It is expected that there will be a significant similarity between the wheat varieties used in this experiment due to their genetic resources.

In this experiment, some elite Egyptian bread wheat cultivars (17) were selected in different regions in Egypt as shown in Table (1). These cultivars have been obtained from Gene Bank, Egyptian Ministry of Agriculture.

Twenty five seeds of each wheat varieties have been planted in pots in greenhouse. After two weeks of planting, the plants were enough to get fresh leaves and were collected from each sample. The samples were immediately transferred to liquid nitrogen tank to prevent deterioration. Weighed about 1.5 g from plant leaves samples and ground by liquid nitrogen to obtain fine powder. Total genomic DNA was isolated from leaves of each of the seventeen varieties according to the protocol described by Anderson et al. (1992), with a few modifications intended to improve the quality of DNA: two consecutive extractions with phenol: chloroform (1:1) were carried out by an additional wash of 97% (left at -20 °C for one hour) an 70% pre-cooled ethanol, respectively. The yield and quality of DNA were assessed by spectrophotometer and gel electrophoreses.

Sixteen microsatellite primer pairs (SSR) were selected from (Röder et al., 1998), Table 2. Amplification reactions were carried out in a total volume of 25 μL, which contained 250 nM of each primer (Metabion GmbH, Germany), 0.2 mM of each deoxynucleotide, 1.5 mM MgCl₂, 1 unit Taq polymerase (Bioline, GmbH, Germany) and 50 – 100 ng of template DNA. All reaction volumes were 25 μL overlaid with a drop of mineral oil. The thermocycling program (MJ Research PTC-100 thermal cycler) used was: one cycle at 94 °C for 3 min, 45 cycles at 94 °C for 1 min at either 50, 55 or 60 °C (depending on the individual microsatellite), 2 min at 72 °C, and the final extension step of 10 min at 72 °C. Electrophoresis was done to visualize the PCR amplified product. It was carried out on 2.0% agarose gel and amplified fragments were visualized by staining wit ethidium bromide. Fragment sizes were determined with PyElph 1.4 software, is the commercial program Quantity One from Bio-Rad (Pavel and Vasile, 2012). The fragment size in “Chinese Spring” was taken as standard Röder et al., (1998).

The fragment(s) sizes in ‘Chinese Spring’ were taken as standard, and the size differences of the fragments in other genotypes were considered to be the result of alterations in the repeat number of the simple sequences at the corresponding site(s). All distinct DNA fragments scored as present (1) or absent (0) were used to compute pair-wise similarity coefficients (Jaccard, 1908) for each of the markers for the purpose of assessing genetic diversity leading to cluster analysis. PowerMarker software (v3.0, 2004) is statistical software for genetic marker data analysis (Liu and Muse, 2005) was used for estimating of allele number, allele frequencies, genotype, heterozygosity and polymorphic information content (PIC). However, Marker index (MI) was calculated according to Powell et al. (1996), MI = Average polymorphic information content (PIC) x Proportion of polymorphic bands x Average number of loci per assay unit.

For phylogenetic analysis, data only from the polymorphic SSR loci were subjected to MVSP software statistical software (Kovach Computing Services, Pentraeth, Wales, U.K). All the 17 wheat varieties were clustered based on the estimated genetic distance. The phylogenetic analysis was carried out with the clustering method of the unweighted Pair Group Method Using Arithmetic Average (UPGMA).

The original 1 – 0 data matrix was used for calculating a correlation matrix between pairs of markers. The correlation matrix was employed for the calculation of eigenvalues, which were then used for determining the coordinates for each genotype that were used for PCA.

