Polymorphism parameters of ISSR markers
All ISSR primers produced scorable and well-defined amplification products, and showed polymorphisms among the 59 analyzed barley accessions (Table 2). The 45 ISSR primers used in this study generated 255 scorable polymorphic bands. The numbers of amplified bands detected in our study ranged between 1 and 11 for the ISSR markers UBC-813 and ISSR-9, respectively. The major allele frequency reached from 0.10 to 0.81, with 0.36 as an average allele per marker. Major alleles with the highest frequency (81%) were observed for the ISCS20 marker. The gene diversity values were observed in the variety of 0.32–0.96 with an average value of 0.77 per ISSR marker. PIC revealed complete consent of discriminating power of ISSR primers, suggesting a high efficiency of this DNA marker to discover genetic diversity among barley accession used in this investigation. Our results showed that the PIC values ranged from 0.96 (ISSR-8 and UBC-823) to 0.29 (ISCS20) with a mean of 0.74 (Table 2). This represents the positive capability of ISSR-8 and UBC-823 primers to assess genotyping in barley germplasm, and therefore provides a useful tool for analyzing population genetics on diverse plants and recognizing population. In the current study, 26 ISSR markers had PIC values larger than the average PIC value (0.74), which could be helpful for trait mapping and tagging studies in Iraqi barley accessions. The ability to determine genetic differences among different genotypes may be more directly related to the number of polymorphisms identified with each marker technique employed in diversity research. In a previous experiment conducted by a group of researchers, Yongcui et al. (2005) studied 60 barley accessions using two molecular marker techniques (RAMP and ISSR). For ISSR in their investigation, the PIC value ranged from (0.20 to 0.93) with an average of 0.676.
Table 2
Sequences, annealing temperature, and polymorphism information parameters of the ISSR markers used in this investigation.
ISSR markers | Sequences (5–3) | Annealing temperature (°C) | Allele number | No. of polymorphism | Major allele frequency | Gene diversity | PIC |
UBC-808 | AGAGAG AGAGAGAGAGC | 50.00 | 11.00 | 5.00 | 0.24 | 0.86 | 0.84 |
UBC-811 | GAGAGAGAGAGAGAGAC | 50.00 | 6.00 | 3.00 | 0.42 | 0.66 | 0.60 |
UBC-812 | GAGAGAGAGAGAGAGAA | 50.40 | 33.00 | 8.00 | 0.12 | 0.95 | 0.95 |
UBC-818 | CACACACACACACACAG | 52.80 | 22.00 | 9.00 | 0.36 | 0.84 | 0.83 |
UBC-823 | TCTCTCTCTCTCTCTCC | 50.00 | 40.00 | 9.00 | 0.10 | 0.96 | 0.96 |
UBC-825 | ACACAC ACACACACACT | 50.00 | 15.00 | 6.00 | 0.34 | 0.84 | 0.82 |
UBC-826 | ACACACACACACACACC | 50.00 | 22.00 | 9.00 | 0.15 | 0.92 | 0.91 |
UBC-841 | GAGAGAGAGAGAGAGACTC | 50.00 | 12.00 | 4.00 | 0.66 | 0.55 | 0.53 |
UBC-888 | CGTCGTCGTCACACACACACACA | 52.00 | 29.00 | 6.00 | 0.12 | 0.95 | 0.94 |
UBC-891 | ACTACTACTTGTGTGTGTGTGTG | 52.00 | 38.00 | 8.00 | 0.20 | 0.94 | 0.93 |
UBC-847 | CACACACACACACACAGC | 50.00 | 27.00 | 7.00 | 0.10 | 0.95 | 0.94 |
UBC-813 | CTCTCTCTCTCTCTCTT | 50.00 | 2.00 | 1.00 | 0.63 | 0.47 | 0.36 |
UBC-810 | GAGAGAGAGAGAGAGAT | 50.00 | 21.00 | 6.00 | 0.24 | 0.89 | 0.88 |
UBC-814 | CTCTCTCTCTCTCTCTA | 50.00 | 10.00 | 5.00 | 0.36 | 0.79 | 0.76 |
UBC-815 | CTCTCTCTCTCTCTCTG | 50.00 | 21.00 | 8.00 | 0.17 | 0.92 | 0.91 |
UBC-822 | TCTCTCTCTCTCTCTCA | 50.00 | 28.00 | 6.00 | 0.12 | 0.94 | 0.94 |
UBC-834 | AGAGAGAGAGAGAGAGGT | 50.00 | 9.00 | 5.00 | 0.34 | 0.80 | 0.76 |
UBC-845 | CTCTCTCTCTCTCTCTTG | 50.00 | 23.00 | 9.00 | 0.24 | 0.89 | 0.88 |
ISSR-1 | AGACACACACACACACAT | 50.00 | 7.00 | 3.00 | 0.32 | 0.76 | 0.73 |
ISSR-6 | GCCTCCTCCTCCTCCTCC | 50.00 | 13.00 | 5.00 | 0.22 | 0.85 | 0.84 |
ISSR-7 | AGATCCTCCTCCTCCTCC | 50.00 | 12.00 | 6.00 | 0.54 | 0.68 | 0.67 |
ISSR-8 | ATCACACACACACACACA | 50.00 | 32.00 | 9.00 | 0.08 | 0.96 | 0.96 |
ISSR-9 | CACACACACACACACATG | 50.00 | 27.00 | 11.00 | 0.14 | 0.93 | 0.93 |
ISSR-10 | GAGAGAGAGAGAGAGAGG | 50.00 | 26.00 | 10.00 | 0.20 | 0.93 | 0.92 |
