Genetic Diversity Assessment of Promising Walnut (Juglans regia L.) Genotypes Using RAPD, ISSR, and iPBS Markers

In the current study, we have identified twenty promising walnut genotypes with superior fruit characteristics over two consecutive growing seasons, according to UPOV criteria. These genotypes, which naturally grow on their roots, were collected from seven different regions (city center, Eyyubiye, Karaköprü, Haliliye, Bozova, and Harran districts) in Şanlıurfa. The study comprised 5 genotypes from Karaköprü, 4 from city center, 3 each from Haliliye and Viranşehir, 2 each from Eyyübiye and Bozova and one genotype from Harran. The map illustrating the collection site is represented in Fig. 1. The geographic coordiates for each genotype are provided in Tab. 1.

Fig. 1:

The map of Şanlıurfa province illustrates the locations of collection sites. In the bottom right corner, a map of Türkiye shows the location of Şanlıurfa province.

First, we carefully collected and labeled leaf samples and transported them to the laboratory in sterile 50 ml tubes stored in an icebox to maintain their integrity. Upon arrival, one leaf from each sample was promptly frozen with liquid nitrogen and then homogenized using a mortar and pestle under liquid nitrogen conditions to ensure DNA isolation. Around 100 mg of the homogenized tissue were transferred into 1.5 ml microcentrifuge tubes and treated with preheated (65°C) extraction and lysis buffer. This was followed by two rounds of chloroform: isoamyl alcohol mix (24:1) and a precipitation step using 100% isopropanol. The resulting DNA was quantified using a DS-11 FX+ spectrophotometer (Denovix, USA) and diluted to a concentration of 10 ng/μl with sterile ultra-pure water. The template DNA samples were then stored at −20 ℃ until further use.

The study utilized three iPBS markers as described by Kalendar et al. (2010), in addition to two ISSR (Godwin et al., 1997) and three RAPD (Lynch and Milligan, 1994) markers (Tab. 1), to produce distinct and well-defined fragments for the walnut genotypes collected from Şanlıurfa Province. The RAPD analysis used three primers: OPF10 with the sequence GGAAGCTTGG, OPA18 (AGGTGACCGT), and OPAZ04 (CCAGCCTCAG). For the ISSR markers, two primers were employed: HR08 (GA)8YC, represented by the sequence GAGAGAGAGAGAGAGAYC, and SDo7 (CT)8RG, with the sequence CTCTCTCTCTCTCTCTRG. Lastly, the iPBS analysis included four primers: 2376 (TAGATGGCACCA), 2238 (ACCTAGCTCATGATGCCA), 2394 (GAGCCTAGGCCA), and 2415 (CATCGTAGGTGGGCGCCA). These primers were selected for their suitability in amplifying polymorphic regions to enhance the understanding of genetic diversity within the studied population.

PCR amplification with the selected primers was conducted in a 25 μL reaction mixture comprising 1× Dream Taq Buffer, 0.2 mM dNTPs, 1 μM primer concentration, 1.2 units of Dream Taq DNA polymerase (Thermo Fischer Scientific, USA), 0.04 units of Pfu DNA polymerase (Thermo Fischer Scientific, USA), and 20 ng template DNA. The PCR reactions were performed using a T100 thermocycler under optimized conditions: for iPBS markers, 35 cycles of 30 s at 94°C, 30 s at 50–62°C (depending on primers), and 2 min at 72°C; for ISSR markers, 60 s at 94°C, 60 s at 52–54°C (depending on primers), and 90 s at 72°C; for RAPD markers, 60 s at 94°C, 30 s at 36°C, and 90 s at 72°C. Each PCR reaction included an initial denaturation step of 3 min at 94°C and a final extension step of 10 min at 72°C. The amplified fragments were separated on a 1.6% (w/v) agarose gel in 1x TAE buffer for over 2 hours and stained with ethidium bromide. Visualization was achieved using a UV transilluminator (G: BOX F3, Syngene, UK). All PCR amplifications were repeated at least twice, and only reproducible bands were considered for evaluation. The iPBS analyses generated a binary data matrix scoring fragments as present (1) or absent (0) at specific positions. The effectiveness of each marker system in assessing genetic variation, as well as the determination of resolving power (RP) and polymorphic information content (PIC) values, followed methodologies proposed by Prevost and Wilkinson (1999) and Roldàn-Ruiz et al. (2000), respectively.

