**1. Introduction**

The microstructure of snow is an important parameter for the modelling of microwave emission and optical reflectance [1–3], and it is therefore also important for remote sensing applications. However, a parameter describing all snow properties, including size, shape, bonding, and the orientation of snow grains, is not simple to define. The parameters most often used for that purpose are traditional grain size [4], correlation length [3,5], optical grain size [6,7], and specific surface area (SSA) [8,9]. Several methods exist to measure these directly or indirectly, but this study concentrates only on the reflectance-derived, in-situ methods.

Snow reflectance at near infrared (NIR) and short-wave infrared (SWIR) wavelengths, especially over 1000 nm, is dominated by grain size [6]. Optical effective grain size is defined as the diameter of spheres having equal optical properties compared with the original grains [6,10]. SSA is defined as the surface area of the air–ice interface per unit mass (unit m2 kg−1) [9,11]. Optical grain size and SSA are related by Equation (1):

$$SSA = \frac{6}{\rho\_{\rm ice} \, D\_0} \tag{1}$$

where *ρice* is the density of ice and *D*0 is the optical grain size, which is derived based on equations in [9,12] as presented by [13]. Grain shape has been shown to affect the reflectance-derived SSA in many wavelengths [10,14,15], and [16] presented a modelling study concerning the influence of grain shape on albedo-derived SSA, resulting in 20–25% error.

DUal Frequency Integrating Sphere for Snow SSA (DUFISSS) was presented by [13]. It uses 1310 nm and 1550 nm lasers and an integrating sphere to measure hemispherical reflectance from the snow sample surface, which was converted to SSA [13,17]. The error of the SSA measurement was 10% based on [13]. The measurement method is similar to that of the IceCube, which is described in Section 2.2. The Profiler of Snow Specific Surface area Using SWIR reflectance Measurement (POSSSUM) and the Alpine Snow Specific Surface Area Profiler (ASSSAP) [18] are also based on the same principle. A contact spectroscopy method is presented by [7] to observe the optical grain diameter from a vertical profile of snowpack based on reflectance measurements of a band near 1030 nm by using a FieldSpec FR and attached modified Contact Probe (ASD Inc., Longmont, CO, USA). The derivation of optical grain size was based on an ice absorption model by [2]. The optical grain size and traditional grain size had poor correlation with less robust results for rounded grains, which is assumed to relate to the effect of grain shape. A review of past field experiments, where reflectance or albedo is compared to some of those parameters describing snow microstructure, is presented next.

Near infrared photography at 850–1000 nm is a method to observe SSA from reflectance as presented by [19]. The correlation between reflectance and SSA was reported to be 0.9 and the inaccuracy of SSA 15%. A clear correlation between hemispherical reflectance derived from measured nadir SWIR spectral reflectance of snow from FiedSpec FR 350–2500 nm (ASD Inc., Longmont, CO, USA) and SSA measured with Brunauer Emmett Teller (BET) analysis [9,20] was presented by [8]. Additionally, they did not find any effect from grain shape on the results. A correlation between grain size and a ratio of 1160 nm and 1260 nm hemispherical reflectance is showed by [21]. The accuracy of average grain size estimation was presented to be ±0.2 mm. Spectral albedo was measured with the Autosolexs instrument (400–1050 nm) to estimate SSA [22]. The accuracy of SSA depended on the solar zenith angle and the leveling of the instrument, and error was presented to be 15%. The SSA derived from Autosolexs had poor correlation with ASSSAP measurements; however, the wavelength and vertical resolutions of the instruments were different. The Automatic Spectro-Goniometer is presented for hemispherical–directional reflectance measurements [23]. The measurements were compared with Discrete Ordinates Radiative Transfer Program for a Multi-Layered Plane-Parallel Medium (DISORT)-modelled reflectance by using spheres with radii equal to the surface-area-to-volume ratio derived from stereological analysis, resulting in model underestimation with a maximum mean root-mean-square error (RMSE) of 0.09% for 1300 nm. A study where albedo was modelled with DISORT based on SSA measured with DUFISSS resulted in 1.1% difference to measured albedo with FieldSpec pro JR (ASD Inc., Longmont, CO, USA) [24]. It is estimated that 1% error in albedo corresponds approximately with 15 m2 kg−1 error in SSA [16]. In addition, the optical diameter of snow grains with data derived from near infrared photography or FieldSpec (ASD Inc., Longmont, CO, USA) spectral albedo or reflectance observations is researched by [25–29]. Grain size or size distribution was measured traditionally or from image-processing of microphotography.

The presented methods from previous studies are primarily intended for measuring reflectance from the surface of the snowpack, and they are not suitable for measuring vertical profiles except for contact spectroscopy with the Contact Probe and near-infrared photography. Since solar radiation is subject to variability, originating from, for example, zenith angle and cloud cover, it is beneficial that the instrument used includes an internal light source for stable illumination. A newly developed instrument, QST (ASD Inc., Longmont, CO, USA), was tested for performing rapid measurements of vertical snow profiles. The hand-held instrument measures almost bidirectional reflectance with similar optical geometry to that of the Contact Probe. The instrument was tested on taiga snowpack in the Arctic Space Centre of the Finnish Meteorological Institute in Sodankylä in northern Finland. QST has been used previously for other purposes such as the detection of ion concentrations in soil [30] and heavy metal pollution in soil [31]. Snow measurements are more challenging due to the deeper penetration depth of the radiation, the fragile structure of the snowpack, and melting of the snow.

Previous results of SSA measurements from a taiga snowpack in Sodankylä include the comparison of SSA and optical grain size to modelling results. The optical grain size derived from SSA measurements of IceCube was compared to SNOWPACK [32–34] modelled optical grain size in [35,36]. SNTHERM [37,38], SNICAR [39], and MOSES [40] modelled optical grain sizes were compared to IceCube-derived optical grain size by [37]. Moreover, the spectral reflectance of a taiga snowpack has previously been measured with a portable Field Spec pro JR spectrometer in the Sodankylä area and a similar mast-based spectrometer located at the same area as the measurements performed for this study [41,42]. In addition, measurements of bidirectional reflectance factor have been performed with a goniospectropolariphotometer [43,44].

The aim of this study was to test the suitability of the QST instrument for measuring the reflectance of snow, and the main conclusion of the study was that an empirical relationship between SSA and QST reflectance exists. The paper has the following structure: Section 2 presents field measurements and methods, results are presented in Section 3, and discussion is made in Section 4.

