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Article

Revealing Genetic Diversity and Population Structure in Türkiye’s Wheat Germplasm Using iPBS-Retrotransposon Markers

by
Fatih Demirel
1,*,
Bünyamin Yıldırım
2,
Barış Eren
1,
Serap Demirel
3,
Aras Türkoğlu
4,
Kamil Haliloğlu
5,
Kamila Nowosad
6,*,
Henryk Bujak
6,7 and
Jan Bocianowski
8
1
Department of Agricultural Biotechnology, Faculty of Agriculture, Igdır University, Igdir 76000, Türkiye
2
Department of Field Crops, Faculty of Agriculture, Igdır University, Igdir 76000, Türkiye
3
Department of Molecular Biology and Genetics, Faculty of Science, Van Yüzüncü Yıl University, Van 65080, Türkiye
4
Department of Field Crops, Faculty of Agriculture, Necmettin Erbakan University, Konya 42310, Türkiye
5
Department of Field Crops, Faculty of Agriculture, Ataturk University, Erzurum 25240, Türkiye
6
Department of Genetics, Plant Breeding and Seed Production, Wrocław University of Environmental and Life Sciences, Grunwaldzki 24A, 53-363 Wrocław, Poland
7
Research Center for Cultivar Testing, Słupia Wielka 34, 63-022 Słupia Wielka, Poland
8
Department of Mathematical and Statistical Methods, Poznań University of Life Sciences, Wojska Polskiego 28, 60-637 Poznań, Poland
*
Authors to whom correspondence should be addressed.
Agronomy 2024, 14(2), 300; https://doi.org/10.3390/agronomy14020300
Submission received: 5 December 2023 / Revised: 11 January 2024 / Accepted: 24 January 2024 / Published: 30 January 2024
(This article belongs to the Special Issue Plant Genetic Resources and Biotechnology)
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Versions Notes

Abstract

:
Investigating the genetic diversity and population structure of wheat germplasm is crucial for understanding the underlying variability essential for breeding programs and germplasm preservation. This research aims to contribute novel insights with respect to the genetic makeup and relationships among these wheat genotypes, shedding light on the diversity present within the Turkish wheat germplasm. In this study, iPBS-retrotransposon markers were employed to analyze 58 wheat genotypes, encompassing 54 landraces and 4 cultivars sourced from Türkiye. These markers serve as genetic indicators that can be used to evaluate genetic variation, build genealogical trees, and comprehend evolutionary connections. The PCR products were visualized on agarose gel, and bands were scored as present/absent. The ten iPBS primers collectively yielded an average of 16.3 alleles, generating a total of 163 polymorphic bands. The number of alleles produced by individual markers ranged from 4 (iPBS-2386) to 29 (iPBS-2219). The genetic parameters were calculated using the popgen and powermarker programs. The genetic relationships and population structures were assessed using the ntsys and structure programs. Polymorphism information content (PIC) per marker varied from 0.13 (iPBS-2390) to 0.29 (iPBS-2386), with an average value of 0.22. Shannon’s information index (I) was calculated as 1.48, while the number of effective alleles (Ne) and Nei’s genetic diversity (H) were determined to be 0.26 and 0.31, respectively. Genotype numbers 3 (Triticum dicoccum) and 10 (Triticum monococcum) exhibited the maximum genetic distance of 0.1292, signifying the highest genetic disparity. Population structure analysis revealed the segregation of genotypes into three distinct subpopulations. Notably, a substantial portion of genotypes clustered within populations correlated with the wheat species. This population structure result was consistent with the categorization of genotypes based on wheat species. The comprehensive assessment revealed noteworthy insights with respect to allele distribution, polymorphism content, and population differentiation, offering valuable implications for wheat breeding strategies and germplasm conservation efforts. In addition, the iPBS markers and wheat genotypes employed in this study hold significant potential for applications in wheat breeding research and germplasm preservation.

