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Article

Genetic Diversity and Population Structure in Türkiye Bread Wheat Genotypes Revealed by Simple Sequence Repeats (SSR) Markers

by
Aras Türkoğlu
1,†,
Kamil Haliloğlu
2,*,†,
Seyyed Abolgahasem Mohammadi
3,
Ali Öztürk
2,
Parisa Bolouri
2,
Güller Özkan
4,
Jan Bocianowski
5,*,
Alireza Pour-Aboughadareh
6 and
Bita Jamshidi
7
1
Department of Field Crops, Faculty of Agriculture, Necmettin Erbakan University, 42310 Konya, Turkey
2
Department of Field Crops, Faculty of Agriculture, Ataturk University, 25240 Erzurum, Turkey
3
Department of Plant Breeding and Biotechnology, Faculty of Agriculture, University of Tabriz, Tabriz 5166616471, Iran
4
Department of Biology, Faculty of Science, Ankara University, 06100 Ankara, Turkey
5
Department of Mathematical and Statistical Methods, Poznań University of Life Sciences, Wojska Polskiego 28, 60-637 Poznań, Poland
6
Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj 31585-854, Iran
7
Department of Food Security and Public Health, Khabat Technical Institute, Erbil Polytechnic University, Erbil 44001, Iraq
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Genes 2023, 14(6), 1182; https://doi.org/10.3390/genes14061182
Submission received: 4 May 2023 / Revised: 19 May 2023 / Accepted: 26 May 2023 / Published: 29 May 2023
(This article belongs to the Special Issue Genetic and Genomic Approaches for Breeding in Wheat)
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Abstract

:
Wheat genotypes should be improved through available germplasm genetic diversity to ensure food security. This study investigated the molecular diversity and population structure of a set of Türkiye bread wheat genotypes using 120 microsatellite markers. Based on the results, 651 polymorphic alleles were evaluated to determine genetic diversity and population structure. The number of alleles ranged from 2 to 19, with an average of 5.44 alleles per locus. Polymorphic information content (PIC) ranged from 0.031 to 0.915 with a mean of 0.43. In addition, the gene diversity index ranged from 0.03 to 0.92 with an average of 0.46. The expected heterozygosity ranged from 0.00 to 0.359 with a mean of 0.124. The unbiased expected heterozygosity ranged from 0.00 to 0.319 with an average of 0.112. The mean values of the number of effective alleles (Ne), genetic diversity of Nei (H) and Shannon’s information index (I) were estimated at 1.190, 1.049 and 0.168, respectively. The highest genetic diversity (GD) was estimated between genotypes G1 and G27. In the UPGMA dendrogram, the 63 genotypes were grouped into three clusters. The three main coordinates were able to explain 12.64, 6.38 and 4.90% of genetic diversity, respectively. AMOVA revealed diversity within populations at 78% and between populations at 22%. The current populations were found to be highly structured. Model-based cluster analyses classified the 63 genotypes studied into three subpopulations. The values of F-statistic (Fst) for the identified subpopulations were 0.253, 0.330 and 0.244, respectively. In addition, the expected values of heterozygosity (He) for these sub-populations were recorded as 0.45, 0.46 and 0.44, respectively. Therefore, SSR markers can be useful not only in genetic diversity and association analysis of wheat but also in its germplasm for various agronomic traits or mechanisms of tolerance to environmental stresses.

1. Introduction

Bread wheat (Triticum aestivum L.) is one of the most important species belonging to the genus Triticum in the Poaceae family [1]. The genomic structure (2n = 6x = 42, AABBDD) of this cereal consisted of three diploid genomes AA, BB and DD, which are inherited from three ancestral species—Triticum urartu Thuman ex Gandil (A genome), Aegilops speltoides Tausch (B genome) and Aegilops tauschii Coss (DD genome) [2]. The rather large genome size (17,000 Mb) and the high rate of repetitive sequences (80%) are important issues to overcome in bread wheat research [3]. Therefore, efficient and sufficient tools should be used in bread wheat genome research.
The green revolution has resulted in increased yield and quality in wheat production and emergence of high-yield varieties. As the world’s population continues to grow, climate change and the resulting global warming are having a serious impact on food supplies. World wheat production is expected to increase by about 50% by 2050 to meet the food needs of a growing population [4,5]. However, in recent decades, wheat yields have not been able to increase sufficiently in the world [6], as well as in Türkiye [7]. As a result, wheat production is unable to meet demand. Given the negative effects of climate change and the growing world population, which is expected to exceed 9 billion by 2050, the need to increase wheat production to ensure global food security is a high priority [8]. In this case, the biggest challenge for wheat farmers is to improve grain yields and crop tolerance to various environmental stressors to meet growing demands [9]. Wheat has accumulated quite a large amount of genetic variability during its evolution. Today, such a large amount of genetic diversity has generally decreased due to repeated cultivation, adaptation, development and use of local varieties for desirable traits [10]. However, the increased homogeneity of the genetic background has become a major challenge for the future genetic development of wheat.
Plant breeding programs mainly focus on genetic diversity, inheritance, conservation and evolution [11]. Homogeneity in a population would mean that all members of that population behave similarly in the face of a stressor and could not withstand an epidemic [12]. Potential new alleles can be used to overcome such adverse conditions [13]. Genetic diversity is a key topic for the adaptation and survival of wheat species to biotic and abiotic stressors, as such stressors are expected to be major constraints to food security [14]. On the other hand, domestication and selection pressures, as well as the use of modern breeding techniques, have already narrowed the wheat gene pool [15]. National and regional strategies should be developed to characterize and preserve the genetic diversity of wheat species. The decline in the level of genetic diversity has led to the use of such genetic resources in breeding programs. Morphological and molecular markers are commonly used to characterize wheat species and assess genetic diversity. Such tools allow breeders to select genotypes that are well adapted to specific conditions and resistant to various biotic and abiotic stresses. Agromorphological markers, special quantitative traits, are often influenced by environmental factors. To address this problem, several molecular markers have emerged as biotechnological tools for studying genetic diversity and population structure [16]. With the development of biological aspects, a number of molecular marker techniques have emerged, such as random amplified polymorphic DNA (RAPD) [17], amplified fragment length polymorphisms (AFLP) [18], inter-simple sequence repeats (ISSR) [19], start codon targeted markers (SCoT) [20], Inter-primer binding site (iPBS)-retrotransposons [21], expressed sequence tag (EST) [22], single nucleotide polymorphism (SNP) [23], next-generation sequencing (NGS) [24], divergence array technology (DArT) [25] and simple sequence repeats (SSR) [26] have been developed. Of these, SSR markers served as effective molecular markers for studying genetic diversity in hexaploid Türkiye wheat embryos [7,27,28]. The number of genotypes and markers used in these studies seemed insufficient for genome-wide association mapping. SSR markers play a key role in marker-assisted selection (MAS) in wheat breeding programs [29]. Currently, SSR databases are available for various crops [30]. To date, many studies have identified SSR markers as effective tools for use in breeding programs [31]. It has been reported that SSR markers offer a more efficient choice than SNPs, due to their faster mutation rates and higher levels of polymorphism that can be found with several highly polymorphic markers [32]. Therefore, SSR markers are largely used to analyze genetic diversity and population structure, as well as to elucidate phylogenetic relationships among plant genetic resources, as such relationships play a key role in developing appropriate breeding programs [30]. SSR markers can originate from coding or non-coding regions of genomes [33]. It was previously reported that these markers located in promoter regions can affect gene expression levels, while those located in coding sequences can affect protein structure and function [34]. SSR markers have many advantages, such as co-dominance, high levels of polymorphism, chromosome specificity and high reproducibility; they are also excellent for identifying and monitoring target traits within varieties [35]. SSR markers are very efficient in wheat research due to their co-dominant structure and wide coverage across the genome [29].
Türkiye encompasses a high level of bread wheat genetic diversity as it is a major center of wheat domestication and diversity. However, there is little information on the population structure and germplasm diversity of wheat. Therefore, the main objective of this study was to investigate genetic diversity and population structure in a set of Türkiye wheat genotypes using SSR markers.