#### **2. Materials and Methods**

Snow measurements have been performed at the Intensive Observation Area (IOA) in Sodankylä in northern Finland since 2006 (Figure 1) for the calibration, validation, and development of remote sensing instruments and interpretation algorithms. Vegetation in the cleared area among sparse pine forests consists of lichen, moss, heather, crowberry, and lingonberry, whose growth rate is approximately 0.4 cm per year in the snow pit area [45]. Typically, the average maximum snow depth (~80 cm) occurs in late March. The average air temperature is below 0 ◦C from November to April, and the average wind speed is low (1–2 m s−1) in the area as described in [35]. Several automated instruments measuring snow, soil, radiation, and meteorological parameters are installed in the IOA, in addition to manually recorded snow pit measurements [35]. The data set for this study was taken from snow pit measurements made in spring 2017 (22 February, 7 March, 16 March, 21 March, and 3 April) with clear or partially cloudy sky conditions.

**Figure 1.** (**a**) Location of Sodankylä in northern Finland; (**b**) snow pit measurement site in Sodankylä.

#### *2.1. Layers, Traditional Grain Size, and Density*

Horizontal layers of the snowpack were defined manually with a brush and toothpicks. Layers were defined according to visual appearance, grain type, grain size, hardness, and wetness. Macrophotography-based, traditional grain size, the largest extent of an average grain [4], was estimated from macro-photographs taken against a 1 mm reference grid. The density profile was measured every 5 cm with a 10 cm × 10 cm × 5 cm rectangular sampler and digital scale with ±1 g accuracy. More detailed information of the methods is presented by [35].

#### *2.2. SSA with IceCube*

The reflectance and SSA was observed with IceCube manufactured by A2 Photonic Sensors, Grenoble, France. IceCube measures the hemispherical reflectance of a 1310 nm laser from the snow sample surface (Figure 2a). A photodiode observes diffuse radiation from the integrating sphere originating from the laser and reflected from the sample. The snow sample is collected with a spatula (or a specific tool), packed into the sample holder to reach a minimum density of 200 kg m −3, and the surface is levelled (Figure 2b). The sample holder is 2.5 cm high and 6 cm in diameter. Software provided by the manufacturer converts the observed voltage value to reflectance using the calibration results. The instrument is calibrated for every measurement occasion using reference plates of different reflectivity. The calibration curve is fitted to the six calibration measurements. Determination of SSA from the reflectance is based on DISORT modelling and depends also on optical parameters of the integrating sphere [13]. In this study, the SSA samples were taken in 3 cm intervals from the vertical profile of the snowpack. A more detailed description of the IceCube measurement procedure is presented in [35] and a discussion on measurement errors is presented in [46].

Additional testing of the measurement accuracy occurred on 21 March 2017. The measurement of each sample from the profile was repeated three times with IceCube by rotating the sample in the azimuth direction between measurements. In addition, three samples were taken from both the surface layer and the depth hoar layer to test the effect of the sampling procedure and packing density. Those samples were also weighed to determine the sample density.

**Figure 2.** (**a**) The IceCube measurement; (**b**) the IceCube sample; (**c**) the QualitySpec Trek (QST) measurement from the snowpack vertical profile; (**d**) the QST measurement from sampled snow.

#### *2.3. Reflectance with QualitySpec Trek*

QST is a portable spectrometer manufactured by ASD Inc., Longmont, CO, USA (Figure 2c,d). QST measures the almost bidirectional reflectance of NIR and SWIR radiation at 350–2500 nm wavelengths with a spectral resolution of 9.8 nm at 1400 nm. The instrument has three detectors: 350–1000 nm (512-element silicon array), 1001–1785 nm (InGaAs photodiode), and 1786–2500 nm (InGaAs photodiode). The instrument also has an internal light source and internal gray scale reference

for optimization and wavelength calibration. The light source is a Quartz Tungsten Halogen bulb with a color temperature of 2870 K ±33 K. Illumination and viewing geometry is presented in Figure 3. The window of QST is approximately 1 cm in diameter, and the whole window is illuminated by the internal light source. The window and materials inside the instrument are designed to minimize specular reflections. The angle from the instrument window to the light source is 55◦ and the angle to the fiber optic is 78◦. The fiber optic cable has a field of view of 25◦, which means that the field of view is 0.82 cm on the window of the instrument. The Contact Probe [7] has the same measurement geometry as QST. Additional calibration is made before every measurement occasion with a separate white reference plate. The reference plate is attached in front of the instrument window with magnetic plugs. It is important that the white reference plate is clean and that the manufacturer provides information for cleaning and replacing the plate. Full-spectrum dark reference is measured also during the start-up with an internal shutter, the light source is turned off, and the white reference is plugged. The dark reference (background) value is subtracted from raw data prior to the reflectance calculation. The sample count averaging time can be set to 1, 2, 5, or 10 s. The instrument scans the entire wavelength range 10 times per second and produces an average of those. The sample is set to physical contact with the instrument window (Figure 2c,d and Figure 3), so cleaning of the window between the measurements is therefore required. A measurement is stored by pushing a trigger button. Sound signals for starting and finishing the measurement notes when the instrument needs to be in stationary position with the (snow) sample. The most recent data is shown on the screen. Audio note recording is possible after the measurement. The instrument also stores coordinates and elevation from the internal GPS for each sample. Automatic and manual data storage options are available. The resulting reflectance is absolute reflectance (reflectance normalized with reference reflectance). Since the instrument is commercial, calibration data and data from the single spectrums before averaging are not available.

The measurement procedure included a white reference measurement, instrument setup (choosing how many times the wavelength range is scanned for averaging), and snow measurement (repeated). Cleaning of the window was rarely needed. Automatic data storage was used. The integration time for a measurement was set to 1 or 2 s. A longer integration time meant more averaged spectrums and therefore less noise for an acquisition. On the other hand, the instrument remained steadier in snow with a shorter integration time. Two procedures related to how the instrument can be used are (1) vertical reflectance profile from the snowpack of the snow pit wall and (2) reflectance from the snow surface without digging a snow pit. The measurements were made with the first procedure from the snowpack profile by pushing the instrument window steadily against the snow pit wall during the measurement (Figure 2c). The window had physical contact to the snow so that the distance between the snow and fiber optic cable was always constant and the measurement configuration did not change. The measurements were made at approximately 2–5 cm intervals from the vertical profile. Some of the IceCube samples were also measured with QST by pushing the window against the snow in the middle of the sample surface (Figure 2d).

**Figure 3.** Illumination and viewing configuration. Fiber optic and light source have the same azimuth angle. Fiber optic cable has 25◦ field of view. Diameter of the window is approximately 1 cm.

#### *2.4. Methods for Comparison of QST and IceCube Measurements*

Reflectance profiles were measured with QST from both the snowpack and the sampled snow on 21 March and 3 April 2017. The two profiles were measured with different height intervals, and therefore both profiles were linearly interpolated for every centimeter (no extrapolation). A comparison presented in Section 3.3 was made only for the heights where both interpolated reflectance values existed.