The primers’ sequences and codes, along with their characteristics such as GC content (%), total bands (TB), polymorphic bands (PB), percentage of polymorphic bands (PPB %), polymorphism information content (PIC), and resolving power (RP), were documented. Mantel tests were conducted to evaluate correlations between different sets of molecular markers using GenALEX ver. 6.5 (Peakall and Smouse, 2012). Data matrices generated from the marker systems were merged and transformed into genetic similarity matrices utilizing Jaccard’s similarity coefficient. The Numerical Taxonomy System (NTSYS-pc version 2.10e program, Rohlf, 2000) was employed to conduct an unweighted pair group method using arithmetic average (UPGMA) clustering analysis. Population structure analysis was performed using a Bayesian model-based algorithm on the binary matrix of individuals, implemented in the software package Structure v.2.3.4 (Pritchard et al., 2000).

The RAPD primers consistently produced polymorphic bands yielding a total of 23 amplified polymorphic bands across the genotypes. The number of polymorphic bands ranged from 6 (OPAZ04 primer) to 10 (OPA18 primer) bands, averaging 7.77 bands per primer pair with a polymorphism rate of 75.53%. The PIC values varied from 0.24 (OPF10 primer) to 0.40 (OPAZ04 primer), averaging 0.31 per primer. The RP ranged from 3.50 (OPF10 primer) to 6.70 (OPA18 primer), with an average of 4.93 (Tab. 2).

The ISSR primers amplified a total of 22 fragments, all of which were polymorphic (75.65%). Fragment amplification varied from 6 (HR08 (GA)8YC primer) to 11 (SDo7 (CT)8RG primer), averaging 8.50 fragments per primer. PIC values ranged from 0.22 (HR08 (GA)8YC primer) to 0.27 (SDo7 (CT)8RG primer), averaging 0.25 per primer. The RP values ranged from 3.00 (HR08 primer) to 4.80 (SDo7 primer), with an average of 3.90 (Tab. 2).

The iPBS primers generated a total of 65 bands, with 72.39% of them being polymorphic. Fragment amplification ranged from 10 (2394 primer) to 13 (2376 primer), averaging 11.75 fragments per primer. PIC values ranged from 0.21 (2238 and 2394 primers) to 0.22 (2376 and 2415 primers), averaging 0.22 per primer. The RP values ranged from 4.00 (2394 primer) to 5.30 (2376 primer), with an average of 4.80 (Tab. 2).

Diversity parameters obtained by different molecular markers utilized on walnuts indicate that the iPBS marker system has a mean number of different alleles (Na) of 1.723, a number of effective alleles (Ne) of 1.256, Shannon’s information index (I) of 0.275, expected heterozygosity (He) of 0.170, and unbiased expected heterozygosity (uHe) of 0.174. The ISSR marker system shows a higher genetic diversity with a mean Na of 1.773, Ne of 1.460, I of 0.392, He of 0.263, and uHe of 0.270. The RAPD marker system presents a mean Na of 1.742, Ne of 1.440, I of 0.391, He of 0.261, and uHe of 0.268. The combined marker system, integrating data from all marker types, provides a comprehensive view with a mean Na of 1.737, Ne of 1.342, I of 0.327, He of 0.211, and uHe of 0.217 (Tab. 3). These findings highlight that the ISSR marker system exhibits the highest genetic diversity among the walnut genotypes, whereas the iPBS marker system shows the lowest. The combined analysis yields intermediate values, reflecting an integrated measure of genetic diversity across all marker systems.