2. Materials and Methods

2.1. Genetic Materials

In this study, 63 genotypes of bread wheat (Triticum aestivum L.) were used as plant material. Variety names and locations are given in Table 1. Bread wheats were collected from eight different regions of Türkiye. All samples were obtained from the Türkiye National Gene Bank [36].

2.2. Extraction of Genomic DNA

Genomic DNA extractions were performed according to the CTAB protocol [37]. The quality of extracted DNA was assessed by agarose gel electrophoresis (0.8%).

2.3. PCR Amplification

For SSR analysis, a total of 425 SSR primers were tested on five randomly selected wheat genotypes. Of the primers tested, 120 polymorphic primers were selected for PCR amplification in all 63 wheat genotypes [38]. Subsequently, 120 out of 425 SSR markers were selected for genotyping all 63 sets of bread wheat. Details of the primers used in this study are given in ST1.
A thermocycler (SensoQuest Labcycler, Göttingen, Germany) was used for PCR amplification. PCR reactions were carried out in a volume of 10 µL and consisted of 25 ng template DNA, 0.5 U Taq polymerase, 0.25 mM dNTP, 1 µM (20 pmol) primer, 10× buffer and 2 mM MgCl2. PCR procedure included: 3 min initial denaturation at 95 °C, 38 cycles at 95 °C for 60 s, 50–60 °C (annealing temperature depending on primers for details, see Table S1) for 60 s, 120 s at 72 °C and final elongation at 72 °C for 10 min [39]. PCR products were stained with 1 µg/mL ethidium bromide and separated by polyacrylamide Mega-Gel dual vertical electrophoresis (Model C-DASG-400-50). The resulting banding pattern was visualized under UV light using a digital camera (Model Nikon Coolpix500, Nikon, Japan) [40].

2.4. Statistical Data Analysis

TotalLab TL120 software (TotalLab Ltd., Gosforth, Newcastle upon Tyne, UK) was used to generate matrices [41]. Several informative parameters such as major allele frequency (MAF), gene diversity (GD) and polymorphic information content (PIC) were estimated using Power Marker version 3.25. POPGEN1.32 software was used to determine unbiased expected heterozygosity (uHe), expected heterozygosity (Exp-Het), effective number of alleles (ne), expected heterozygosity Nei (h) and Shannon’s information index (I) values [42]. The Dice similarity index [43] was used to calculate the genetic similarity between each pair of genotypes. NTSYS-pc V2.1 was used to construct a dendrogram using the unweighted double group method with arithmetic mean (UPGMA) and SAHN clustering [25]. Principal coordinate analysis (PCoA) and molecular analysis of variance (AMOVA) were calculated using GenAlExV6.5 [44]. A clustering algorithm on the Bayesian model STRUCTURE 2.2 was used to obtain an explicit picture of genetic composition [45]. For this analysis, input values and parameters were selected as described by Evanno et al. [45]. Finally, the number of actual sub-populations was determined using the Structure Harvester website [46]. MCMC chains were run with a firing period of 100,000 iteration, followed by 100,000 iterations using a model that allowed for admixture and correlated allele frequencies.