The empirical relationship between SSA and reflectance was studied with 1160 and 1260 nm reflectance-dependent coefficient Q, which is the ratio of reflectance at the bottom of the ice absorption feature and the reflectance change in the ice absorption feature. The absorption coefficient of ice is larger at 1260 nm than at 1160 nm. Q is calculated with Equation (2) as

$$Q = \frac{R\_{1260}}{R\_{1160} - R\_{1260}} \tag{2}$$

where *R*1160 is reflectance at 1160 nm and *R*1260 is reflectance at 1260 nm.

Similarly, interpolation was also needed to compare SSA and reflectance-derived Q from the same heights of snow. Linear interpolation without extrapolation was made for SSA when the reflectance profiles from the snowpack were studied. For 21 March, the values from the first of the three SSA measurements of the same sample were used. The comparison presented in Section 3.4 was made with interpolated SSA values from the same heights as the original reflectance profile measurements from the snowpack. Interpolation was not needed in the case of the reflectance profile from sampled snow because the same samples were measured with both instruments.

Clearly erroneous reflectance values measured with QST were removed from the analysis (>0.1 difference to the closest value without fitting to other values around). One of those was from the snowpack profile in 16 March (63 cm height), another one was from the snowpack profile in 21 March (70 cm height), and the two last ones were from the sampled snow in 21 March (48 and 51 cm height).


**2. SSR distribution in** *P. vera*

Motalebipour et al. [14] performed a genome survey study in pistachio and estimated genome size of pistachio as 600 Mb. The authors used 26.77 Gb Illumina PE (pair-end) reads of Siirt cultivar to estimate genome size and to reveal SSR distribution in pistachio genome. Motalebipour et al. [14] detected a total of 59,280 di-, tri-, tetra-, penta-, and hexa-nucleotide SSR motifs, and the dinucleotide motifs were the most abundant type of repeats (63.2%) in pistachio (**Figure 1**), followed by tri- (18.0%), tetra- (12.8%), penta- (3.8%), and hexanucleotide motifs (2.2%).

Motalebipour et al. [14] searched repeat motifs in their genome survey study in pistachio, and AT/AT (23.0%) and TA/TA (21.6%) were the most abundant repeats, followed by AG/CT (7.6%) and GA/TC (6.2%), AAT/ATT (4.1%), CA/TG (2.6%), and TAA/ATT (2.6%) (**Figure 2**). The most abundant tetra- and penta-nucleotide

#### **Figure 1.**

*Distribution of 59,280 SSRs in the pistachio genome based on repeat type. Obtained from Motalebipour et al. [14].*

#### **Figure 2.**

*Distribution of SSR motifs in pistachio at 40× coverage sequencing data. The X-axis represents motif types, and the Y-axis represents the count of motifs in the genome of pistachio. Obtained from Motalebipour et al. [14].*

repeat motif types were AAAT/ATTT (2.1%) and AAAAT/ATTTT (0.44%), respectively (**Figure 2**). Among hexanucleotide motifs, AAAAAT/ATTTTT and GCCCAA/TTGGGC (0.07%) were the most abundant motifs. The authors calculated one SSR per 8.67 kb in the pistachio genome.

Khodaeiaminjan et al. [28] had about 10x Illumina data of three *P. vera* (Siirt, Ohadi, and Bağyolu) cultivars and one *P. atlantica* genotype (Pa-18) to find pairwise polymorphic SSR loci in silico. The authors detected a total of 3821, 3833, 2024, and 4597 SSRs in Siirt, Ohadi, and Bağyolu *P. vera* cultivars and in *P. atlantica* genotype, respectively. The dinucleotide motifs were the most abundant type of repeats (48.6%) in four genotypes followed by hexa- (21.3%) and trinucleotide motifs (19.4%) (**Figure 3**).

Khodaeiaminjan et al. [28] calculated the number of repeats in 4 genotypes, and 4 and 11 repeated motifs were the most abundant followed by 12, 8, 5, and 13 times repeated motifs (**Figure 4**).

Jazi et al. [29] performed a transcriptome study by RNA sequencing of a pooled sample representing 24 different tissues of 2 pistachio cultivars with contrasting salinity tolerance under control and salt treatment in pistachio. The authors searched SSR motifs in transcriptome sequences of pistachio, and 11,337 SSRs were defined as di- to hexanucleotide motifs in 11,130 contigs. Di- and trinucleotide repeats were the most abundant SSRs, accounting for 40–44% of total SSRs, followed by tetra- (9.5%), penta- (3.1%), and hexanucleotide repeats (2.2%). The pistachio transcriptome was rich in GA/TC (12.13%), AG/CT (11.02%), AT/AT (8.32%), TA/TA (8.04%), GAA/TTC (5%), and AGA/TCT (4.02%). To make these SSRs useful, a total of 7605 primer pairs were generated from the microsatellites with sufficient flanking sequences. However, none of them were tested in *Pistacia* species.

**97**

*SSR Markers in the Genus* Pistacia

**Figure 3.**

**Figure 4.**

*Khodaeiaminjan [36].*

*DOI: http://dx.doi.org/10.5772/intechopen.89966*

*and in* P. atlantica*. Obtained from Khodaeiaminjan [36].*

**3. Development of SSRs from** *P. vera*

successfully produced PCR products.

In *P. vera*, the first SSR markers were developed by Ahmad et al. [37] using enrichment method from Kerman pistachio cultivar. The authors constructed dinucleotide (CT and CA) and trinucleotide (CTT)-enriched genomic DNA libraries. Eighty-nine clones randomly selected from 295 clones for "CT/GA"-enriched library and 57 clones (64%) contained the repeats. Sixty-two clones out of 250 clones, randomly selected from "CA/GT"-enriched library, and 37 (59%) clones contained the repeats. Of the total 151 clones, 67 (44%) clones had sufficient flanking sequence for primer design. Thirty-three clones were selected randomly for "CTT/GAA"-enriched library. Eight (24%) clones had sufficient flanking sequence for primer design. The authors tested these primer pairs in commercially important American, Iranian, Turkish, and Syrian pistachio cultivars, and 14 (56%) SSR loci

*The number of motif repeats in* P. vera *Siirt, Ohadi, and Bağyolu cultivars and in* P. atlantica. *Obtained from*

*The number of di-, tri-, tetra-, penta-, and hexanucleotide SSRs in Siirt, Ohadi, and Bağyolu* P. vera *cultivars*

#### **Figure 3.**

*The number of di-, tri-, tetra-, penta-, and hexanucleotide SSRs in Siirt, Ohadi, and Bağyolu* P. vera *cultivars and in* P. atlantica*. Obtained from Khodaeiaminjan [36].*