1. Introduction

Wheat stands prominently as a fundamental dietary staple in global human nutrition, recognized for its indispensability [1]. The success of this agricultural commodity hinges on its adaptability and potential for high yields. Notably, the presence of the gluten protein fraction assumes a pivotal role, conferring the viscoelastic properties essential for the processing of dough into diverse food products such as bread, pasta, and noodles, among others [2]. Beyond its culinary significance, wheat enriches the human diet by providing essential amino acids, minerals, vitamins, phytochemicals, and dietary fiber components. These nutritive elements are particularly abundant in whole-grain products [3].
The cultivated species of wheat, scientifically classified as Triticum L., are systematically grouped into three distinct subcategories based on their chromosomal composition: (i) diploid einkorn (Triticum monococcum ssp. monococcum) (2n = 14, AA), (ii) tetraploid emmer (Triticum dicoccum L.) and durum wheat (Triticum durum Desf.) (2n = 28, AABB), and (iii) hexaploid spelt (Triticum spelta L.) and bread wheat (Triticum aestivum L.) (2n = 42, AABBDD) [4].
The Fertile Crescent is widely acknowledged as the presumed center of origin and diversity for wheat [5]. In terms of wheat domestication, pivotal locations include the southeastern region of Türkiye and northern Syria [6]. Türkiye’s wheat diversity assumes a critical global role, providing valuable genetic resources for wheat enhancement. The emergence of durum wheat, characterized by easy husk separation, can be traced back to the eastern Mediterranean region [7]. Through the analysis of restriction fragment length polymorphism data (RFLP), it has been discerned that durum wheat has superseded its precursor, T. dicoccum, becoming the predominant cultivated variety of allotetraploid wheat [8]. Despite the broader cultivation of bread and durum wheats, the cultivation of einkorn and emmer wheat species persists, thanks to the efforts of smaller farmers [9].
The examination of genetic diversity in plants assumes a pivotal role in plant genetics, breeding, conservation, and evolution [10]. To effectively leverage existing gene resources in wheat breeding, a comprehensive understanding of these resources’ properties, coupled with targeted breeding experiments, is essential [11]. Wheat’s genetic diversity, akin to other crops, has diminished due to domestication and rigorous selection methods in contemporary plant breeding programs [12]. Consequently, there has been a reduction in on-farm genetic diversity and a decline in regionally well-adapted and genetically distinct landraces.
The genes that provide resistance to diseases, pests, and environmental pressures may be found in many genetic sources, even when these genes are not present in the crop types that are now in use [13]. These valuable genes may be present in wild species and traditional landraces [14,15]. The remaining portions of these gene pools are commonly referred to as genetic resources, as emphasized in the current literature [11]. Plant breeders rely heavily on these genetic resources to cultivate novel agricultural plant varieties. The essential process of germplasm characterization is integral to breeding endeavors, enabling breeders to identify unique genetic variants for use in marker-assisted breeding [16]. Molecular markers have become indispensable tools for unraveling the genetic diversity of wheat [12,17]. They have significantly transformed breeding research, streamlining the time required to complete breeding studies [18]. Notably, molecular markers remain unaffected by environmental factors, allowing for a more precise estimation of genetic variation at the DNA level [19].
Continuous progress in scientific research has led to the development of molecular markers with diverse qualities [20]. Various molecular marker techniques have been employed to analyze genetic diversity and associations among different Triticum species. These methods encompass amplified fragment length polymorphism (AFLP) [21], inter-simple-sequence repeat (ISSR) [22], simple-sequence repeat (SSR) [23], diversity array technology (DArT) [12], random amplified polymorphic DNA (RAPD) [24], start codon targeted (SCoT) markers [25], expressed sequence tag (EST) [26], single-nucleotide polymorphism (SNP) [27], and next-generation sequencing (NGS) [28]. Each of these molecular marker techniques offers distinct advantages and contributes to a comprehensive understanding of the genetic landscape of wheat. Within the array of molecular markers, retrotransposons stand out as genetic elements capable of prolific reproduction and mobility, comprising substantial portions of the genomes across various eukaryotic organisms [29]. Retrotransposons (RTNs) play a crucial role in fostering genetic diversity in plants. Notably, in numerous plant species characterized by expansive genomes, retrotransposons constitute over 50% of the nuclear DNA [30]. The concept of utilizing iPBS-retrotransposons as a comprehensive marker applicable to both animal and plant species was introduced by Kalendar et al. [31]. iPBS-retrotransposons have since been employed in molecular characterization, phylogenetic analysis, and evolutionary research across a variety of crop plants [15].
The iBPS-retrotransposon marker has been employed in wheat [18,29]. However, there has been a limited exploration such as with respect to the type of germplasm or the characterization of wheat populations using iBPS-retrotransposons. Consequently, the current study focuses on genetically characterizing and elucidating the population structure of Turkish wheat genotypes, encompassing T. durum, T. dicoccum, T. monococcum, and T. aestivum, with the primary objective of evaluating genetic diversity. Therefore, the purpose of this study was to characterize Türkiye wheat germplasm with different ploidy levels for the purpose of assessing genetic diversity and investigating the population structure.

2. Materials and Methods

2.1. Plant Materials

The study utilized a total of 58 genotypes, categorized as follows: 13 durum wheat genotypes (T. durum), 20 bread wheat genotypes (T. aestivum), 16 emmer wheat genotypes (T. dicoccum), 5 einkorn wheat genotypes (T. monococcum), and 4 cultivars (Ahmetağa, Aydın-93, Fırat-93, and Cemre) as plant material (Figure 1, Table 1, Supplementary Table S1). These wheat genotypes are now accessible at the department of agriculture at Iğdır University in Türkiye.