3. Results

3.1. Marker Polymorphism, Genetic Diversity and Principal Coordinate Analysis (PCoA)

Information on the descriptive parameters of SSR markers is shown in Table 2. Of the 425 markers, 120 showed polymorphisms. Genetic variation in SSR loci of bread wheat genotypes was calculated based on Na, MAF, Exp-Het, uHe, GD, H, NE, I and PIC values (Table 2). It was confirmed that 120 SSR loci had a total of 651 alleles in 63 wheat genotypes. The number of alleles per polymorphic locus varied between 2. 00 (BARC 37, BARC 64, BARC 80, BARC 88, BARC 89, BARC 94, BARC 152, BARC 175, BARC 240, CFA, 2, CFA 2187, CFA 152, CFA 2070, CFA 2099, CFD 18, CFD 190, GWM 160, GWM 340, GWM 391, GWM 443, WMC 42, WMC 261, WMC 320, WMC 333, WMC 336, WMC 420, WMC 524, WMC 580, WMC 765, WMC 805, WMC 807, WMC 173 and WMS 72) and 19. 0 (WMC 500) with an average value of 5.442. MAF values ranged from 0.143 (WMC 500) to 0.984 (BARC 37, BARC 80, BARC 88, BARC 89, BARC 94, BARC 152, CFA 2187, CFD 18, GWM 340, WMC 261, WMC 320, WMC 333, WMC 524 and WMC 807) with an average value of 0.631 (Table 2).
Exp-He ranged from 0.00 (BARC 37, BARC 94, CFA 2070 and GWM 340) to 0.359 (GWM 350) with a mean of 0.124. uHe values ranged from 0.00 (BARC 37, BARC 94, CFA 2070 and GWM 340) to 0.319 (GWM 350) with a mean of 0.112 (Table 2). GD values ranged from 0.031 (BARC 37) to 0.920 (WMS 46) with a mean value of 0.460. The highest Ne, H and I values were 1.578 (GWM 350), 1.667 (WMC 687) and 0.459 (GWM 350), respectively, while the lowest Ne, H and I values were 1. 00 (BARC 37, BARC 94, BARC 206, CFA 2070, GWM 340), 0.556 (BARC 206) and 0.00 (BARC 37, BARC 94, BARC 206, CFA 2187 and WMC 173) with mean values of 0.110, 1.187 and 0.165, respectively. PIC values ranged from 0.031 (BARC 37, BARC 80, BARC 88, BARC 89, BARC 94, BARC 152, CFA 2187, CFD 18, GWM 340, WMC 261, WMC 320, WMC 333, WMC 524, WMC 807) to 0.915 (WMS 462) with a mean value of 0.430 (Table 2).
Principal coordinate analysis (PCoA) was conducted using Nei’s neutral genetic distance. The three principal coordinates explained 12.64, 6.38 and 4.90% of genetic diversity, respectively (23.92% diversity in total). The presence of genetic diversity was confirmed by the distribution of genotypes in the diagram (Figure 1). The results of the AMOVA showed that the fraction of genetic diversity within populations was greater than between them (78% vs. 22%) (Table 3).

3.2. Genetic Distance and Cluster Analysis for SSR Markers

Phylogenetic relationships were investigated for 63 bread genotypes using 120 SSR markers. Dice similarity coefficients were calculated for the 120 SSR markers, and a UPGMA tree was generated (Figure 2). Genetic diversity (GD) values ranged from 0.184 to 0.420 with a mean of 0.279. The highest GD was observed between genotypes G1 and G27, while the lowest was found between four wheat samples (G10 and G11; G41 and G; G43 and G; G62 and G63). The wheat genotypes were grouped into three main clusters. The first cluster (Cluster I) was divided into two sub-clusters. The first sub-cluster (G I-1) included 29 genotypes (G63, G62, G60, G59, G58, G57, G56, G61, G55, G54, G53, G52, G50, G49, G51, G48, G46, G45, G44, G47, G42, G43, G41, G40, G39, G38, G37, G36 and G35). The second sub-cluster (G II-2) included 27 genotypes (G27, G26, G29, G34, G33, G32, G31, G30, G28, G25, G24, G23, G22, G21, G20, G19, G18, G17, G16, G15, G14, G10, G13, G9, G12, G11 and G6). The second main cluster (Cluster II) had two sub-clusters, the first sub-cluster (G II-1) including four genotypes (G2, G4, G5 and G3) and the second sub-cluster (G II-2) including two genotypes (G7 and G8). The third main cluster (Cluster III) had only one genotype (G1). It was observed that most genotypes were collected within Cluster I (Figure 2).

3.3. Population Genetic Structure Analysis for SSR Markers

The results of STRUCTURE analysis classified all tested genotypes into three sub-populations (sub-population A—red, sub-population B—green and sub-population C—blue) with a membership probability as <0.8 (Figure 3). Accordingly, sub-population A included 18 wheat genotypes (28.57%, G20, G21, G30, G23, G26, G24, G22, G13, G28, G25, G15, G16, G18, G14, G29, G31, G19 and G27). Sub-population B included 28 wheat genotypes (44.44%, G53, G57, G58, G56, G40, G52, G54, G60, G43, G45, G47, G49, G55, G50, G51, G48, G61, G46, G42, G59, G41, G44, G63, G39, G37, G62, G38 and G36). Sub-population C included 11 wheat genotypes (17.46%, G4, G2, G5, G3, G6, G1, G9, G7, G11, G10 and G8). In addition, six wheat genotypes (9.52%) including G33, G32, G17, G34, G35 and G12, were placed in mixed groups. The values of the F-statistic (Fst) for the first, second and third sub-populations were estimated to be 0.253, 0.330 and 0.244, respectively. The expected heterozygosity (He) was determined as 0.452 for the first, 0.463 for the second and 0.444 for the third sub-population (Table 4 and Table 5). Accordingly, sub-populations A and C were identified as the most diverse populations (Table 6).

4. Discussion

Detection of genetic variation using molecular markers is highly dependent on the mode of reproduction, domestication history and size of the samples analyzed. Collection, conservation and management of genetic resources are key issues in sustainable agriculture development [47]. Assessing levels and patterns of genetic diversity allows accurate classification of species and identification of individuals with desirable traits [48]. Existing genetic resources, their geographic location and relationships are commonly used to determine population diversity [49]. Comprehensive knowledge of bread wheat genetic diversity will have a significant impact on germplasm conservation and utilization. Such knowledge also facilitates breeding programs. Breeders have made significant progress in detecting various morphological traits and variation of molecular traits at the DNA level [50]. Molecular markers offer efficient tools for improving traditional breeding programs because they are not affected by environmental and developmental factors [51]. SSR markers are commonly used to analyze the genetic diversity of wheat genotypes [30]. In this study, 120 SSR markers were used to determine molecular variation and population structure in core-collection of Türkiye bread wheat genotypes.