#### **Figure 4.**

*The number of motif repeats in* P. vera *Siirt, Ohadi, and Bağyolu cultivars and in* P. atlantica. *Obtained from Khodaeiaminjan [36].*

### **3. Development of SSRs from** *P. vera*

In *P. vera*, the first SSR markers were developed by Ahmad et al. [37] using enrichment method from Kerman pistachio cultivar. The authors constructed dinucleotide (CT and CA) and trinucleotide (CTT)-enriched genomic DNA libraries. Eighty-nine clones randomly selected from 295 clones for "CT/GA"-enriched library and 57 clones (64%) contained the repeats. Sixty-two clones out of 250 clones, randomly selected from "CA/GT"-enriched library, and 37 (59%) clones contained the repeats. Of the total 151 clones, 67 (44%) clones had sufficient flanking sequence for primer design. Thirty-three clones were selected randomly for "CTT/GAA"-enriched library. Eight (24%) clones had sufficient flanking sequence for primer design. The authors tested these primer pairs in commercially important American, Iranian, Turkish, and Syrian pistachio cultivars, and 14 (56%) SSR loci successfully produced PCR products.

Kolahi-Zonoozi et al. [27] developed SSRs using the FIASCO protocol (Fast Isolation by AFLP of Sequences Containing Repeats) from genomic DNA of *P. vera* cv Akbari. A total of 234 clones were sequenced, and 125 (53.4%) contained SSR motifs. A total of 90 clones having repeats close to the edge of the insert was eliminated, and 35 clones remained and were used to design 42 primer pairs. The authors tested 42 SSR primer pairs in 45 Iranian pistachio cultivars for amplification and polymorphism. Sixteen primer pairs (38.1%) successfully produced scorable bands, and 12 pairs (28.6%) showed polymorphism in 45 pistachio cultivars. The most common repeat motifs in our study were dinucleotides. A total of 32 alleles was obtained with an average of 2.75. The *PIC* values ranged from 0.19 to 0.56 with an average of 0.33. The expected heterozygosity (*He*) varied from 0.081 to 0.518 with an average of 0.345, while the observed heterozygosity (*Ho*) ranged from 0.023 to 0.930 with a mean of 0.490.

Zaloğlu et al. [16] used enrichment method and developed SSR markers from *P. vera* cv Siirt. Genomic libraries enriched with the repeats of "CA," "GA," and "AAC" and "AAG" were constructed. A total of 94 clones (58.8%) contained repeats, and the CA-enriched library contained the highest number (32 clones; 86.5%), followed by the GA-enriched library (25 clones; 61%), whereas the AAC (16 clones; 45.7%)- and AAG (22 clones; 46.8%)-enriched libraries had the lowest number of clones containing repeats. From each library 57 clones were selected and a total of 228 clones was sequenced. A total of 84 primer pairs was designed, 59 generated (70.2%) bands, and 47 polymorphic in the characterization of 7 diverse pistachio cultivars. The number of alleles ranged from two to nine with an average of 3.6. A higher number and frequency of SSRs was obtained from dinucleotide-enriched libraries than trinucleotide libraries. The SSR loci from the CA enriched library (*Ho* = 0.49) were more homozygous than those from the other libraries (*Ho* = 0.59–0.61). The AAC-enriched library (*He* = 0.55) was the least informative, whereas the GA library (*He* = 0.71) was the most informative; the CA and AAG libraries had intermediate values. As a result, the GA-enriched library was the best, whereas the AAC library was the worst among the four libraries in terms of perfect repeats, number of alleles, polymorphism, and informativeness. The AAG-enriched library was also good because of its perfect repeats, observed heterozygosity, and numbers of amplified and polymorphic loci. The authors suggested to use the GA- and AAG-enriched libraries in further SSR marker development studies in pistachio. A higher frequency of SSRs was obtained from the dinucleotide-enriched (73.1%) libraries than the trinucleotide-enriched (46.3%) ones. The dinucleotide-enriched libraries had a higher number of alleles (*Na* = 3.8) and effective number of alleles (*Ne* = 3.0) than the trinucleotide-enriched libraries (3.3 and 2.5, respectively). The dinucleotide-enriched libraries (*He* = 0.67) were more informative than the trinucleotide ones (*He* = 0.59). The 59 SSR primer pairs were tested in 8 different *Pistacia* species, and 54 were transferable to at least 1 *Pistacia* species. The SSR loci in trinucleotide (72.7%)-enriched libraries had higher transferability than dinucleotide (54.7%) ones.

Topçu et al. [17] sequenced more clones from GA- and AAG-enriched libraries based on suggestion done by Zaloglu et al. [16]. A total of 192 clones was sequenced from each library, 135 primer pairs were designed, and 110 generated PCR products. Topçu et al. [17] tested 110 SSR loci in 12 diverse pistachio cultivars for amplification and polymorphism. A total of 46 loci from the GA library and 18 loci from the AAG library was polymorphic in *P. vera*. A total of 64 polymorphic loci produced 264 alleles with an average of 4.13 alleles per locus. The observed (*Ho*) and expected heterozygosity (*He*) values were 0.52 and 0.56, respectively, while average polymorphism information content (*PIC*) was 0.51. One hundred out of 110 SSR loci were transferable to at least one of the tested 10 *Pistacia* species. *P. eurycarpa* that is the closest species to *P. vera* had the highest number of transferable loci, whereas *Pistacia texana* and *P. lentiscus* that are the farthest species to *P. vera* had the lowest number of transferable loci.

**99**

**4.1** *P. atlantica*

*SSR Markers in the Genus* Pistacia

*DOI: http://dx.doi.org/10.5772/intechopen.89966*

ranged between 0.04 and 0.85 with an average of 0.50.