2.2. DNA Extraction and PCR Amplification of Wheat Genotypes

A total of 25 seeds of each wheat accession were germinated in trays in a greenhouse. To harvest DNA from each wheat plant, the young leaves of each plant were powdered thoroughly in liquid nitrogen. The DNA extraction process was evaluated on bulked samples consisting of 10 individuals chosen at random from each accession. By utilizing the DNA extraction method proposed by Zeinalzadehtabrizi et al. [32], gDNA was extracted from 58 different genotypes. A NanoDrop ND-1000 UV/Vi spectrophotometer (Thermo Fisher Scientific Company, Waltham, MA, USA) was utilized to ascertain the concentrations of DNA contained within the sample. The iPBS primers produced by Kalendar et al. [31] were used as markers in the molecular characterization. In this investigation, a total of ten iPBS markers were chosen based on their ability to create distinct and measurable bands for all genotypes. Polymerase chain reaction (PCR) was conducted in a thermal cycler device (SensoQuest Labcycler, Göttingen, Germany) with a total capacity of 10 μL of reaction mixture.
In the reaction mixture, components comprising 3 μL of DNA (about 20 ng μL−1), 0.3 μL of Taq DNA polymerase (5 U μL−1), 0.5 μL of dNTP (2 mM), 1 μL of primer (20 pmol), 1 μL of MgCl2 (2 mM), 4.2 μL of dH2O, and 1 μL of 10X PCR buffer were used. The thermal profile cycle of the polymerase chain reaction (PCR) included one cycle of pre-denaturation at 94 °C for thirty seconds, forty-two cycles of amplification (94 °C for twenty-five seconds, annealing temperature (Table 2) for forty-five seconds, and 72 °C for one minute), and one cycle of final extension at 72 °C for five minutes. The PCR products were resolved on a 3% agarose gel in 1 × TBE buffer at a voltage of 120 V for a duration of 4 h. Ultimately, bands on the gel were visualized using ultraviolet (UV) light and captured as photographs by an Imager Gel Doc XR  +  system (Bio-Rad, Hercules, CA, USA) (Figure 2). The GeneRuler 100 bp DNA Ladder manufactured by Thermo Scientific (Emeryville, CA, USA) was used as a molecular size marker.

2.3. iPBS Data Scoring and Analysis

In the scoring process, only robust and distinct bands were taken into consideration. The band pictures that were acquired via the use of iPBS-retrotransposons were scored as binary data, and the results were entered using Excel software. A value of 0 denotes the absence of a band, while a value of 1 represents the presence of a band. Scoring was performed with reference to a 100 bp+ DNA ladder, as illustrated in Figure 2. Using PopGene version 1.31 [33], the effective number of alleles (Ne) and Shannon’s information index (I) were evaluated for each iPBS marker. Nei genetic diversity (H) and polymorphism information content (PIC) were determined using PowerMarker V3.025 software [34]. NTSYS-PC software version 2.02 was used to compute the Dice similarity index, to generate a UPGMA (unweighted pair group method with arithmetic mean) dendrogram, and to create a two-dimensional graph with PCoA (Principal coordinate analysis) [35]. Furthermore, this software was used to perform the Mantel test [36], which calculates the correlation coefficient (r) between the cophenetic values and the Dice similarity index. Using the COPH module, the cophenetic value matrix was initially computed by using the tree matrix that was obtained from the iPBS analysis. The MXCOMP module was used to investigate the goodness-of-fit value (r) representing the relationship between the UPGMA dendrogram and the Dice similarity index matrix [37]. To undertake an analysis of the population structure of wheat varieties, STRUCTURE version 2.3.4 was utilized [38]. The appropriate number of subpopulations (delta K value) was determined using the approach described by Evanno et al. [39] using the STRUCTURE HARVESTER program [40]. The STRUCTURE program was used to determine the expected heterozygosity (He) and fixation index (FST) values that reflect populations [37].

3. Results

3.1. Polymorphism Disclosed by iPBS Primer

Distinct and scorable bands were successfully obtained from each primer utilized in the investigation, as outlined in Table 3. In total, 168 bands were generated across the 20 employed primers. Among these, 163 bands were both visible and quantifiable, representing polymorphic variations. The allelic diversity observed among the primers ranged from 4 alleles (iPBS 2386) to 29 alleles (iPBS 2219), with an average of 16.3 alleles. iPBS marker analysis unveiled a spectrum of polymorphic information content (PIC) values, with iPBS 2390 exhibiting the lowest value of 0.13 and iPBS 2386 displaying the highest value, at 0.29. The mean PIC value across all markers was computed as 0.22. The polymorphism rate displayed variability, ranging from 80% (iPBS 2386) to 100% (iPBS 2219, iPBS 2278, iPBS 2377, iPBS 2378, and iPBS 2383). Shannon’s information index (I) revealed diverse values, ranging from the lowest value of 0.11 for iPBS 2390 to the highest value of 0.48 for iPBS 2386. The mean value of Shannon’s information index across all observations was calculated to be 0.31. The effective number of alleles (Ne) ranged from 1.35 (iPBS 2390) to 1.74 (iPBS 2386), with a mean value of 1.48. These results collectively provide a comprehensive overview of the genetic diversity and informative parameters derived from the iPBS marker analysis conducted in the investigated wheat germplasm.