4.1. Monitoring of Genetic Diversity

Using 120 SSR markers, 651 alleles were identified in 63 wheat genotypes. The number of polymorphic alleles ranged from 2.00 to 19.0 with an average of 5.442. Polymorphism can result from SSR expansion, contraction or interruption [52]. The current mean of polymorphic alleles was higher than the 458 [53], 49 [54] and 38 [31] values, the lower than the 1620 [48] and 939 [27] values represented in previous studies. Teshome et al. [35] reported that the number of alleles (Na) per locus ranged from 2 to 6. The current average number of alleles was lower than the values of 5.7 [55], 10 [56], 10.06 [30], 7.97 [48], 7.2 [57], 6.8 [58], 5.9 [7] and 5.89 [59] and higher than the values of 3.3 [60] and 5.05 [61] reported in previous studies. The differences in the results of these studies are mainly attributed to differences in genotypes and number of markers. The number of alleles per marker largely depends on the relative distance of the locus from the centromere, the allele frequency motif and the number of repeats [16]. Allelic diversity is also influenced by genetic composition, designating the number of alleles per locus [30].
Exp-He values ranged from 0.00 to 0.359 with an average of 0.124. The differences in Obs-He values can be attributed to several factors, including the molecular markers used, the number of selections and the geographic location of the wild–type origin and location of the samples. Our result is higher than that of Teshome et al. [62] with 0–0.05 and lower than that of Ateş Sönmezoğlu [7] with an average value of 0.75 and Tsonev et al. [63] with an average of 0.185.
Genetic diversity (GD) values ranged from 0.031 to 0.920, with an average value of 0.460. Arystanbekkyzy et al. [64] indicated that genetically distinct genotypes can facilitate breeding programs for desired traits. Henkrar et al. [65], Ateş Sönmezoğlu and Terzi [7] and Belete et al. [30] observed greater gene diversity for primers producing a higher number of alleles. Our result was lower than Tsonev et al. [63] with an average of 0.658 and Mohi-Ud-Din [53] with an average of 0.936.
In this study, the highest value of h, ne and I with 1.667, 1.578 and 0.459, respectively, were observed, while the lowest values of h, ne and I were 0.556, 1.00 and 0.00, respectively. A higher number of effective alleles indicates greater genetic diversity and is therefore generally desirable in breeding programs. The Shannon information index is also an indicator of genetic variation in a population. Teshome et al. [66] reported I values with 0.53. These values were greater than the current results. Mohi-Ud-Din et al. [53] reported the average number of effective alleles per locus as 18.32, indicating considerable diversity in the genotypes studied. The lower values of diversity indices in the present study were attributed to differences in germplasm.
PIC and MAF values indicate significant genetic variability among all wheat species used. They are also reliable indicators of genetic diversity in the plant. The current MAF values ranged from 0.143 to 0.984 with an average of 0.631. Our result was higher than Mohi-Ud-Din et al. [53] with an average of 0.296. The polymorphism information content (PIC) is used as an indicator of the diversity of a gene or DNA segment of a population. It also indicates evolutionary pressure on alleles and mutations. Current PIC values ranged from 0.031 to 0.915 with an average of 0.430. In this study, 25 markers had a PIC value of ≥0.5, indicating their potential use in wheat germplasm genetic diversity studies. Locus has high diversity when the PIC value is ≥0.5 and low diversity when the PIC value is ≤0.25 [67]. In similar studies conducted on wheat genotypes with SSR markers, the average PIC values were lower than the value of 0.62 [63], 0.65 [68], 0.65, [48], 0.52 [28], 0.50 [7], 0.57 [69], 0.83 [53] and higher than Erayman et al. [27] with an average of 0.205, Demirel [70] with an average of 0.19 and Pour-Aboughadareh et al. [54] with an average of 0.32, and Kumar et al. [51] reported an average of 0.33 PIC values. Of the 25 SSR markers, 21 had a PIC value greater than 0.800, indicating that these markers were highly informative and effective.
Principal coordinate analysis (PCoA) is commonly used to spatially represent relative genetic distances between populations [57]. It is also a multidimensional dataset that provides key patterns across multiple loci and samples. The two-dimensional diagram reflects the actual distances between genotypes [56]. In this study, the three main coordinates were able to explain 12.64, 6.38 and 4.90% (23.92% in total) of the total variation. Data were considered reliable when the explained portion of variation was ≥25% [71]. SSR-based clustering offers reliable differentiation of wheat genotypes based on their origin. In this study, significant correlations were found between PCoA clustering and cluster analysis. Mohi-Ud-Din et al. [53] indicated that PCoA was unable to group 56 genotypes based on their population. However, Pour-Aboughadareh et al. [54] found that PCoA confirmed the clustering pattern. Based on AMOVA results, there was more variability within populations (78%) than between populations (22%). Consistent with our results, Mohi-Ud-Din et al. [53] found that differences between populations accounted for 7% of total genetic diversity, with the rest (93%) attributed to differences within populations.

4.2. Genetic Identity, Genetic Distance and Clustering Anlaysis

Genetic differences between populations play a huge role in the conservation of genetic resources [72]. Our results showed that the highest genetic distance (GD) occurred between G1 and G27 and the lowest between G10 and G11; G41 and G; G43 and G; G62 and G63. Kumar et al. [12] found dissimilarity indices ranging from 0.62 to 0.85. Erayman et al. [27] reported similarity indices between 0.52 and 0.97 for all species and between 0.69 and 0.97 for wheat cultivars.
The current SSR markers were able to group all genotypes well based on phylogenetic relationships. The UPGMA method divided the present genotypes into three main clusters. Cluster I included 56 (88.88%); cluster II included 6 (9.52%); and cluster III included 1 (1.58%) genotype. UPGMA analysis showed a mix of frequencies as submissions from different geographic regions were grouped into the same subgroups. The current results showed that the clustering models were not able to clearly distinguish between wheat genotypes based on geographic origin. Clustering of genotypes showed no significant relationship between geographic origin and genetic similarity. Such a case indicated gene flow between genotypes. Differences between genotypes were attributed to the greater genetic distance between them. Grouping based on geographic origin was not clear. Such findings were also supported by analysis of population structure. The present results are consistent with those of Mohammadi et al. [71] and Pour-Aboughadareh et al. [54]. Tsonev et al. [63] divided 117 varieties into 2 major clusters, consistent with the 2 major subpopulations of the K = 2 genetic structure analysis. Mohi-Ud-Din et al. [53] used UPGMA analysis to assess the genetic diversity of wheat genotypes using SSR markers and grouped wheat genotypes into five major clusters. Pour-Aboughadareh et al. [54] found that for phylogenetic relationships, SSR markers gave better performance than gene-based techniques.