Motalebipour et al. [14] obtained 40× sequencing data from *P. vera* cv. Siirt to develop SSR markers. A total of 59,280 SSR motifs was detected with a frequency of 8.67 kb in pistachio. The authors randomly selected 950 SSR loci and screened them in three *P. vera* cultivars (Siirt, Ohadi, and Bağyolu) in one *P. atlantica* genotype (Pa-18). A total of 610 loci (64.2%) generated amplification products, 197 (20.7%) loci were monomorphic, and the remaining 143 (15.1%) SSR loci failed to generate amplification products. Of the 610 that amplified, 204 produced polymorphic and easily scorable bands. Of these, 193 were perfect (94.6%), 8 (3.9%) were compound, and 3 (1.5%) were interrupted repeats. Dinucleotide motifs were the most abundant (63.2%), followed by tri- (18.0%), hexa- (12.8%), tetra- (3.8%), and pentanucleotide motifs (2.2%). Two SSR primer pairs amplified at two loci, and 206 SSR loci were obtained and used to study genetic diversity in *Pistacia*. The authors tested these 206 loci in 24 pistachio cultivars along with 20 wild accessions belonging to five *Pistacia* species (four accessions from each one). A total of 2036 alleles was obtained from 206 SSR loci ranging from 2 to 19 alleles with an average of 9.88 in testing 44 *Pistacia* accessions. Of the analyzed 206 SSR loci, 41 were polymorphic, and 136 had amplifications in all tested 6 *Pistacia* species. The observed heterozygosity (*Ho*) ranged from 0.0 to 0.82 with an average of *Ho* = 0.41. The average *He* value was 0.74, which ranged between 0.08 and 0.91. The *PIC* values ranged from 0.08 to 0.90, with an average of 0.71. All 206 SSR loci generated amplification products in 24 *P. vera* culticars, and a total of 897 alleles was produced with an average of 4.5 alleles per locus. Two hundred (97.1%) SSR loci were polymorphic in 24 pistachio cultivars. The observed heterozygosity (*Ho*) value ranged from 0.00 to 1.00 with an average of 0.46. The expected heterozygosity (*He*) values varied from 0.04 to 0.87 with an average of 0.55. The polymorphism information content values

Khodaeiaminjan et al. [28] used in silico approach to develop polymorphic SSR markers in pistachio. The authors compared 10× Illumina sequencing data of three *P. vera* (Siirt, Ohadi, and Bağyolu) cultivars and one *P. atlantica* genotype (Pa-18) to find pairwise polymorphic SSR loci in silico*,* and 750 loci were detected. The authors tested all 750 loci in 18 *P. vera* cultivars and 6 *P. atlantica* genotypes, and they obtained 625 polymorphic loci from 618 SSR primer pairs. A total of 3947 alleles was obtained from 625 loci with an average of 6.2 allele per locus. A total of 613 (98.1%) SSR loci in 18 *P. vera* cultivars and 544 SSR loci (87.0%) in six *P. atlantica* genotypes was polymorphic. The numbers of alleles were 2631 within *P. vera* and 2183 in *P. atlantica*. The lowest genetic diversity values were obtained from the Bagyolu-Pa-18 pairwise combination, while in silico SSR loci from Ohadi-Pa-18 pair produced high genetic diversity values. In the analysis of 24 *Pistacia* genotypes, a total of 613 (98.1%) loci was polymorphic, and the average number of alleles (*Na*) was 6.3 ranging from 2 to 20. The average expected heterozygosity (*He*) and observed heterozygosity (*Ho*) values were 0.67 and 0.53, respectively. The average polymorphism information content value was 0.63. The highest genetic diversity values were produced by SSR loci from the Ohadi-Pa18 and Ohadi-Siirt pairwise combinations,

whereas the lowest values were in SSR loci from the Bagyolu-Pa18 pair.

There is only one study using *P. atlantica* DNAs to develop SSRs by Khodaeiaminjan et al. [28]. The authors used 10× coverage sequencing data of monoecious *P. atlantica* genotype, namely, Pa-18, and generated 4597 SSR loci.

**4. SSRs in wild** *Pistacia* **species**

#### *SSR Markers in the Genus* Pistacia *DOI: http://dx.doi.org/10.5772/intechopen.89966*

Motalebipour et al. [14] obtained 40× sequencing data from *P. vera* cv. Siirt to develop SSR markers. A total of 59,280 SSR motifs was detected with a frequency of 8.67 kb in pistachio. The authors randomly selected 950 SSR loci and screened them in three *P. vera* cultivars (Siirt, Ohadi, and Bağyolu) in one *P. atlantica* genotype (Pa-18). A total of 610 loci (64.2%) generated amplification products, 197 (20.7%) loci were monomorphic, and the remaining 143 (15.1%) SSR loci failed to generate amplification products. Of the 610 that amplified, 204 produced polymorphic and easily scorable bands. Of these, 193 were perfect (94.6%), 8 (3.9%) were compound, and 3 (1.5%) were interrupted repeats. Dinucleotide motifs were the most abundant (63.2%), followed by tri- (18.0%), hexa- (12.8%), tetra- (3.8%), and pentanucleotide motifs (2.2%). Two SSR primer pairs amplified at two loci, and 206 SSR loci were obtained and used to study genetic diversity in *Pistacia*. The authors tested these 206 loci in 24 pistachio cultivars along with 20 wild accessions belonging to five *Pistacia* species (four accessions from each one). A total of 2036 alleles was obtained from 206 SSR loci ranging from 2 to 19 alleles with an average of 9.88 in testing 44 *Pistacia* accessions. Of the analyzed 206 SSR loci, 41 were polymorphic, and 136 had amplifications in all tested 6 *Pistacia* species. The observed heterozygosity (*Ho*) ranged from 0.0 to 0.82 with an average of *Ho* = 0.41. The average *He* value was 0.74, which ranged between 0.08 and 0.91. The *PIC* values ranged from 0.08 to 0.90, with an average of 0.71. All 206 SSR loci generated amplification products in 24 *P. vera* culticars, and a total of 897 alleles was produced with an average of 4.5 alleles per locus. Two hundred (97.1%) SSR loci were polymorphic in 24 pistachio cultivars. The observed heterozygosity (*Ho*) value ranged from 0.00 to 1.00 with an average of 0.46. The expected heterozygosity (*He*) values varied from 0.04 to 0.87 with an average of 0.55. The polymorphism information content values ranged between 0.04 and 0.85 with an average of 0.50.

Khodaeiaminjan et al. [28] used in silico approach to develop polymorphic SSR markers in pistachio. The authors compared 10× Illumina sequencing data of three *P. vera* (Siirt, Ohadi, and Bağyolu) cultivars and one *P. atlantica* genotype (Pa-18) to find pairwise polymorphic SSR loci in silico*,* and 750 loci were detected. The authors tested all 750 loci in 18 *P. vera* cultivars and 6 *P. atlantica* genotypes, and they obtained 625 polymorphic loci from 618 SSR primer pairs. A total of 3947 alleles was obtained from 625 loci with an average of 6.2 allele per locus. A total of 613 (98.1%) SSR loci in 18 *P. vera* cultivars and 544 SSR loci (87.0%) in six *P. atlantica* genotypes was polymorphic. The numbers of alleles were 2631 within *P. vera* and 2183 in *P. atlantica*. The lowest genetic diversity values were obtained from the Bagyolu-Pa-18 pairwise combination, while in silico SSR loci from Ohadi-Pa-18 pair produced high genetic diversity values. In the analysis of 24 *Pistacia* genotypes, a total of 613 (98.1%) loci was polymorphic, and the average number of alleles (*Na*) was 6.3 ranging from 2 to 20. The average expected heterozygosity (*He*) and observed heterozygosity (*Ho*) values were 0.67 and 0.53, respectively. The average polymorphism information content value was 0.63. The highest genetic diversity values were produced by SSR loci from the Ohadi-Pa18 and Ohadi-Siirt pairwise combinations, whereas the lowest values were in SSR loci from the Bagyolu-Pa18 pair.