3.2. Genetic Distance and Cluster Analysis for Wheat Genotypes

The Dice method was employed to assess the similarity between the 58 genotypes, resulting in the generation of similarity coefficient values for each genotype (Supplementary Table S2). The mean Dice similarity coefficient was calculated to be 0.5948. Upon scrutinizing the genotypes using the Dice similarity coefficient, it was observed that genotypes 3 and 10 exhibited the lowest similarity, registering a coefficient value of 0.1299. In contrast, genotypes 17 and 49 were identified as the most similar, with a coefficient value of 0.8829.
To further analyze the relationships among the 58 wheat genotypes, clustering analysis was conducted using the UPGMA technique, with the Dice similarity index serving as the basis for the study. The resulting dendrogram was utilized to construct the similarity matrix. Subsequently, a Mantel test analysis was performed using the Dice similarity matrix, revealing a correlation coefficient value of r = 0.96011 for the 58 wheat genotypes. This investigation provides valuable insights into the genetic relationships and clustering patterns among the examined wheat genotypes. The dendrogram visually represents variations in similarity levels, spanning from 0.29 to 0.88 (Figure 3). Initially, the dendrogram partitioned the genotypes into two primary clusters, designated as groups S1 and S2. Within the overarching group denoted as S2, additional subsidiary groups were identified. Specifically, subgroups S3 and S4 emerged within group S2. Subgroup S4 further gave rise to groups S5 and S6, with subgroup S6 leading to subgroups S7 and S8. In a similar manner, subgroup S8 contributed to the formation of subgroups S9 and S10, and subgroup S10 resulted in subgroups S11 and S12. Notably, groups S11a and S11b were delineated beneath subgroup S11, while groups S13 and S14 formed under subgroup S12. The dendrogram analysis provided insights into the degrees of separation among major groups and subgroups, denoted as S1, S2, S3, S4, S6, S7, S8, S10, S11, S11a, S11b, S12, and S13, with separation degrees measuring approximately 0.750, 0.430, 0.548, 0.495, 0.541, 0.600, 0.582, 0.640, 0.700, 0.773, 0.710, 0.660, and 0.710, respectively. It is noteworthy that no degree of separation was observed in subgroups S5, S9, and S14 due to the clustering of individual genotypes within these subgroups. This dendrogram analysis effectively portrays the hierarchical relationships and clustering patterns among the wheat genotypes.
Upon scrutinizing the major groups and subgroups, a distinct cluster was observed within main group S1. This cluster comprises genotypes 5, 10, 27, and 48 from T. monococcum, along with genotype number 45 from T. aestivum. Subgroup S3 exclusively includes genotypes 2, 3, and 28 from T. dicoccum, forming distinct clusters. Additionally, genotype number 58, specifically belonging to T. aestivum, exhibited a unique clustering pattern within subgroup S5. In subgroup S7, a cluster was identified where genotype 34 (T. monococcum) and genotype 54 (T. dicoccum) are grouped together. Furthermore, genotype 57 (T. durum) displayed a distinctive clustering pattern within subgroup S9. The study results also highlight that T. dicoccum formed a distinct subgroup, namely S11b, encompassing genotypes 7, 11, 13, 14, 16, 20, 21, 23, 25, 33, 44, and 46. Within subgroup S11a, it was determined that only T. durum exhibited clustering, with genotypes 29, 50, 52, and 56 forming a distinct subgroup. Subgroup S13, representing the T. aestivum species, included the following accessions: 4, 6, 8, 9, 12, 18, 19, 22, 24, 30, 31, 36, 37, 38, 39, 40, 41, 42, 51, and 55. Clustering analysis further revealed that genotypes 15, 17, 26, 32, 35, 43, 47, 49, and 53 originating from T. durum tended to group together. Finally, within subgroup S14, it was identified that only genotype number 1 (T. durum) exhibited a clustering pattern. These clustering patterns provide valuable insights into the genetic relationships and subgroupings among the examined wheat genotypes.
The similarity matrix was employed to construct a two-dimensional graph, representing the principal coordinate analysis (PCoA). The cumulative value of the first two principal coordinates accounted for 68.21% of the total variation (Figure 4). The groups that are displayed in Figure 4 and referred to as S1, S3, S5, S7, S9, S11a, S11b, S13, and S14 correspond to those presented in the separated branches in Figure 3. The results from the PCoA analysis revealed that the observed clustering patterns were consistent with the outcomes obtained from the cluster analysis (Figure 3 and Figure 4). This alignment reinforces the robustness and reliability of the observed clustering patterns, providing a coherent representation of the genetic relationships among the wheat genotypes.