4.3. Population Diversity, Gene Differentiation and Gene Flow of Populations

Natural diversity is used to analyze population structure to detect genes/qTLs of agronomic traits [73]. Such analysis reveals similarities between genotypes and sub-populations. It has been proven to be more reliable and provide more information than other clustering algorithms [30]. The population structure facilitates the selection of different parents and the mapping of marker–trait relationships for use in breeding programs. In this study, analysis of the population structure showed that all varieties came from three subpopulations. The genetic composition of a population is largely determined by various factors, including recombination, genetic drift and natural selection. Subpopulation A contained 18 wheat genotypes (28.57%); subpopulation B contained the highest number of genotypes (28–44.44%); and subpopulation C contained 11 wheat genotypes (17.46%). In addition, 6 wheat genotypes (9.52%) were in mixed groups.
The smallest number of genotypes included in the mixed groups indicated that the genotypes present had a wide range of genetic pools. This study analyzed the population structure of wheat genotypes representing the diversity of Türkiye wheat genotypes. The Bayesian model yielded similar clustering results to UPGMA and PCoA. STRUCTURE analysis revealed three groups (A, B and C) at K = 3. Group B contained the highest number of genotypes. The present results on population structure are consistent with the findings of Mohi-Ud-Din et al. [53], dividing 56 wheat genotypes into three sub-populations, as well as Tascioglu et al. [48], dividing wheat genotypes into three sub-groups based on Bayesian model and PCA. On the other hand, the present results on population structure are not consistent with those of Le Couviour et al. [74] and Tsonev et al. [63], mainly due to the different genetic materials used in these studies. F-statistic (Fst) value was determined to be 0.253 for the first, 0.330 for the second and 0.244 for the third sub-population. Th expected value of heterozygosity (He) was determined as 0.452 for the first, 0.463 for the second and 0.444 for the third sub-population. Mohi-Ud-Din et al. [53] reported two significant differences (p < 0.01) in paired population Fst values.

5. Conclusions

To facilitate the conservation, classification and maintenance, as well as the use of these valuable genes available in genetic resources, genetic diversity analysis is needed. In Türkiye, many efforts have been made to identify the best wheat genotypes in terms of yield and agromorphological traits. Although wheat genotypes collected from some regions have been previously characterized using other marker systems, here the SSR marker set was used to assess genetic diversity and population structure in set of bread wheat genotypes. Our results showed acceptable values for average allele number, PIC, GD, Ex-He, u-He parameters. In addition, the mean values of Ne, H and I for all genotypes tested were estimated at 1.190, 1.049 and 0.168, respectively. AMOVA showed that variability within populations was higher than between them (78% vs. 22%). In addition, the Fst values for the assumed sub-populations were 0.253, 0.330 and 0.244, respectively. In conclusion, our findings again showed that there is a high level of genetic diversity among Türkiye bread wheat genotypes, which in turn can be taken into account in future wheat breeding programs.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/genes14061182/s1, Table S1: Details of 120 SSR markers, including primer name, primer sequences and chromosomal location.

Author Contributions

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

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

All data needed to conduct this study is provided within the manuscript.

Data Availability Statement

Data is contained within the article.

Conflicts of Interest

The authors declare no conflict of interest.

