 alleles, with an average of 7.8 alleles per locus. Moradkhani et al. (2015) reported relatively higher total allele number for 20 accessions from 5 different Aegilops-Triticum species with 10 SSR primers. Using 21 microsatellite markers for the (black), Ae. tauschii (green), Ae. cylindrica (red), Ae. biuncialis (blue), Ae. neglecta (yellow), and Ae. triuncialis (violet). (Color figure online) 123 Genet Resour Crop Evol Fig. 2 Scatter plot of 72 Aegilops accessions based on SSR data. Species are color-coded according to that in Fig. 1. I—Ae. tauschii genotypes from Nakhchivan and Kvemo Kartli, II—Ae. tauschii genotypes from Mountainous Shirvan and other regions of Georgia 52 genotypes representing the same species Naghavi et al. (2009) found 13 alleles per primer; a range was from 3 to 7. In our study, the primers had allele variation from 5 to 12, which indicate the effectiveness of individual markers. We found that all five SSR markers in our study were effective for fingerprinting Aegilops species, as all primers produced speciesspecific alleles. In general, 51% of alleles were species-specific and 15.4% were accession-specific. Bertin et al. (2004) checked the efficiency of SSR markers for spelt accessions from 27 different countries from four continents and reported that 17% of the alleles were accession-specific and 24% were countryspecific. The specific alleles reported in this study could be used as molecular identity data for specific 123 Aegilops species and genotypes. In our study, two separate alleles (203 bp of gwm210 and 185 bp of wmc179) were sufficient to identify all accessions of Ae. cylindrica, whereas a combination of two alleles (134/230 of wmc179) could distinguish all Ae. triuncialis accessions analyzed. Besides the high rate of the specific alleles, the transferability of SSR markers across six species was 100%, with exception of gwm210. Moreover, two monomorphic primers, that were excluded from the study also gave amplification products in all studied species. The transferability of SSR markers depends on the conservation of both SSR region and annealing sequence of the primer (Tuler et al. 2015). Thus, the high percentage of transferability in our study shows Genet Resour Crop Evol high conservation of these regions and close phylogenetic relationship among the studied Aegilops species. Primer gwm210 differed by the absence of amplification in Ae. biuncialis and Ae. umbellulata. The reason for this can be the mutation at the flanking region of the microsatellites, which avoids the annealing of the primer and the absence of the microsatellite (Morin et al. 2004). The studied SSR markers exhibited a high level of genetic diversity; the mean GDI and PIC values were 0.725 and 0.688, respectively. Although other investigations have addressed the issue of molecular diversity in Aegilops, most were either only on Ae. tauschii or used marker systems other than SSRs. Only a few previous studies focused on the genetic diversity of different Aegilops species using SSR markers (Moradkhani et al. 2015; Naghavi et al. 2008). In the study of Naghavi et al. (2009) SSR analysis using 21 SSR primers in three species of Aegilops and T. aestivum found a PIC value of 0.58. In our study individual primers were characterized with even higher values. Our data suggest that SSR markers gwm210 and wmc179 could be used with confidence in fingerprinting among and within Aegilops species; 92% and 78% of the alleles detected by these markers were either species or accession specific, and they were able to distinguish between three and four species. The current collection represents 6 Aegilops species from the Transcaucasian region. Despite the different number of accessions per species, we tried to further investigate the genetic diversity at the species level. In our study, Ae. biuncialis (0.62), which is represented by eight samples, were characterized by the highest diversity, whereas those of Ae. tauschii, which had four times the number of samples, was the second highest (Table 3). Aegilops tauschii encompasses two subspecies; subsp. tauschii and subsp. strangulata (Hammer 1978; Dvorak et al. 1998). The main center of diversity for subsp. tauschii is eastern Turkey to western China and Pakistan, whereas subsp. strangulata occurs on the southern shores of the Caspian Sea and in Azerbaijan (Kilian et al. 2011) and has a wide diversity in these areas. Only a small minority (15%) of the Ae. tauschii samples in the current investigation belonged to subsp. strangulata. This fact may partially explain the moderate diversity obtained for Ae. tauschii (0.50). When studying the genetic diversity of Ae. tauschii within