3.3. Population Genetic Structure Analysis for Wheat Genotypes

The population structure analysis for the 58 wheat genotypes involved the utilization of a model-based technique within the STRUCTURE program. To partition each entry into respective subgroups, the Delta K values were calculated, and the STRUCTURE HARVESTER online tool was employed for this purpose (refer to Figure 5). The maximum value for Delta K was determined to be 112.629. Based on the Delta K findings, it was ascertained that the 58 wheat genotypes can be effectively classified into three distinct populations. This result provides valuable information about the underlying genetic structure within the set of wheat genotypes, facilitating a clearer understanding of the diversity and relationships among them. The STRUCTURE program’s model-based approach is instrumental in delineating and characterizing subpopulations, contributing insights into the population genetics of the studied wheat germplasm.
The population structure analysis results (Figure 6) highlight the categorization of the 58 examined genotypes into three distinct subgroups denoted as P1, P2, and P3. Genotypes with a membership coefficient equal to or greater than 0.80 were considered to possess a high level of genetic purity, while five genotypes displayed hybrid characteristics. The average FST values for each subgroup were determined, quantifying population differentiation through allele correlations. Specifically, the FST values were calculated as 0.5353 for P1 (red), 0.4416 for P2 (green), and 0.6934 for P3 (blue). Additionally, the analysis revealed that the P2 population exhibited the highest expected heterozygosity value of 0.1944, while the P1 population had the lowest expected heterozygosity value of 0.1501 (Table 4). These findings provide insights into the genetic structure and differentiation among the identified subgroups, offering a comprehensive understanding of the diversity and purity levels within the studied wheat genotypes. The utilization of membership coefficients and genetic parameters enhances the characterization of distinct populations, contributing valuable information to wheat breeding and germplasm preservation efforts.
The P1 population consists only of genotypes belonging to the bread wheat and durum wheat genotypes. The P2 population consists of several genotypes of T. dicoccum (genotypes 7, 11, 13, 14, 16, 20, 21, 23, 25, 28, 33, and 44) and T. durum (genotypes 29, 46, 50, 52, 56, and 57), together with genotype 34 (T. monococcum). The population denoted as P3 exhibited the presence of genotypes of T. monococcum, specifically designated as 5, 10, 27, and 48. Additionally, a genotype of T. aestivum, identified as 45, was also observed within this population (Table 5).