References

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Genes 14 01182 g001 550
Figure 1. Biplot rendered using PCoA analysis for 63 Türkiye bread wheat genotypes based on SSR marker data. Ari: Anatolia Agricultural Research Institute, Bri: Bahri Dagdas International Agricultural Research Institute, Cri: Çukurova Agricultural Research Institute, Ear: Eastern Anatolia Region, Eari: Eastern Anatolia Agricultural Research Institute, Car: Central Anatolia Region, Sri: Sakarya Agricultural Research Institute, Fci: Field Crops Central Research Institute, Tri: Trakya Agricultural Research Institute indicate, respectively.
Figure 1. Biplot rendered using PCoA analysis for 63 Türkiye bread wheat genotypes based on SSR marker data. Ari: Anatolia Agricultural Research Institute, Bri: Bahri Dagdas International Agricultural Research Institute, Cri: Çukurova Agricultural Research Institute, Ear: Eastern Anatolia Region, Eari: Eastern Anatolia Agricultural Research Institute, Car: Central Anatolia Region, Sri: Sakarya Agricultural Research Institute, Fci: Field Crops Central Research Institute, Tri: Trakya Agricultural Research Institute indicate, respectively.
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Figure 2. The UPGMA dendrogram shows the grouping of 63 Türkiye bread wheat genotypes based on 120 SSR markers data.
Figure 2. The UPGMA dendrogram shows the grouping of 63 Türkiye bread wheat genotypes based on 120 SSR markers data.
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Figure 3. Estimation of number of groups. (a) Plot of ΔK over K (range 2–10), (b) Bar plot grouping of 63 genotypes, (c) Line graphs from the mixture model of Ln P (D) and ∆K.
Figure 3. Estimation of number of groups. (a) Plot of ΔK over K (range 2–10), (b) Bar plot grouping of 63 genotypes, (c) Line graphs from the mixture model of Ln P (D) and ∆K.
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Table
Table 1. The passport of the investigated Türkiye bread wheat genotypes.
Table 1. The passport of the investigated Türkiye bread wheat genotypes.
Code Genotype/Variety NameVariety Owner Organization/OriginSeason of Sowing
G1Aksel 2000Field Crops Central Research InstituteFacultative
G2AlparslanEastern Anatolia Agricultural Research InstituteWinter
G3Altay 2000Anatolia Agricultural Research InstituteWinter
G4Atlı 2002Field Crops Central Research InstituteFacultative
G5Aytın 98Anatolia Agricultural Research InstituteWinter
G6Bağcı 2002Bahri Dağdaş International Agricultural Research InstituteFacultative
G7Bayraktar 2000Field Crops Central Research InstituteFacultative
G8Bolal 2973Anatolia Agricultural Research InstituteFacultative
G9Çetinel 2000Anatolia Agricultural Research InstituteWinter
G10Dağdaş 94Bahri Dağdaş International Agricultural Research InstituteFacultative
G11Demir 2000Field Crops Central Research InstituteFacultative
G12Doğankent 1Çukurova Agricultural Research InstituteSpring
G13Doğu 88Eastern Anatolia Agricultural Research InstituteWinter
G14Gerek 79Anatolia Agricultural Research InstituteWinter
G15Gün 91Field Crops Central Research InstituteWinter
G16Harmankaya 99Anatolia Agricultural Research InstituteWinter
G17İkizce 96Field Crops Central Research InstituteFacultative
G18İzgi 2001Anatolia Agricultural Research InstituteWinter
G19Karahan 99Bahri Dağdaş International Agricultural Research InstituteWinter
G20Kate A-1Trakya Agricultural Research InstituteWinter
G21Kıraç 66Anatolia Agricultural Research InstituteWinter
G22Kırgız 95Anatolia Agricultural Research InstituteWinter
G23Kırkpınar 79Trakya Agricultural Research InstituteFacultative
G24Kutluk 94Anatolia Agricultural Research InstituteWinter
G25LancerEastern Anatolia Agricultural Research InstituteWinter
G26MızrakField Crops Central Research InstituteFacultative
G27MüfitbeyAnatolia Agricultural Research InstituteWinter
G28NenehatunEastern Anatolia Agricultural Research InstituteWinter
G29Palandöken 97Eastern Anatolia Agricultural Research InstituteWinter
G30Pamukova 97Sakarya Agricultural Research InstituteSpring
G31PehlivanTrakya Agricultural Research InstituteWinter
G32ProstorTrakya Agricultural Research InstituteWinter
G33Seri 82Çukurova Agricultural Research InstituteWinter
G34Soyer02Anatolia Agricultural Research InstituteWinter
G35Sönmez 2001Anatolia Agricultural Research InstituteWinter
G36Sultan 95Anatolia Agricultural Research InstituteWinter
G37Süzen 97Anatolia Agricultural Research InstituteWinter
G38TosunbeyField Crops Central Research InstituteWinter
G39TürkmenField Crops Central Research InstituteFacultative
G40UzunyaylaField Crops Central Research InstituteFacultative
G41Yakar 99Field Crops Central Research InstituteFacultative
G42Zencirci 2002Field Crops Central Research InstituteFacultative
G43Ak-702Anatolia Agricultural Research InstituteWinter
G44Ak buğdayCentral Anatolia RegionWinter
G45Ankara 093/44Field Crops Central Research InstituteWinter
G46ConkesmeEastern Anatolia RegionFacultative
G47Haymana 79Field Crops Central Research InstituteWinter
G48Kılçıksız buğdayCentral Anatolia RegionWinter
G49KırikEastern Anatolia RegionFacultative
G50Kırmızı KılçıkEastern Anatolia RegionFacultative
G51Kırmızı YerliEastern Anatolia RegionFacultative
G52Koca buğdayCentral Anatolia RegionWinter
G53Köse 220/39Field Crops Central Research InstituteFacultative
G54OrsoSakarya Agricultural Research InstituteFacultative
G55Özlü buğdayCentral Anatolia RegionWinter