#### **4. SSRs in wild** *Pistacia* **species**

#### **4.1** *P. atlantica*

There is only one study using *P. atlantica* DNAs to develop SSRs by Khodaeiaminjan et al. [28]. The authors used 10× coverage sequencing data of monoecious *P. atlantica* genotype, namely, Pa-18, and generated 4597 SSR loci. The authors tested 625 loci in 6 *P. atlantica* genotypes, and all had amplification, while 544 (87.0%) were polymorphic. The number of alleles (*Na*) ranged from 1.0 to 8.0 with a mean of 3.5. The mean of observed heterozygosity (*Ho*) was 0.51, while the average *PIC* and expected heterozygosity (*He*) values were 0.53 and 0.52, respectively.

Motalebipour et al. [14] developed 206 SSR loci from *P. vera* and tested them in 4 diverse *P. atlantica* genotypes. A total of 200 SSR loci generated amplification products with a high rate of transferability (97.1%). Thirty-nine (19.5%) of the amplified SSR loci were monomorphic, and the rest were polymorphic (80.5%). A total of 527 alleles was produced by 161 polymorphic SSR loci, ranging from 2 to 7 per locus with an average of 3.3 alleles per locus. The observed heterozygosity (*Ho*) ranged from 0.0 to 1.00 with an average of *Ho* = 0.48. The average *He* value was 0.56, which ranged from 0.22 to 0.84. The *PIC* values ranged from 0.19 to 0.82, with an average of 0.49.

Zaloğlu et al. [16] and Topçu et al. [17] developed 59 and 110 SSRs from *P. vera* and tested them for amplification in *P. atlantica*, and a total of 47 and 96 SSR loci was amplified*,* respectively. So, the authors published a total of 143 SSR loci for further studies in *P. atlantica* from both studies. The authors did not test their polymorphism levels in *P. atlantica*.

### **4.2** *P. khinjuk*

Arabnezhad et al. [31] were the first in developing SSRs from *P. khinjuk*, who constructed two enriched DNA libraries with dinucleotide (AG) and trinucleotide (ATG) microsatellite motifs*.* Thirty-six contained microsatellite motifs from 44 sequenced clones. Among them, a higher proportion of microsatellites (71%) were simple perfect, and the remaining SSRs identified as interrupted perfect (17%) and complex imperfect (11%) repeats. Nine of the sequences contained too short flanking DNA sequence to design primer pairs; thus only 27 primer pairs were designed and tested in six *Pistacia* species. Of 27 primer pairs, 25 pairs successfully amplified SSR loci in *P. khinjuk* with expected size. Five primer pairs were subsequently discarded due to low rate of amplification across six *Pistacia* species. The authors tested the remaining SSRs in a total of 18 *Pistacia* genotypes (13 *P. vera* cultivars and 1 genotype from each of *P. khinjuk, P. atlantica, P. mutica, P. integerrima*, and *P. palaestina*). The primer pairs produced 114 alleles in 18 *Pistacia* genotypes. In 13 *P. vera* cultivars, the average number of alleles per locus was 2.8, ranging from one to six. In all *Pistacia* accessions, the average values of *He* and *PIC* were 0.61 and 0.56, respectively, while values of these diversity parameters calculated 0.45 and 0.38 when only *P. vera* genotypes were considered.

Zaloğlu et al. [16] and Topçu et al. [17] developed 59 and 110 SSRs from *P. vera* using enrichment method, and a total of 39 and 96 SSR loci had amplification*,* respectively. It is still necessary to test 135 SSR loci for polymorphism in *P. khinjuk* for further studies.

#### **4.3** *P. lentiscus*

The first SSR development study in *P. lentiscus* was published by Albaladejo et al. [30] who used di- (GA, GT, AT, GC), tri- (CAA, ATT, GCC), and tetranucleotide (GATA, CATA, ATAG) genomic-enriched libraries. The authors randomly selected 163 clones and 75 (46%) had microsatellite motifs. A total of 21 primer pairs was designed and tested in 16 individuals. Eight of 21 primer pairs displayed consistent and polymorphic patterns, whereas the others were discarded due to producing monomorphic and multibanding patterns and failing in amplification. Forty-two

**101**

*SSR Markers in the Genus* Pistacia

an average of 0.41.

**4.4** *P. chinensis*

to 0.89, respectively.

amplified in *P. chinensis*.

**4.5** *P. weinmannifolia*

*chinensis.*

*DOI: http://dx.doi.org/10.5772/intechopen.89966*

both studies to use them in the future.

individuals from two populations sampled in Southern Spain were used for characterization of eight loci. A total of 59 alleles was detected, ranging from 3 to 13 per

Motalebipour et al. [14] developed 206 SSR loci from *P. vera* and tested them in four *P. lentiscus* genotypes. A total of 151 SSR loci was amplified with a 73.3% transferability rate. Of the amplified SSR loci, 83 (55.0%) were polymorphic. A total of 217 alleles was obtained from 83 polymorphic SSR loci in *P. lentiscus,* ranging from 1 to 6, with an average of 2.6 alleles per locus. The observed heterozygosity (*Ho*) values ranged from 0.00 to 1.00 with an average of 0.50. The expected heterozygosity (*He*) values varied from 0.22 to 0.78 with an average of 0.49. The polymorphic information content (*PIC*) values ranged between 0.19 and 0.75 with

Zaloğlu et al. [16] and Topçu et al. [17] developed 59 and 110 SSRs from *P. vera* and tested them for amplification in *P. lentiscus*, and a total of 31 (52.5%) and 76 (69.1%) SSR loci was amplified*,* respectively. The authors did not test their polymorphism levels in *P. lentiscus*. Therefore, there are 107 SSR loci for *P. lentiscus* from

Ding and Lu [33] developed 12 polymorphic microsatellite loci from *P. chinensis* using a microsatellite-enriched genomic library based on magnetic beads. These loci were characterized in 24 individuals from 3 populations located on Thousand Island Lake, Zhejiang Province, China. The number of alleles per locus varied from 3 to 16. The mean number of alleles per locus was between 3.3 and 4.0 at the population level. The observed and expected heterozygosity ranged from 0.12 to 0.87 and 0.23