4. Discussion

The assessment of genetic diversity and population structure in plants holds significant importance in terms of advancing plant breeding initiatives [17]. Evaluating indigenous species in regions of domestication and exploring species diversity can provide valuable insights into evolutionary patterns and the impacts of socioeconomic and geoecological variables on genetic structure. Numerous studies have been conducted, comprehensively analyzing of wheat germplasm and its wild counterparts, and employing diverse molecular markers [6,12,17,41]. Within the realm of retrotransposon-based markers, researchers have successfully utilized the iPBS-retrotransposon marker for molecular characterization of wheat [11]. iPBS markers offer several advantages, particularly in targeting retrotransposons, which are commonly found in plant genomes [18]. The versatility of iPBS markers allows for their application across various organisms following successful implementation in one organism [31]. iPBS molecular markers exhibit high productivity due to their long primer size and strong binding affinity [42]. In this study, an average of 16.3 polymorphic bands were obtained using 10 iPBS markers. On the other hand, the mean polymorphic bands that were reported in this study were higher than those that were reported by Nazarzadeh et al. [22] using RAPD and ISSR markers, Kumar et al. [43] using ISSR markers, Alshehri et al. [44] using SCoT and ISSR primers, Çifçi and Yağdi [45] using RAPD markers, Nadeem [11] using iPBS marker, and Abbasi Holasou et al. [41] using IRAP and REMAP markers.
The polymorphic information content (PIC) value is a metric employed to gauge the effectiveness of polymorphic loci in exploring genetic diversity using markers [46]. In this investigation, the mean PIC value was determined to be 0.22. While this value is comparatively lower than those reported in other studies utilizing distinct marker systems [11,43,44,45,47], the mean PIC value obtained in our research exceeds the value published by Khaled et al. [48]. The observed variance in PIC values could potentially be attributed to the use of different marker systems or the presence of a variable number of genotypes and markers in the study [49]. The choice of markers, along with the specific genetic makeup of the studied wheat germplasm, can influence PIC values. Nonetheless, despite variations, the PIC value remains a valuable indicator of the informativeness of markers in capturing genetic diversity in the context of this research.
This research revealed that the average Nei’s gene diversity (H) value was 0.26. Notably, the H value obtained in this study surpasses those reported in earlier wheat research utilizing iPBS markers for characterization [11]. Additionally, Shannon information index (I) value (0.31) and the effective number of alleles (Ne, 1.48) observed in this research exceed the values reported in previous studies of various plant species employing iPBS markers [11,37,50]. The heightened values observed for several diversity indices in this research may be attributed to variations in germplasm and the characteristics of the employed molecular marker [11]. The iPBS-retrotransposons marker system, known for its exceptional reproducibility and worldwide applicability, has been established in various investigations [18,19]. Consequently, this marker system can be prioritized for the molecular characterization of the wheat germplasm over other dominant marker systems [11,18]. The findings underscore the effectiveness of iPBS markers in capturing genetic diversity and providing valuable insights into the wheat germplasm under investigation.
After conducting the Mantel test, the correlation coefficient was determined to be r = 0.96011 based on the results. A correlation value of 0.9 or above indicates a strong correlation between the dendrogram and the similarity indexes, suggesting that the dendrogram accurately reflects the similarity index [51]. In a study conducted by Nasri et al. [52] on genetic diversity in bread wheat genotypes using retrotransposon-based marker systems, the cophenetic correlation coefficient was computed through the Mantel test to assess the compatibility between similarity matrices and dendrogram-derived matrices. The correlation between the cophenetic matrices derived from the IRAP and REMAP markers was found to be extremely weak (r = 0.13). Similarly, Saeidi et al. [53] examined genetic diversity and the retroelement insertion polymorphism in Aegilops tauschii genotypes from Iran. They computed the matrix correlation coefficient (r) to compare matrices, obtaining a result of r = 0.9297. In a study on T. dicoccoides genotypes, Beharav et al. [54] used the Mantel test to compare data from the RAPD marker system and three distinct genetic matrices (SSM = simple matching similarity; SD = Dice similarity; SJ = Jaccard similarity). The Mantel correlation coefficient (r) for simple matching was 0.92, whereas for Dice and Jaccard similarities, it was 0.998 and 0.593, respectively. In the present investigation, the substantial Mantel correlation coefficient indicates the high reliability of our similarity matrix, providing strong evidence for the accuracy of the dendrogram in capturing the underlying genetic relationships among the studies wheat genotypes.
Assessing genetic diversity is a pivotal aspect of breeding research aimed at enhancing various traits in crops, including quality and yield [49]. The results of the similarity analysis and cluster analysis conducted in this investigation reveal a substantial amount of genetic diversity among all the studied genotypes. The model-based structure algorithm effectively classified the 58 wheat genotypes into three groups, primarily based on their species. However, there was not an obvious genetic discrimination of the populations according to species on UPGMA. This could be due to either an insufficient number of selected iPBS markers or an uneven distribution of the markers among different genomes of the genotypes used in this study.
It is noteworthy that only a fraction of the T. monococcum genome is present in hexaploid wheat. Therefore, utilizing the genetic diversity inherent in the T. monococcum genome can be instrumental in uncovering novel and additional traits when developing tetraploid and hexaploid cultivars [17]. A comprehensive understanding of the extent of diversity within a collection of wheat genetic resources is crucial for making significant advancements in wheat improvement. This knowledge serves as a foundation for targeted breeding efforts aimed at enhancing specific traits and overall crop performance. The conventional breeding approach relies on the identification of naturally occurring or artificially produced alleles that provide resistance to plants. These alleles are then transferred to superior genotypes using various breeding procedures. The efficacy of the conventional breeding method relies on the presence of functional diversity. Genetic diversity is constrained by the genetic bottleneck that arises during farming [55]. The introduction of diversity by natural or random stimulation is a limiting element in the breeding process and leads to unexpected outcomes in plant breeding [56]. Researchers and breeders used to look to crops’ wild relatives and other plant genetic resources as a “last-option emergency solution” when contemporary elite germplasm failed to provide the desired results. Introducing desired characteristics into the cultivated background from unadopted germplasm, such as crop wild relatives or landrace materials, requires more time and money than doing so from elite lines. This is because there is low linkage drag. The advancement of technology has led to the acquisition of genome and transcriptome sequences for several plant species, marking the beginning of a new age in plant breeding [57].

5. Conclusions

Further studies on molecular diversity, agromorphological characterization, and genotype identification are imperative to safeguard the wheat germplasm and improve crops. In this study, we investigated the relatedness and diversity of wheat genotypes using iPBS-based markers to assess genetic variation across various ploidy levels of wheat. In this work, the efficiency of iPBS molecular markers in distinguishing polyploidy in wheat species was demonstrated. Despite the observed low polymorphic information content (PIC) values in the employed iPBS markers, principal coordinate analysis (PCoA), in addition to clustering and population structure analyses, revealed a significant level of genetic diversity among the wheat genotypes. Recent research on wheat population structure and genetic diversity favors molecular markers with high levels of informativeness, such as iPBS. Molecular analyses identified genotype 3 and genotype 10 as genetically distinct among the wheat genotypes, suggesting their potential as parents in future breeding experiments. Preserving these valuable genotypes by incorporating them into germplasms is crucial. This study indicates that landrace wheats (Triticum ssp.), known for their extensive genetic variation, hold promise as essential resources for germplasm and as valuable materials for future investigations, such as single-nucleotide polymorphism (SNP) and genome-wide association studies (GWAS).

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy14020300/s1.