The results of PCR amplification of a number of microsatellite loci in seventeen Egyptian bread wheat cultivars using sixteen microsatellite primer pairs are summarized in (Table 2). Altogether, 190 alleles at 16 loci were obtained with average 11.875 alleles per locus. The maximum number of alleles detected at Xgwm32-3A belonging to (GA) n was 20 alleles with size ranged from 163-179 bp. However, the minimum number of alleles detected at Xgwm156-5A belonging to (GT) n and Xgwm157-2D (CT) n was 8 alleles with size from 270-299 bp and 107-113 bp respectively, Table 2. Allele frequency per locus varied from eight (Xgwm156-5A and Xgwm157-2D) to twenty Xgwm32-3A. It is known that microsatellite primer pairs are locus specific and that is meant to be a single locus marker comparing with other molecular markers as RFLP probes, RAPD, ISSR and SCoT primers which multilocus (Vivodík et al., 2016). In the present study, the 16 loci that were assigned to specific chromosomes were able to distinguish between 17 Egyptian bread wheat and thus useful for detecting polymorphism.

Such a discriminatory set should also ensure the uniform distribution of the microsatellite primers of this set across the three genomes of bread wheat since in several studies, including the present study, microsatellites have been shown to be more frequent in the A and B genomes, than in the D genome (Röder et al., 1998; Stephenson et al., 1998).

The SSRs relative with them, more alleles detected at (CA) n and (AC) n one loci each, however, (GA) n loci 4 loci, (CT) n and (GT) n loci5 loci each. Röder et al. (1995) reported that (GT) n repeats to be more polymorphic than other simple repeats such as (GA) n in wheat. However, in barley more alleles were detected for (GA) n repeats than for (GT) n repeats (Struss and Plieske 1998). The results were obtained agreement with (Dreisigacker et al., 2004; Dvojkovic et al., 2010; Spanic et al., 2012) and opposed with Kumar et al. (2016). The loci of microsatellite are multi-allelic and the alleles co-dominant, proved that the microsatellite marker strict in determined DNA polymorphism and highly informative genetic markers (Devos et al., 1992; Devos et al., 1993; Xie et al., 1993; Mason, 2015) than other markers as single nucleotide polymorphisms (SNPs) which are biallelic and dominant. The earlier studies proved that four microsatellite markers set each had the ability to distinguish between 24 genotypes in barley and 16 genotypes in tomato (Rusell et al., 1997; Bredemeijer et al., 1998). Furthermore, the microsatellite set could be used to distinguish each genotype in a set of more than 100 wheat genotypes (Manifesto et al., 1999).

Polymorphism Information Content (PIC) was estimated for 16 loci (Table 2). The PIC values ranged from 0.8137 of locus Xgwm157-2D to 0.9342 of locus Xgwm32-3A with an average of 0.8669. As well as, the heterozygosity values were estimated as well, ranging from 0.8235 to 1.00 with average 0.9816. The marker index (MI) value over all 16 microsatellite markers was 0.8530. The heterozygosity was detected in three loci (Xgwm71.2-2A, Xgwm113-4B and Xgwm161-3D) with values 0.9412, 0.9412 and 0.8235 respectively, (Table 3). The genotype number per locus was estimated based on allelic frequency data, where the highest number detected 17 of locus Xgwm32-3A and lowest number founded 10 of locus Xgwm156-5A and Xgwm157-2D with an average 13.6250 (Table 2). The data of microsatellite loci and the corresponding alleles were used to calculate the polymorphic information content (PIC) and heterozygosity (H) to evaluate a marker system for its ability to detect high levels of DNA polymorphism in an analysis of genetic diversity. In earlier studies on bread wheat, the PIC values were ranged from 0.23 to 0.79 (Röder et al., 1995) and from 0.29 to 0.79 (Plaschke et al., 1995) and 0.21 to 0.90 (Prasad et al., 2000). On the other hand, the PIC values obtained by (Bohn et al., 1999) were very low (0.30). However, (Saeidi et al., 2006; Khalighi et al., 2008; Tahernezhad et al., 2010; Mir et al., 2012; Arora et al., 2014) studies were the opposite of the previous results and consistent with our results where PIC value > (0.7). The marker index is also used to measure the efficiency of polymorphism Khodadadi et al., (2011).