Motalebipour et al. [14] developed 206 SSR loci from *P. vera* and tested them in 4 diverse *P. chinensis* genotypes, and 177 SSR loci produced amplification products with 85.9% transferability rate. Of the amplified loci, 119 loci (67.2%) were polymorphic. A total of 365 alleles was amplified by 119 polymorphic SSR loci with an average of 3.1 alleles per locus*.* The average observed heterozygosity (*Ho*) was 0.48, and it ranged between 0.00 and 1.00. The expected heterozygosity (*He*) values varied from 0.22 to 0.84 with an average of 0.54. The polymorphism information content (*PIC*) values ranged between 0.19 and 0.82 with an average of 0.48 in *P.*

Zaloğlu et al. [16] and Topçu et al. [17] developed SSRs from *P. vera* and tested them for amplification in *P. chinensis*. A total of 121 SSR loci had amplification PCR products in *P. chinensis,* with 71.6% transferability rate. However, the authors did not test polymorphism levels of the amplified SSR loci in *P. chinensis*. Chen et al. [32] developed 14 SSR loci from *P. weinmannifolia*, and 9 of them (64.3%) were

Chen et al. [32] developed SSRs using the FIASCO protocol (Fast Isolation by AFLP of Sequences Containing Repeats) from genomic DNA of *P. weinmannifolia*. A total of 205 clones was sequenced, 147 contained SSR motifs, and 94 allowed primer design with sufficient flanking regions. The primer pairs were tested for polymorphism in 24 individuals from 2 populations, and 14 produced polymorphic microsatellite loci with an average of 4.1 alleles (ranging from 1 to 9) per locus in *P. weinmannifolia*. The expected (*H*e) and observed (*H*o) heterozygosities ranged from 0.000 to 0.906 and from 0.000 to 0.933, respectively. Ten of these loci

locus. The expected heterozygosity ranged from 0.139 to 0.895.

#### *SSR Markers in the Genus* Pistacia *DOI: http://dx.doi.org/10.5772/intechopen.89966*

individuals from two populations sampled in Southern Spain were used for characterization of eight loci. A total of 59 alleles was detected, ranging from 3 to 13 per locus. The expected heterozygosity ranged from 0.139 to 0.895.

Motalebipour et al. [14] developed 206 SSR loci from *P. vera* and tested them in four *P. lentiscus* genotypes. A total of 151 SSR loci was amplified with a 73.3% transferability rate. Of the amplified SSR loci, 83 (55.0%) were polymorphic. A total of 217 alleles was obtained from 83 polymorphic SSR loci in *P. lentiscus,* ranging from 1 to 6, with an average of 2.6 alleles per locus. The observed heterozygosity (*Ho*) values ranged from 0.00 to 1.00 with an average of 0.50. The expected heterozygosity (*He*) values varied from 0.22 to 0.78 with an average of 0.49. The polymorphic information content (*PIC*) values ranged between 0.19 and 0.75 with an average of 0.41.

Zaloğlu et al. [16] and Topçu et al. [17] developed 59 and 110 SSRs from *P. vera* and tested them for amplification in *P. lentiscus*, and a total of 31 (52.5%) and 76 (69.1%) SSR loci was amplified*,* respectively. The authors did not test their polymorphism levels in *P. lentiscus*. Therefore, there are 107 SSR loci for *P. lentiscus* from both studies to use them in the future.

#### **4.4** *P. chinensis*

Ding and Lu [33] developed 12 polymorphic microsatellite loci from *P. chinensis* using a microsatellite-enriched genomic library based on magnetic beads. These loci were characterized in 24 individuals from 3 populations located on Thousand Island Lake, Zhejiang Province, China. The number of alleles per locus varied from 3 to 16. The mean number of alleles per locus was between 3.3 and 4.0 at the population level. The observed and expected heterozygosity ranged from 0.12 to 0.87 and 0.23 to 0.89, respectively.

Motalebipour et al. [14] developed 206 SSR loci from *P. vera* and tested them in 4 diverse *P. chinensis* genotypes, and 177 SSR loci produced amplification products with 85.9% transferability rate. Of the amplified loci, 119 loci (67.2%) were polymorphic. A total of 365 alleles was amplified by 119 polymorphic SSR loci with an average of 3.1 alleles per locus*.* The average observed heterozygosity (*Ho*) was 0.48, and it ranged between 0.00 and 1.00. The expected heterozygosity (*He*) values varied from 0.22 to 0.84 with an average of 0.54. The polymorphism information content (*PIC*) values ranged between 0.19 and 0.82 with an average of 0.48 in *P. chinensis.*

Zaloğlu et al. [16] and Topçu et al. [17] developed SSRs from *P. vera* and tested them for amplification in *P. chinensis*. A total of 121 SSR loci had amplification PCR products in *P. chinensis,* with 71.6% transferability rate. However, the authors did not test polymorphism levels of the amplified SSR loci in *P. chinensis*. Chen et al. [32] developed 14 SSR loci from *P. weinmannifolia*, and 9 of them (64.3%) were amplified in *P. chinensis*.

#### **4.5** *P. weinmannifolia*

Chen et al. [32] developed SSRs using the FIASCO protocol (Fast Isolation by AFLP of Sequences Containing Repeats) from genomic DNA of *P. weinmannifolia*. A total of 205 clones was sequenced, 147 contained SSR motifs, and 94 allowed primer design with sufficient flanking regions. The primer pairs were tested for polymorphism in 24 individuals from 2 populations, and 14 produced polymorphic microsatellite loci with an average of 4.1 alleles (ranging from 1 to 9) per locus in *P. weinmannifolia*. The expected (*H*e) and observed (*H*o) heterozygosities ranged from 0.000 to 0.906 and from 0.000 to 0.933, respectively. Ten of these loci

contained dinucleotide repeat motifs, and four loci had complex repeat motifs. The authors tested 14 primer pairs in *P. chinensis* and *P. mexicana* for their transferability, and 9 (64.2%) loci in *P. chinensis* and 4 (28.6%) loci in *P. mexicana* were successfully transferable.

Zaloğlu et al. [16] developed 59 SSRs from *P. vera* and tested them for amplification in *P. weinmannifolia*, and a total of 23 SSR loci was amplified with a 39.0% transferability rate. However, the authors did not test their polymorphism levels within *P. weinmannifolia*.

#### **4.6** *P. integerrima*

There is no study in the literature developing SSRs from *P. integerrima* tissues. Arabnezhad et al. [31] developed 27 SSR loci in *P. khinjuk*, and 18 (66.7%) were successfully amplified in *P. integerrima*. Two previous studies [16, 17] developed a total of 169 SSRs from *P. vera* and tested them for amplification in *P. integerrima*. A total of 147 SSR loci had amplification products with 87.0% transferability rate. There is a necessity to test the amplified loci for polymorphism within *P. integerrima*.