Azerbaijan and Georgia, we found similar PIC values, which underlines the importance of both countries as a source of valuable diversity of Ae. tauschii. Pestsova et al. (2000) and Lelley et al. (2000) also noted the richness of Ae. tauschii in Azerbaijan. Ae. triuncialis represented by 12 accessions, the majority being from Azerbaijan, has GDI = 0.46, while the lowest diversity (GDI = 0.08) was in Ae. cylindrica, consisting of a nearly equal number (11) of accessions. Low diversity in Ae. cylindrica is consistent with previous findings of Goryunova et al. (2004), Pester et al. (2003) and Caldwell et al. (2004). Contrary to this, Moradkhani et al. (2015) and PourAboughadareh et al. (2017) determined a relatively higher diversity in Ae. cylindrica compared with other species based on SSR and SCoT markers. The differences might be related to the sample size, which was low in previous studies and utilization of Ae. cylindrica germplasms from different countries. Aegilops cylindrica is a relatively new tetraploid species (Gandhi et al. 2005) spread over a wide area, including the Caucasus. The very low PIC value indicates a low diversity of Ae. cylindrica in Azerbaijan and Georgia. Clustering based on genetic distance was in agreement with taxonomy. The dendrogram obtained from five SSR markers allowed nearly all Aegilops species to be distinguished at the cluster or subcluster levels. The ability of SSR markers to differentiate among species in several crops has been reported previously (Ehtemam et al. 2010; Alnaddaf et al. 2012). Out of six clusters five were uniform and contained only one specie. Cluster I included four species classified as members of the Aegilops section. Ae. neglecta (UM and UMN) distributed among three subclusters and was closer to both Ae. triuncialis (UC) and Ae. umbellulata (U). Similarly, Queen et al. (2004) noted that in a dendrogram derived from SSAP retrotransposon data, Ae. biuncialis, Ae. neglecta and Ae. triuncialis were present together. Alnaddaf et al. (2012) also reported identical restriction profiles for Ae. neglecta, Ae. umbellulata and Ae. triuncialis, all U-genome species. Pour-Aboughadareh et al. (2017) indicated that Ae. triuncialis, Ae. umbellulata and Ae. neglecta had the highest degree of similarity due to their sharing of U genome, which was also found in our dendrogram. Both studies confirmed the integrity and stability of U genome as the pivotal genome, 123 Genet Resour Crop Evol where all polyploid species with the U genome tended to cluster together with their diploid progenitor. This tendency also was noted by Gong et al. (2006) using ISSR markers. Most of the genetic distances inside each subcluster were short, with 100% similarity among some sets. Although Ae. tauschii is distributed among three clusters, they do not interfere with other species, which on one hand indicates a high diversity within the species and, on the other hand, the uniqueness of the D genome. The wide diversity within the D genome of Ae. tauschii populations was confirmed by AFLP markers (Dvorak et al. 1998; Lelley et al. 2000). The only specie from Cylindropyrum section Ae. cylindrica formed an independent cluster, within which the genotypes showed high similarities among each other. No grouping was observed according to countries, within the species, as Azery and Georgian genotypes were placed as a mixture in clusters and subclusters. Moreover, the grouping within Ae. tauschii was also not in accordance with subspecies (subsp. strangulata and subsp. tauschii). However, there was a clear tendency of a joint grouping of accessions from the same provinces within the countries. Azerbaijan Aegilops genotypes can be separated into two groups: i) genotypes collected from Agsu, Gobustan and Shamakhi; all these areas are included into Mountainous Shirvan region and ii) genotypes collected from the Nakhchivan Autonomous Republic. Nakhchivan is a semi-desert region that is geographically separated from the main portion of Azerbaijan. Georgian genotypes were from three regions (Tbilisi, Kakheti and Kvemo Kartli) which were geographically neighbor provinces. As mentioned Ae. biuncialis genotypes divided into two very distinct groups; independent cluster VI and group within cluster I. All Azery Ae. biuncialis genotypes from cluster VI were collected from Mountainous Shirvan region, whereas all three genotypes that fell into cluster I were collected from Nakhchivan province with altitude values 1261-2121 masl. The only genotype from Tbilisi (Georgia) also fell into cluster VI. Ae. tauschii genotypes were distributed among clusters III, IV and V. The cluster III contained Ae. tauschii genotypes from