G56Polatlı KösesiCentral Anatolia RegionFacultative
G57Sert buğdayCentral Anatolia RegionWinter
G58Sürak 1593/51Field Crops Central Research InstituteWinter
G59TirEastern Anatolia RegionWinter
G60Yayla 305Anatolia Agricultural Research InstituteWinter
G61ZerinCentral Anatolia RegionFacultative
G62Bezostaja 1Sakarya Agricultural Research InstituteWinter
G63Karasu 90Eastern Anatolia Agricultural Research InstituteWinter
Table
Table 2. List of the used SSR primers along with the results of estimated informativeness parameters for each of them.
Table 2. List of the used SSR primers along with the results of estimated informativeness parameters for each of them.
MarkerNaMAFEx-Heu-HeGDNeHIPIC
BARC 17.000.3020.2230.2010.7731.3431.2590.3010.739
BARC 33.000.8410.0290.0270.2721.0360.6300.0470.242
BARC 243.000.9680.0420.0410.0621.0651.1110.0620.061
BARC 372.000.9840.0000.0000.0311.0001.0000.0000.031
BARC 453.000.9680.0420.0410.0621.0651.1110.0620.061
BARC 483.000.7780.1080.0960.3581.1720.8890.1380.313
BARC 5411.00.3170.1330.1210.7941.1950.8700.1870.767
BARC 598.000.3170.3020.2670.7971.4591.5830.3980.770
BARC 642.0000.9680.0430.0420.0611.0671.1110.0620.060
BARC 734.000.4130.0720.0650.6961.1070.7040.0980.642
BARC 7813.00.2380.1870.1690.8601.2761.0950.2600.845
BARC 802.000.9840.0420.0410.0311.0651.1110.0620.031
BARC 882.000.9840.0420.0410.0311.0651.1110.0620.031
BARC 892.000.9840.0420.0410.0311.0651.1110.0620.031
BARC 942.000.9840.0000.0000.0311.0001.0000.0000.031
BARC 1017.000.3170.1690.1510.7861.2560.9720.2250.755
BARC 1057.000.3330.3130.2790.7811.4951.5560.4120.750
BARC 1138.000.3330.1310.1220.7491.2001.0280.1890.708
BARC 1227.000.2540.2880.2500.7951.4011.5930.3820.764
BARC 1286.000.3490.1890.1730.7311.2951.1670.2580.687
BARC 1304.000.7620.0910.0870.3911.1380.9440.1350.358
BARC 1333.000.9050.0480.0440.1761.0750.8150.0650.168
BARC 1357.000.3170.1650.1470.7611.2561.0440.2200.723
BARC 14010.00.3490.2010.1770.8021.3021.1670.2670.780
BARC 1416.000.8250.1080.0980.3091.1580.8610.1540.297
BARC 1426.000.3170.2040.1850.7611.3101.1670.2780.722
BARC 1522.000.9840.0420.0410.0311.0651.1110.0620.031
BARC 1656.000.3490.1410.1300.7421.2250.9170.1930.699
BARC 1752.000.9370.1620.1440.1191.2471.1670.2170.112
BARC 1973.000.8570.0640.0600.2491.1050.8890.0900.225
BARC 2043.000.9520.0260.0250.0921.0461.0560.0360.089
BARC 2063.000.9210.0000.0000.1481.0000.5560.0000.141
BARC 21610.00.3810.1690.1500.7711.2361.0560.2360.743
BARC 2402.000.9680.0260.0250.0611.0461.0560.0360.060
CFA 1526.000.7140.1450.1350.4691.2361.0000.2030.448
CFA 20402.000.9840.0000.0000.0311.0001.0000.0000.031
CFA 20492.000.6670.1720.1540.4441.2761.3330.2250.346
CFA 21874.000.9050.0610.0550.1771.0890.6300.0850.169
CFA 20435.000.6980.2030.1860.4801.3431.2220.2710.448
CFA 20702.000.9210.0000.0000.1461.0001.0000.0000.135
CFA 20992.000.9680.0260.0250.0611.0461.0560.0360.060
CFA 21554.000.9520.0290.0280.0921.0410.7780.0470.091
CFA 21638.000.3170.2610.2350.7851.4111.3890.3480.753
CFA 21858.000.3810.1900.1720.7751.2991.1390.2570.747
CFA 21906.000.5080.1050.0930.6621.1400.8890.1490.618
CFA 22563.000.9370.0170.0160.1201.0250.7780.0260.116
CFA 22.000.8570.0530.0500.2451.0860.8330.0760.215
CFD 182.000.9840.0420.0410.0311.0651.1110.0620.031
CFD 495.000.4130.3240.2880.7031.4991.5930.4250.652
CFD 1902.000.8890.0280.0270.1981.0541.0000.0380.178
CFD 28713.00.1900.1900.1700.8841.2811.2000.2600.873
GWM 1602.000.7940.1670.1510.3281.2811.3330.2150.274
GWM 2994.000.9370.0230.0220.1211.0310.5930.0390.119
GWM 3144.000.7300.2190.1980.4261.3691.4440.2830.383
GWM 3193.000.8890.0560.0550.2011.1071.1110.0760.186
GWM 3376.000.4600.1070.0970.5881.1580.9170.1500.501
GWM 3402.000.9840.0000.0000.0311.0001.0000.0000.031
GWM 3507.000.2700.3590.3190.8131.5781.6300.4590.788
GWM 3685.000.7140.2090.1840.4591.3191.3700.2780.427
GWM 3828.000.3330.2250.2020.7871.3361.3060.3070.758
GWM 3912.000.7620.0970.0850.3631.1401.2220.1260.297
GWM 4135.000.5710.1530.1350.6011.2421.0440.1970.552
GWM 4432.000.9680.0890.0780.0611.1350.8330.1160.060
GWM 4933.000.5400.1550.1390.6021.2391.2220.2100.534
GWM 4976.000.5400.2170.1950.6341.3311.3610.2940.589
GWM 5013.000.7300.1740.1590.4021.2801.2590.2330.333
GWM 5337.000.3650.1800.1610.7181.2741.1390.2430.671
WMC 422.000.5710.0610.0550.4901.0980.8890.0820.370
WMC 9910.00.5560.1560.1390.6541.2360.8890.2100.630
WMC 1146.000.5080.1130.1020.6471.1720.8060.1530.595
WMC 1663.000.5870.0350.0320.5091.0570.5560.0460.408
WMC 2108.000.3020.1880.1710.7501.2821.1670.2640.707
WMC 2613.000.8410.1190.1070.2721.2050.9630.1530.242
WMC 3172.000.9840.0210.0200.0311.0321.0560.0310.031
WMC 3207.000.4130.0940.0870.7501.1370.8060.1360.719
WMC 3292.000.9840.0210.0200.0311.0321.0560.0310.031
WMC 3332.000.9840.0210.0200.0311.0321.0560.0310.031
WMC 3362.000.9680.0610.0530.0611.1020.6670.0750.060
WMC 3563.000.4920.1440.1290.5681.2221.1480.1960.474
WMC 3614.000.5240.1410.1270.6181.2270.8520.1860.554
WMC 4067.000.6350.1600.1400.5431.2360.8440.2090.500
WMC 4133.000.9680.0490.0470.0621.0821.1110.0680.061
WMC 4202.000.9680.0610.0530.0611.1020.6670.0750.060
WMC 4353.000.4920.0580.0550.5441.0950.8890.0810.439
WMC 46811.00.5240.1570.1420.6791.2370.9580.2160.653
WMC 50019.00.1430.2010.1780.9141.2761.2720.2800.908
WMC 5242.000.9840.0210.0200.0311.0321.0560.0310.031
WMC 53218.00.1900.1570.1420.8971.2290.9860.2190.889
WMC 5534.000.4760.1820.1650.5721.2871.2220.2460.480
WMC 5802.000.8730.0550.0510.2221.0890.8330.0770.197