Motalebipour et al. [14] developed 206 SSR loci from *P. vera* and tested them in four diverse *P. integerrima* genotypes. A total of 193 SSR loci generated amplification products with a high rate of transferability (93.7%). Of the amplified SSR loci, 157 (81.3%) were polymorphic in *P. integerrima*. A total of 416 alleles was produced by 157 SSR loci with an average of 2.70 alleles per locus. The average observed (*Ho*) and expected (*He*) heterozygosities were 0.50 and 0.52, respectively. The expected heterozygosity (*He*) values varied from 0.22 to 0.78 with an average of 0.55. The polymorphism information content (*PIC*) values ranged between 0.19 and 0.75 with an average of 0.44.

#### **4.7** *P. terebinthus*

There is no SSR development study also from *P. terebinthus* tissues in the literature. Motalebipour et al. [14] generated 206 SSR loci from *P. vera* and tested them in *P. terebinthus* genotypes, and 183 SSR loci produced amplification products with 88.8% transferability rate. Of amplified SSRs, 142 were polymorphic. A total of 416 alleles was produced by 142 polymorphic SSR loci*.* The number of alleles (*Na*) ranged from 1 to 7 with an average of 3.4 alleles per locus. The observed heterozygosity (*Ho*) ranged from 0.0 to 1.0 with an average of 0.47. The expected heterozygosity (*He*) values varied from 0.22 to 0.84 with an average of 0.56. The polymorphic information content (*PIC*) values changed between 0.19 and 0.82 with an average of 0.50.

Zaloğlu et al. [16] and Topçu et al. [17] developed 169 SSRs from *P. vera* and analyzed them in *P. terebinthus* for amplification. A total of 128 SSR loci had PCR products in *P. terebinthus,* with 71.6% transferability rate. However, the authors did not test polymorphism levels of the amplified SSR loci in *P. terebinthus*.

#### **4.8** *P. texana, P. mexicana,* **and** *P. eurycarpa*

Chen et al. [32] developed 14 SSR loci from *P. weinmannifolia*, and 4 of them (28.6%) were amplified in *P. mexicana*. Zaloğlu et al. [16] and Topçu et al. [17] developed 59 and 110 SSRs from *P. vera* and tested them for amplification in *P. mexicana*, and a total of 33 and 77 SSR loci was amplified in *P. mexicana*, respectively. Topçu et al. [17] generated 110 SSRs from *P. vera* and tested them for amplification in *P. eurycarpa* and *P. texana*. A total of 100 and 76 SSR loci was amplified with a 90.9 and 69.1% transferability rates. However, Zaloğlu et al. [16]

**103**

Khodaeiaminjan et al. [28].

*SSR Markers in the Genus* Pistacia

*cana, P. texana*, and *P. eurycarpa.*

*DOI: http://dx.doi.org/10.5772/intechopen.89966*

**5. Use of SSR markers in the genus** *Pistacia*

and Topçu et al. [17] did not test polymorphism levels of the SSR loci in *P. mexi-*

There are only several studies characterizing *Pistacia* species and *P. vera* accessions using SSR markers. Pazouki et al. [38] used SSR markers and characterized 304 *Pistacia* accessions belonging to *P. khinjuk*, *P. vera,* and *P. atlantica* subsp. *kurdica*. The authors indicated lower level of polymorphism and variation within *P. atlantica* subsp. *kurdica* than *P. vera* and *P. khinjuk*. Motalebipour et al. [14] used 1505 alleles amplified by 136 SSR primer pairs for phylogenetic analysis of 6 *Pistacia* species. The closest species to *P. vera* was *P. atlantica*, and *P. integerrima*, *P. chinensis*, *P. terebinthus* followed it, while *P. lentiscus* was the most diverse species to the cultivated pistachio. The structure analysis confirmed the cluster analysis as well. Albaladejo et al. [30] used 8 SSR loci to characterize 42 *P. lentiscus* accessions belonging to 2 *P. lentiscus* populations. The number of alleles changed between 3 and 13 by obtaining a total of 59 alleles. The expected heterozygosity ranged from 0.139 to 0.895. Two *P. lentiscus* populations were separated clearly in cluster analysis. Chen et al. [32] characterized 24 *P. weinmannifolia* accessions using 14 SSR loci. The number of allele changed between one and nine with an average of 4.1. *Ho* values were between 0.000 and 0.933, while *He* values ranged from 0.000 to 0.906. Ahmad et al. [37] used 14 SSR loci to characterize Iranian, Turkish, and Syrian pistachio cultivars. A total of 46 alleles was produced by 14 SSR loci ranging from 2 to 5 allele per loci. Cluster analysis placed most of the Iranian samples in one group, while the Syria samples were the most diverse and did not group in a single cluster. Kolahi-Zonoozi et al. [27] described 45 Iranian pistachio accessions by 12 SSR loci. The *PIC* values changed between 0.19 and 0.56 with an average of 0.33. The average *Ho* and *He* values were 0.490 and 0.345, respectively. Khodaeiaminjan et al. [28] characterized 18 pistachio cultivars from different origins by 2631 SSR alleles using 625 SSR loci. The constructed dendrogram separated pistachio cultivars mainly in two groups according to their geographical origin: one group contained the cultivars originated from Iran, and the second group included cultivars originated from Mediterranean countries such as Turkey, Syria, Greece, and Italy. Siirt cultivar (origin is Southeast part of Turkey) placed between two main groups. The results were in agreement with a hypothesis on diffusion of pistachio cultivars suggested by Kafkas et al. [19] who hypothesized that the Siirt cultivar is in a transition subcluster between Iranian and Mediterranean cultivars and pistachio cultivation diffused from its center of origin, the Iranian-Caspian region, via southeastern Turkey to Syria, the Mediterranean region of Europe, and North Africa. Motalebipour et al. [14] used 1505 alleles from 136 SSR primer pairs for genetic diversity analysis of 24 pistachio cultivars, and similar results were obtained with the study performed by

The SSR markers in the genus *Pistacia* were also used for genetic linkage map construction and QTL analysis. Khodaeiaminjan et al. [28] constructed the first SSR-based genetic linkage map of pistachio using an F1 segregating population derived from a cross between "Siirt" and "Bağyolu" cultivars. A total of 385 SSR markers was mapped along with 15 chromosomes, and the consensus map had 1511.3 cM length with an average of 25.6 SSR markers per LG, and the average distance between the markers was 3.9 cM with a 0.25 marker density. The first QTL study in pistachio was performed by Motalebipour et al. [35] who constructed a genetic linkage map of pistachio using an inter-specific F1 population and SSR markers. The authors mapped a total of 388 SSR markers along with 15 linkage

and Topçu et al. [17] did not test polymorphism levels of the SSR loci in *P. mexicana, P. texana*, and *P. eurycarpa.*