The marker index (MI) value was calculated also for used markers and was close to (0.8530) the MI value (0.70) reported by (Prasad et al., 2000), however those results were higher than the MI value (0.21) obtained by (Bohn et al., 1999) on SSRs wheat. When comparing the MI value with other markers found that, SAMPL (9.61) and AFLP (3.41) in bread wheat (Bohn et al., 1999). However, MI value (6.14) that is intermediate between the above two contrasts was also available for AFLPs in soybean (Powell et al., 1996).

In order to investigate, genetic relationships between 17 Egyptian wheat genotypes cluster analysis based on Jaccard’s similarity coefficients and UPGMA algorithm were calculated for the 43 durum wheat germplasm. A Jaccard's genetic similarity matrix is presented in Table 4a and 4b. The average similarity among 17 Egyptian bread wheat was 0.357. The nearest neighbor cluster analysis obtained from Jaccard's similarity coefficient (Figure 1) illustrated the variability between 17 Egyptian bread wheat. The detected of DNA polymorphism by 16 microsatellite markers allowed of estimates genetic distance and clustering of 17 Egyptian bread wheat cultivars in two major groups. The first group (Group I) included Misr 1, Misr 2, Gimmasa 7, Gimmasa 7, Gimmasa 9, Gimmasa 10, Gimmasa 11, Gimmasa 12 and Giza 168. The second group (Group II) included the other cultivars Sids 8, Sids 12, Sids 13, Sakha 93, Sakha 94, Sakha 69, Shandaweel 1, Bani Sweef 3 and Bani Sweef 4. The similarities between 17 Egyptian bread wheat based on 16 microsatellite markers were ranged from 0.109 in Shandweel 1, Bani Sweef 3 and Bani Sweef 4 cultivars to 0.524 in Gimmaza 7 and Gimmaza 9 with average of 0.357. A Jaccard’s genetic similarity matrix was estimated between pairs of Egyptian wheat cultivars using 16 microsatellite markers through cluster analysis. This study used UPGMA cluster analysis based on genetic similarity values for SSR alleles from all the wheat cultivars to construct a dendrogram (Figure 1). The similarity value between 17 Egyptian wheat cultivars was ranged from 0.109 to 0.524 with average value 0.357. This average of genetic similarity value (0.357) can be compared with other studies, whereas SSR-based genetic similarity coefficient values of 0.31 (Plaschke et al., 1995) and 0.57 (Bohn et al., 1999). However, STS-based genetic similarity coefficient value of 0.81 (Chen et al., 1994) were reported. In these different studies on genetic diversity in bread wheat, undertaken using a variety of molecular markers, the variation in genetic similarity coefficient values may be attributed either to the differences in number of genotypes and the probes/primers used (e.g. 119 RFLP probes were used by Paull et al. 1998) or to the relative superiority of microsatellites to detect DNA polymorphism. An unusually low value of RFLP-based genetic similarity (0.18) reported by Paull et al. (1998) is certainly due to the larger sample of 124 diverse genotypes and bigger set of 119 RFLP probes used in this study. From the previous study it illustrated the importance and usefulness of the microsatellites (SSR) technique in wheat (Prasad et al., 1999; Roy et al., 1999). Based on the results obtained are considered the microsatellites technique was sensitive and critical in differentiating between the different varieties of Egyptian wheat under study, as well as in determining the polymorphism, genotype identification, genetic similarity and estimation of genetic diversity.

The principle component analysis was used to visualize the genetic relationships among genotypes shown in Figure 2. Of the total polymorphism, only 40.44% was accounted for by the first two components, implying that the used markers possessed a suitable dispersion of markers in the genome. The 17 Egyptian wheat cultivars were clustered into two groups. The principle component analysis thus is largely compatible to those from cluster analysis obtained from UPGMA.