The correlation analysis of the three marker types and combined markers linked to the Mantel test showed a significant correlation among the markers. The results are submitted in Tab. 4. The lowest correlation was monitored between the RAPD and iPBS markers (r=0.006), while the highest correlation was determined between the iPBS markers and the combination of the three-marker analysis (r=0.786). Mantel test results displayed a significant correlation with the r-value larger than 0.5 among the three marker types, the combined data with RAPD (0.541) and iPBS markers (0.786), whereas the correlations were observed all less than 0.5 between other molecular markers. These differences are due to the development characteristics of the molecular markers. Therefore, the use of combined molecular markers will make significantly contributions to the definitions of diversity and relationships of walnut genetic resources.

The clustering analysis of walnut genotypes was performed using three different marker systems: RAPD, ISSR, and iPBS, as well as a combined analysis of all markers. The results were presented in the form of dendrograms, which showed the genetic relationships and clustering patterns among the genotypes (Fig. 2). In the RAPD dendrogram, the genotypes were clustered into three main groups. The red cluster contained genotypes 8, 26, 27, 29, 36 48, and 61 indicating a close genetic relationship among these genotypes. The green cluster included genotypes 15, 30, 31, 76, 89, 90, and 91 showing another distinct genetic grouping. The blue cluster comprised genotypes 18, 45, 73, 75, 103, and 118 which were more genetically distant from the other two clusters (Fig. 2).

Fig. 2:

Dendrogram of 20 promising walnut genotypes using UPGMA method based on RAPD, ISSR, iPBS, and combined data.

The ISSR dendrogram also revealed three main clusters. The red cluster included genotypes 29, 36, 48, 61, 76, and 89. The green cluster contained genotypes 18, 30, 31, and 90 indicating another distinct grouping. The blue cluster comprised the rest as 8, 15, 26, 27 together with 45, 73, 75, 94, 103, and 118, showing a more diverse genetic relationship (Fig. 2).

The iPBS dendrogram showed a slightly different clustering pattern with three main groups. The red cluster included genotypes 15, 29, 30, 45, 75, 90, and 94 similar to the RAPD and ISSR results. The green cluster contained genotypes 8, 18, 26, and 89, indicating another distinct grouping. The blue cluster comprised the rest of the genotypes, showing a more diverse genetic relationship (Fig. 2).

The combined analysis dendrogram, which integrated the data from RAPD, ISSR, and iPBS markers, provided a more comprehensive view of the genetic relationships among the genotypes. The red cluster included genotypes 15, 29, 45, 75, 90, and 94 consistently indicating their close genetic relationship across all marker systems. The green cluster contained genotypes 18 and 89.. The blue cluster comprised most of the genotypes as 8, 26, 27, 30, 31, 36, 48, 61, 73, 76, 103, and 118, exhibiting a broader genetic diversity (Fig. 2).

The clustering analysis using different marker systems revealed consistent genetic relationships among some walnut genotypes, particularly in the cluster containing genotypes 8, 26, and 27 which appeared closely related across all analyses. The other clusters possessed some variability depending on the marker system used, indicating different aspects of genetic diversity being captured by each marker type. The combined analysis provided a more robust understanding of the genetic structure of the walnut genotypes, integrating the strengths of each marker system to give a comprehensive view of their genetic relationships.

The PCoA results for the twenty promising walnut genotypes, based on ISSR, RAPD, iPBS, and combined markers, revealed distinct clustering patterns and genetic relationships (Fig. 3). The principal coordinates of the analyzed twenty promising walnut genotypes depend on the applied molecular markers. In the ISSR marker analysis, PC 1 explained 33.75% and PC 2 explained 24.59% of the variance. It was observed that genotypes 94, 73, and 75 were closely related, clustered near the center, indicating strong genetic similarities. Another distinct group included genotypes 48, 36, 89, and 29, which also exhibited close genetic relationships. In contrast, genotypes 15, 27, and 26 were positioned further apart from the other clusters, reflecting broader genetic diversity within this group. Genotype 76, while slightly isolated, showed some genetic similarity to the central cluster (Fig. 3).

Fig. 3:

The principal coordinates of the analyzed twenty promising walnut genotypes depend on the applied molecular markers. Each point on the graph represents a specific genotype, with the positioning revealing the similarity or divergence among them based on the molecular data applied in the analysis.