Mountainous Shirvan region (Azerbaijan) and mainly genotypes from Tbilisi and Kakheti (Georgia). On contrary, clusters IV and V embrace Ae. tauschii genotypes from Nakhchivan 123 (Azerbaijan) and Kvemo Kartli (Georgia). Out of seven genotypes from Kvemo Kartli, five fell into these clusters. As for the other species, Ae. neglecta does not include genotypes from Nakhchivan, the only genotype from Kvemo Kartli was very distant from genotypes collected from Agsu (Mountainous Shirvan). Out of 12 Ae. triuncialis four were from Nakhchivan of which 2 were identical in a separate subcluster and one placed together with Georgian genotype from Kvemo Kartli. Thus from above sayings, two tendencies can be underlined. First of all, the clear differences between Aegilops accessions collected from Mountainous Shirvan and Nakhchivan regions of Azerbaijan. This could be due to the differences in soil and climatic condition of two regions. Nakhchivan region has a continental semi-arid climate and extremely arid and mountainous geography. The second tendency was a joint grouping of Nakhchivan (Azerbaijan) and Kvemo Kartli (Georgian) genotypes. We checked the effect of longitude, latitude and altitude to the grouping within clusters and found that these parameters are not a strong predictor of genetic diversity in the studied collection. So, latitude and altitude values in Nakhchivan and Kvemo Kartli differed largely. Next, we checked the collection sites of Aegilops genotypes in these regions. The remarkable point is that the collection site of samples from Nakhchivan was close to Lizbirtchay valley (Lizbirt River Valley) (along roadside next to riverbed; up the river road), whereas collection sites in Kvemo Kartli were around Kumisi Lake (hillside of lake Kumisi; on the other side of a drainage ditch from hillside of lake Kumisi). So, similar genetic background in genotypes from these two distinct provinces could be a result of the similar environmental conditions and growth habitat. The previous studies on Triticum-Aegilops species and Triticum urartu Thum. ex Gandil. using SSR, ISSR and isozyme markers reported that geographically distant regions can be very similar in their environmental conditions and can be resulted in joint grouping of the geographically distant populations (Moghaddam et al. 2000; Moradkhani et al. 2015). In order to further dissect the relationship among and within the Aegilops species, PCoA analysis was used. In general, the PCoA biplot agreed with the cluster analysis, with slight differences (Fig. 2). High diversification within Ae. tauschii and Ae. biuncialis Genet Resour Crop Evol was confirmed since they were spread along the PCo plot. The first axis could differentiate nearly all Ae. tauschii accessions which fell into the right part of the scatter plot. The below right quadrant contained Ae. tauschii genotypes from Mountainous Shirvan (Azerbaijan) and Tbilisi (Georgia) (circle II), while upper quadrant contained Ae. tauschii from Nakhchivan (Azerbaijan) and Kvemo Karti (Georgia) (circle I). Two distinct groups within Ae. biuncialis can be also distinguished. On contrary to the dendroqram Ae. cylindrica genotypes grouped in close proximity to Ae. tauschii. Aegilops tauschii is considered the putative D-genome donor for Ae. cylindrica Host (Badaeva et al. 2002; Pour-Aboughadareh et al. 2017). The results also substantiated those of Pour-Aboughadareh et al. (2017) and Bordbar et al. 2011. To sum up, we used molecular techniques to investigate different Aegilops species from different endemic geographical regions. All the analyses performed in the current research supported a marked genetic differentiation and strong genetic discontinuities among the Aegilops species. The grouping of Aegilops accessions from Azerbaijan and Georgia based on SSR data, first of all, was in accordance to species, while subspecies, countries, longitude, latitude and altitude did not affect the grouping within species. On the second level, accessions from the same province were often placed in the same subclusters, indicating that grouping based on genetic parameters was closely related to the geographic region within countries, while a joint grouping of provinces from different countries could be explained by the similarity in environmental conditions. The strong point of our work lies in filling gaps of molecular data on Aegilops species from Azerbaijan and Georgia. Despite the fact that Azerbaijan is considered a center of origin for diploid Ae. tauschii, accessions of the tetraploids Ae. biuncialis and Ae. triuncialis from this country were also highly diverse. 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