Author Contributions

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

Funding

This research received no external funding.

Data Availability Statement

All data supporting the conclusions of this article are included in this article.

Acknowledgments

This work is a molecular characterization part of Fatih Demirel’s Ph.D. research dissertation and received financial assistance from the Scientific Research Projects Coordination Unit of Igdir University under project number “2017-FBE-D01”.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Geographical images of the locations where the wheat genotypes used in the study were collected.
Figure 1. Geographical images of the locations where the wheat genotypes used in the study were collected.
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Figure 2. PCR amplification profile of wheat genotypes captured using the iPBS-2219 marker. M, 100-base-pair DNA ladder.
Figure 2. PCR amplification profile of wheat genotypes captured using the iPBS-2219 marker. M, 100-base-pair DNA ladder.
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Figure 3. UPGMA dendrogram created using the Dice similarity index for 58 wheat genotypes.
Figure 3. UPGMA dendrogram created using the Dice similarity index for 58 wheat genotypes.
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Figure 4. Two-dimensional PCoA analysis of 58 wheat accessions.
Figure 4. Two-dimensional PCoA analysis of 58 wheat accessions.
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Figure 5. Line graphs from the mixture model of Ln P (D) and ∆K for the wheat population.
Figure 5. Line graphs from the mixture model of Ln P (D) and ∆K for the wheat population.
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Figure 6. Result of structure analysis of the genetic population of 58 wheat genotypes (K = 3).
Figure 6. Result of structure analysis of the genetic population of 58 wheat genotypes (K = 3).
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Table 1. Details of the wheat germplasm collection.
Table 1. Details of the wheat germplasm collection.
No.ProvinceTownSpeciesNo.ProvinceTownSpecies
1DiyarbakırCenterT. durum30KayseriEpçeT. aestivum
2KayseriEpçeT. dicoccum31NiğdeCenterT. aestivum
3KarsKuyucukT. dicoccum32KarsGeçitT. durum
4KahramanmaraşElbistanT. aestivum33KarsKüçükçatmaT. dicoccum
5KastamonuİhsangaziT. monococcum34KastamonuİhsangaziT. monococcum
6KonyaCenterT. aestivum35KarsGeçitT. durum
7KastamonuİhsangaziT. dicoccum36MardinCenterT. aestivum
8AksarayCenterT. aestivum37YozgatCenterT. aestivum
9VanGedelovaT. aestivum38KayseriYemlihaT. aestivum
10KastamonuİhsangaziT. monococcum39SivasDivriğiT. aestivum
11KayseriYeniköyT. dicoccum40KonyaMerkezT. aestivum
12KayseriPınarbaşıT. aestivum41AdıyamanMerkezT. aestivum
13KastamonuİhsangaziT. dicoccum42SivasMerkezT. aestivum
14KayseriHoşçaT. dicoccum43SivasGemerekT. durum
15MersinSilifkeT. durum44KastamonuİhsangaziT. dicoccum
16KayseriDeveliT. dicoccum45SivasGürünT. aestivum
17IğdırCenterT. durum46KarsGüvercinT. dicoccum
18KarsGeçitT. aestivum47MersinMerkezT. durum
19VanCenterT. aestivum48KastamonuİhsangaziT. monococcum
20KastamonuİhsangaziT. dicoccum49IğdırCenterT. durum
21KarsCenterT. dicoccum50ŞanlıurfaCenterT. durum
22ErzincanCenterT. aestivum51VanGedelovaT. aestivum
23KarsBüyükçatmaT. dicoccum52MersinÇarkçılıT. durum
24KayseriGümüşörenT. aestivum53KonyaMerkezT. durum
25KarsCenterT. dicoccum54KarsDuraklıT. dicoccum
26KastamonuCenterT. durum55AhmetağaBahri Dagdas IARI 1T. aestivum
27KastamonuİhsangaziT. monococcum56Aydın-93GAP IARTC 2T. durum
28KarsBüyükçatmaT. dicoccum57Fırat-93GAP IARTCT. durum
29ÇankırıCenterT. durum58CemreGAP IARTCT. aestivum
1 IARI, International Agricultural Research Institute; 2 IARTC, International Agricultural Research and Training Center.
Table 2. Sequences and annealing temperatures of 10 iPBS-retrotransposon primers used to study genetic diversity in 58 wheat genotypes.
Table 2. Sequences and annealing temperatures of 10 iPBS-retrotransposon primers used to study genetic diversity in 58 wheat genotypes.
NumberMarker NamePrimer Sequences (5′ → 3′)Annealing Temperature (°C)
1IPBS-2219GAACTTATGCCGATACCA57
2IPBS-2270ACCTGGCGTGCCA60
3IPBS-2271GGCTCGGATGCCA57.5
4IPBS-2278GCTCATGATACCA44
5IPBS-2375TCGCATCAACCA44
6IPBS-2377ACGAAGGGACCA44
7IPBS-2378GGTCCTCATCCA44
8IPBS-2383GCATGGCCTCCA48
9IPBS-2386CTGATCAACCCA48
10IPBS-2390GCAACAACCCCA44
Table 3. Genetic parameters of iPBS markers used for 58 genotypes.
Table 3. Genetic parameters of iPBS markers used for 58 genotypes.
Marker NameTNB 1NPBPR (%)HPICINe
IPBS-221929291000.230.200.291.39
IPBS-2270191894.70.280.230.311.61
IPBS-2271212095.20.250.210.381.40
IPBS-227818181000.300.240.321.54
IPBS-2375109900.230.190.241.38
IPBS-237719191000.310.250.421.52
IPBS-237820201000.240.200.181.41
IPBS-238316161000.280.230.341.46
IPBS-238654800.360.290.481.74
IPBS-2390111090.90.150.130.111.35
Total168163
Mean16.816.395.080.260.220.311.48
1 TNB: total number of bands; NPB: number of polymorphic bands; PR: polymorphism rate; H: genetic diversity of Nei; PIC: polymorphic information content; I: Shannon’s information index; Ne: effective number of alleles.
Table 4. Expected heterozygosity and FST (fixation index) values of subpopulations according to the results of structure analysis.
Table 4. Expected heterozygosity and FST (fixation index) values of subpopulations according to the results of structure analysis.
PopulationExpected Heterozygosity (He)FST
P10.15010.5353
P20.19440.4416
P30.16390.6934
Mean0.16950.5567
Table 5. Membership coefficients dividing 58 wheat genotypes into subpopulations according to the results of structure analysis.
Table 5. Membership coefficients dividing 58 wheat genotypes into subpopulations according to the results of structure analysis.
Genotype
Number		P1P2P3Genotype
Number		P1P2P3
10.4860.4280.086300.9960.0040.001
20.0030.7460.251310.9990.0000.000
30.0010.7820.217320.9690.0280.003
40.9970.0020.000330.0050.9950.000
50.0010.0010.998340.0930.8480.059
60.9970.0010.002350.9970.0030.001
70.0040.9960.001360.9970.0020.001
80.9530.0460.001370.9640.0330.003
90.9960.0020.001380.9980.0020.000
100.0010.0000.999390.9780.0210.001
110.0020.9660.032400.8630.1020.035
120.9940.0050.000410.9800.0180.002
130.0020.9970.001420.9990.0010.000
140.0040.9950.001430.9980.0010.002
150.9980.0010.000440.0010.9990.001
160.0030.9960.000450.0010.0000.998
170.9990.0010.001460.0020.9970.001
180.9950.0010.003470.9680.0320.001
190.9980.0010.001480.0010.0000.999
200.0160.9830.001490.9900.0090.000
210.0030.9960.001500.0050.9940.000
220.9850.0140.001510.9920.0020.006
230.0080.9910.001520.0030.9270.071
240.9970.0030.001530.9200.0020.079
250.0100.9900.000540.0370.6760.287
260.9910.0040.005550.9490.0480.003
270.0010.0010.998560.0170.8840.099
280.0030.9240.073570.0370.8450.119
290.0090.9900.001580.4960.1590.345
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Demirel, F.; Yıldırım, B.; Eren, B.; Demirel, S.; Türkoğlu, A.; Haliloğlu, K.; Nowosad, K.; Bujak, H.; Bocianowski, J. Revealing Genetic Diversity and Population Structure in Türkiye’s Wheat Germplasm Using iPBS-Retrotransposon Markers. Agronomy 2024, 14, 300. https://doi.org/10.3390/agronomy14020300