WMC 6177.000.4920.1920.1730.6861.3001.1110.2560.648
WMC 6873.000.6670.3320.2920.4791.5061.6670.4320.410
WMC 7652.000.9680.1130.1020.0611.1911.2220.1440.060
WMC 8052.000.9680.0890.0780.0611.1350.8330.1160.060
WMC 8072.000.9840.0360.0340.0311.0580.6670.0520.031
WMC 8103.000.6510.1600.1380.5131.2171.0370.2140.458
WMC 1732.000.9370.0000.0000.1191.0001.0000.0000.112
WMS 510.00.3970.1560.1430.7661.2350.9560.2180.739
WMS 104.000.6030.1400.1310.5411.2151.3330.1990.470
WMS 243.000.6190.1850.1650.4831.3051.3890.2380.381
WMS 443.000.8250.1050.0970.2971.1730.8330.1390.268
WMS 466.000.6190.1590.1460.5401.2581.0740.2140.481
WMS 5219.00.1590.1910.1660.9201.2571.1110.2590.915
WMS 5510.00.2380.2740.2380.8521.4071.3110.3550.836
WMS 588.000.2860.1500.1340.7991.2081.0000.2100.772
WMS 639.000.3650.1070.0960.7641.1670.6850.1450.731
WMS 673.000.4920.1080.1000.5151.1671.1670.1490.398
WMS 722.000.9680.1130.1020.0611.1911.2220.1440.060
WMS7711.00.2860.2240.2020.8261.3351.2670.3070.805
WMS 1074.000.4600.0870.0810.6631.1390.8150.1210.604
WMS 1188.000.4920.1950.1760.6811.2981.1390.2670.641
WMS 12418.00.1590.1750.1520.9181.2311.1360.2410.912
WMS 14810.00.3020.1460.1340.8251.2170.9110.2060.806
WMS 1558.000.4130.0800.0740.7361.1200.5930.1150.700
WMS 18912.00.1590.2280.2040.8921.3241.3520.3170.883
WMS 1909.000.4600.0780.0730.7201.1200.6110.1130.688
WMS 2975.000.3650.1040.0970.7081.1640.7780.1450.656
WMS 40315.000.2380.1460.1290.8671.2080.9030.2010.854
WMS 49312.000.2700.1730.1540.8261.2590.9780.2330.805
WMS 5664.000.6510.2310.2080.5311.3971.4440.2940.491
Mean5.4420.6310.1240.1120.4601.1901.0490.1680.430
Na: Observed number of alleles, MAF: Major allele frequency, Exp-He: Expected heterozygosity, uHe: unbiased expected heterozygosity, GD: Gene diversity, Ne: Effective number of alleles, H: Nei’s expected heterozygosity, I: Shannon’s Information index, PIC: Polymorphism information content.
Table
Table 3. Results of AMOVA analysis for investigated Türkiye bread wheat genotypes.
Table 3. Results of AMOVA analysis for investigated Türkiye bread wheat genotypes.
Source of VariationdfSum of Squares (SS)Mean of Squares (MS)Variance Component% of Total Variance (%)
Among Population81054.528131.81613.02622
Within Population542546.10747.15047.15078
Total623600.635 60.176100
Table
Table 4. Membership coefficients of investigated Türkiye bread wheat genotypes in each estimated sub-population.
Table 4. Membership coefficients of investigated Türkiye bread wheat genotypes in each estimated sub-population.
CodeSubpopulation Subpopulation
IIIIIICode NumberIIIIII
G10.0110.0170.973G330.7200.2720.009
G20.0020.0040.994G340.5000.4970.004
G30.0050.0090.986G350.2940.6850.021
G40.0030.0020.995G360.1460.8300.024
G50.0100.0030.987G370.1370.8590.004
G60.0190.0030.978G380.1350.8570.008
G70.0510.0030.946G390.1300.8640.006
G80.1290.0060.865G400.0040.9940.002
G90.0370.0080.955G410.0360.9220.042
G100.1020.0030.896G420.0090.9770.014
G110.0800.0020.917G430.0060.9920.002
G120.4560.0020.542G440.1030.8870.010
G130.9880.0020.010G450.0050.9920.003
G140.9670.0040.029G460.0100.9820.008
G150.9780.0030.019G470.0040.9920.004
G160.9750.0030.022G480.0060.9860.008
G170.6760.0040.320G490.0040.9920.004
G180.9680.0040.028G500.0080.9870.005
G190.9300.0030.067G510.0080.9870.005
G200.9950.0030.003G520.0040.9940.002
G210.9950.0020.003G530.0020.9960.001
G220.9890.0070.004G540.0030.9940.002
G230.9920.0040.003G550.0030.9880.009
G240.9900.0060.003G560.0020.9950.003
G250.9790.0170.004G570.0020.9960.003
G260.9920.0040.004G580.0020.9960.002
G270.7940.2030.003G590.0030.9630.033
G280.9880.0080.003G600.0030.9930.004
G290.9580.0160.026G610.0100.9840.006
G300.9930.0050.002G620.0090.8590.132
G310.9450.0510.005G630.1070.8650.028
G320.6810.2850.034
Table
Table 5. Expected heterozygosity (He) and Fst values of 3 sub-populations.
Table 5. Expected heterozygosity (He) and Fst values of 3 sub-populations.
Subpopulation (K)Expected Heterozygosity (He)Fst
10.4520.253
20.4630.330
30.4440.244
Mean0.4530.276
Table
Table 6. Genetic differentiation coefficients for three estimated sub-populations.
Table 6. Genetic differentiation coefficients for three estimated sub-populations.
Sub-Populations (K)Sub-Population ASub-Population BSub-Population C
Sub-population A-0.4920.323
Sub-population B0.492-0.566
Sub-population C0.3230.566-
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Türkoğlu, A.; Haliloğlu, K.; Mohammadi, S.A.; Öztürk, A.; Bolouri, P.; Özkan, G.; Bocianowski, J.; Pour-Aboughadareh, A.; Jamshidi, B. Genetic Diversity and Population Structure in Türkiye Bread Wheat Genotypes Revealed by Simple Sequence Repeats (SSR) Markers. Genes 2023, 14, 1182. https://doi.org/10.3390/genes14061182

AMA Style

Türkoğlu A, Haliloğlu K, Mohammadi SA, Öztürk A, Bolouri P, Özkan G, Bocianowski J, Pour-Aboughadareh A, Jamshidi B. Genetic Diversity and Population Structure in Türkiye Bread Wheat Genotypes Revealed by Simple Sequence Repeats (SSR) Markers. Genes. 2023; 14(6):1182. https://doi.org/10.3390/genes14061182

Chicago/Turabian Style

Türkoğlu, Aras, Kamil Haliloğlu, Seyyed Abolgahasem Mohammadi, Ali Öztürk, Parisa Bolouri, Güller Özkan, Jan Bocianowski, Alireza Pour-Aboughadareh, and Bita Jamshidi. 2023. "Genetic Diversity and Population Structure in Türkiye Bread Wheat Genotypes Revealed by Simple Sequence Repeats (SSR) Markers" Genes 14, no. 6: 1182. https://doi.org/10.3390/genes14061182

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