For the RAPD marker analysis, PC 1 accounted for 34.04% and PC 2 for 22.44% of the variance. This analysis identified that genotypes 94, 90, 30, 31, 15, and 89 formed a closely related cluster, indicating significant genetic similarity. Another distinct cluster included genotypes 26, 27, and 8, positioned close to each other. Additionally, genotypes 61, 48, 36, and 29 exhibited greater genetic diversity and were more spread out. One of the main groups consisted of genotypes 18, 45, 75, 73, 103, and 118, which were positioned further from the main clusters, highlighting significant genetic variation (Fig. 3).

In the iPBS marker analysis, PC 1 explained 36.36% and PC 2 explained 23.81% of the variance. The analysis indicated that genotypes 94 and 90 formed a closely related cluster, demonstrating significant genetic similarities. Another cluster included genotypes 26, 8, 18, and 89, which were closely grouped. Additionally, genotypes 27, 31, 36, 48, 61, 73, 76, 103, and 118 showed broader genetic diversity and were more dispersed. Genotypes 30 and 75 were positioned further from the main clusters, indicating substantial genetic variation (Fig. 3). The combined PCoA analysis, integrating data from ISSR, RAPD, and iPBS markers, provided a comprehensive view of genetic relationships. PC 1 explained 23.83% and PC 2 explained 17.31% of the variance. This analysis revealed that genotypes 8, 18, 26 and 89 consistently formed a closely related cluster across all marker systems, demonstrating robust genetic similarities. Genotypes 90 and 94 also closely related, forming another distinct genetic grouping. Genotypes 15, 29, 30, 45, and 75 exhibited broader genetic diversity and were more dispersed compared to the other clusters On the other hand, the biggest cluster was formed by genotypes 27, 31, 36, 48, 61, 73, 76, 103, and 108, resulting in a total of four clusters in the PCA while there were three in the clustering analysis (Fig. 3). These results underscore the value of using multiple marker systems to capture a nuanced understanding of genetic diversity and relationships among walnut genotypes, providing essential insights for breeding and conservation programs.

The genetic structure of walnut genotypes was analyzed using STRUCTURE, and the results are displayed in Fig. 4a. The bar plot illustrates how individual genotypes are assigned to different genetic clusters, each represented by a different color. The analysis revealed three main genetic clusters. The red cluster primarily includes genotypes 8,30, 36, 76, 73, and 118 which exhibit a high proportion of red color, indicating their genetic similarity and grouping into one cluster. The green cluster consists mainly of genotypes 15, 18, 27, 61, 89, 90, 94, and 103, characterized by a significant proportion of green color, signifying their genetic distinctiveness from the red cluster. Lastly, genotypes 26, 29, 31, 45, 48 and 75 display a high proportion of blue color, indicating that they form a separate genetic cluster (Fig. 4a).

Fig. 4:

The genetic structure of walnut genotype populations carried out using STRUCTURE HARVESTER. The proportions of gene contributions (a) and Delta K values according to the Evanno test (b). Different colors represent the genetic participation ratio for each relevant group.

Some genotypes exhibit mixed colors, indicating admixture or genetic similarity to more than one cluster. For example, genotypes 15, 61, and 89 show a mix of red, green, and blue colors, suggesting they have genetic contributions from all three clusters. Genotypes 26 and 75 showed a high proportion of green, indicating their close relationship with green-colored genotypes. Additionally, genotype 31 consisted of nearly equal parts red and blue, suggesting it is a mixture of these two clusters (Fig. 4a). The Delta K plot indicates the population number of clusters (K) (Fig. 4b). The highest peak in the plot corresponds to the optimal number of clusters, K=3, confirming the presence of three distinct genetic clusters among the walnut genotypes analyzed. These results provide insights into the genetic structure and diversity of the walnut genotypes, identifying clear genetic groupings and highlighting genotypes with mixed ancestry. Understanding this genetic structure is crucial for breeding and conservation programs, as it helps identify genetically distinct groups and manage genetic resources more effectively.