AMA Style

Demirel F, Yıldırım B, Eren B, Demirel S, Türkoğlu A, Haliloğlu K, Nowosad K, Bujak H, Bocianowski J. Revealing Genetic Diversity and Population Structure in Türkiye’s Wheat Germplasm Using iPBS-Retrotransposon Markers. Agronomy. 2024; 14(2):300. https://doi.org/10.3390/agronomy14020300

Chicago/Turabian Style

Demirel, Fatih, Bünyamin Yıldırım, Barış Eren, Serap Demirel, Aras Türkoğlu, Kamil Haliloğlu, Kamila Nowosad, Henryk Bujak, and Jan Bocianowski. 2024. "Revealing Genetic Diversity and Population Structure in Türkiye’s Wheat Germplasm Using iPBS-Retrotransposon Markers" Agronomy 14, no. 2: 300. https://doi.org/10.3390/agronomy14020300

APA Style

Demirel, F., Yıldırım, B., Eren, B., Demirel, S., Türkoğlu, A., Haliloğlu, K., Nowosad, K., Bujak, H., & Bocianowski, J. (2024). Revealing Genetic Diversity and Population Structure in Türkiye’s Wheat Germplasm Using iPBS-Retrotransposon Markers. Agronomy, 14(2), 300. https://doi.org/10.3390/agronomy14020300

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