ISSR-12 | AGAGAGAGAGAGAGAGCT | 50.00 | 8.00 | 6.00 | 0.56 | 0.63 | 0.58 |
ISSR-14 | GAGAGAGAGAGAGAGAGC | 50.00 | 9.00 | 5.00 | 0.46 | 0.75 | 0.73 |
ISSR-16 | ACACACACACACACACGA | 50.00 | 7.00 | 3.00 | 0.46 | 0.69 | 0.65 |
ISSR-18 | TGTGTGTGTGTGTGTGG | 50.00 | 18.00 | 8.00 | 0.24 | 0.86 | 0.84 |
UBC-846 | CACACACACACACACAAT | 50.00 | 34.00 | 10.00 | 0.20 | 0.93 | 0.93 |
UBC-849 | GTGTGTGTGTGTGTGTGA | 50.00 | 20.00 | 8.00 | 0.34 | 0.84 | 0.83 |
UBC-852 | GATAGATAGACAGACA | 48.00 | 4.00 | 2.00 | 0.53 | 0.59 | 0.51 |
UBC-856 | ACACACACACACACACYA | 42.00 | 3.00 | 2.00 | 0.49 | 0.53 | 0.42 |
UBC-881 | GGGTGGGGTGGGGTG | 54.00 | 31.00 | 8.00 | 0.15 | 0.94 | 0.94 |
HB-10 | GAGAGAGAGAGACC | 50.00 | 21.00 | 7.00 | 0.25 | 0.88 | 0.87 |
HB-11 | GTGTGTGTGTGTCC | 50.00 | 12.00 | 6.00 | 0.49 | 0.73 | 0.71 |
HB-15 | GTGGTGGTGGC | 50.00 | 8.00 | 4.00 | 0.53 | 0.66 | 0.62 |
ISSR-AGC6G | AGCAGCAGCAGCAGCAGCG | 55.00 | 3.00 | 2.00 | 0.58 | 0.51 | 0.41 |
PCP-3 | GTGCGTGCGTGCGTGC | 60.00 | 16.00 | 5.00 | 0.36 | 0.82 | 0.81 |
ISCS20 | DHBCGACGACGACGACGA | 60.00 | 5.00 | 3.00 | 0.81 | 0.32 | 0.29 |
ISCS21 | BDBACAACAACAACAACA | 55.00 | 8.00 | 4.00 | 0.73 | 0.45 | 0.43 |
ISSR-CCA | DDBCCACCACCACCACCA | 55.00 | 6.00 | 4.00 | 0.71 | 0.47 | 0.45 |
HB-12 | CACCACCACGC | 48.50 | 5.00 | 3.00 | 0.68 | 0.50 | 0.47 |
B-13 | CAACAACAACAACAA | 44.70 | 5.00 | 3.00 | 0.64 | 0.54 | 0.50 |
ISSR-CA | BDBCACACACACACA | 50.00 | 9.00 | 4.00 | 0.31 | 0.80 | 0.77 |
Mean | | | 16.32 | 5.80 | 0.36 | 0.77 | 0.74 |
Total | | | | 255.00 | | | |
Polymorphism information of CDDP markers
Genome-conserved sections in various plant species have helped to develop molecular markers, such as SCoT and CDDP. These markers use longer primers and higher annealing temperatures, making them more reliable, reproducible, and simple to create than other arbitrary markers like RAPD or DAF. Furthermore, they focus on gene domains, making them preferred to random markers in QTL mapping applications. To study genetic diversity among 59 barley accessions, nine CDDP primers were tested. All primers produced scorable fragments. Across all barley accessions, 35 polymorphic bands with an average of 3.89 bands per primer were generated (Table 3). The maximum and minimum number of polymorphic bands were obtained by MADS-1, WRKY-R3, and ERF2 (6 bands) and MYB1, ERF1, and KNOX1 (2 bands), respectively. The frequency of the main allele ranged from 0.34 to 0.75, with a mean value of 0.48. ERF1 had the highest frequency of major alleles in the barley accessions, with a 0.75 frequency. The gene diversity in the barley accessions collection with an average of 0.67 and ranged from 0.42 (ERF1) to 0.82 (WRKYF1). PIC values for nine primers ranged from 0.39 (ERF1) to 0.80 (WRKYF1) with an average value of 0.63 per primer. The PIC values detected in the CDDP primers were ranked in descending order as WRKY-F1 > ERF2 and WRKY-R3 > MYB2 > KNOX1 > ABP1-1 > MYB1 > MADS-1 > ERF1. WRKY-F1 is a transcription factor with developmental and physiological roles in plants (Xie et al. 2005).
The average polymorphic allele (3.89) found in this study was lower than the 4.60 alleles found in barley previously reported by Ahmed et al. (2021) using 10 CDDP primers across 82 barley genotypes. The average PIC value (0.63) in this study was high compared to another study finding documented by Ahmed et al. (2021), who mentioned a PIC mean value of 0.37.
Table 3
Primer information and diversity parameters for the CDDP markers used in this investigation.
CDDP markers | Sequence of primer (5–3) | Annealing temperature (°C) | No. of polymorphism | Major allele frequency | Gene diversity | PIC |
ABP1-1 | ACSCCSATCCACCGC | 50.00 | 3.00 | 0.44 | 0.67 | 0.61 |
WRKYF-1 | TGGCGSAAGTACGGCCAG | 50.00 | 4.00 | 0.34 | 0.82 | 0.80 |
MYB1 | GGCAAGGGCTGCCGC | 50.00 | 2.00 | 0.54 | 0.60 | 0.54 |
MYB2 | GGCAAGGGCTGCCGG | 50.00 | 4.00 | 0.44 | 0.74 | 0.71 |
ERF1 | CACTACCCCGGSCTSCG | 50.00 | 2.00 | 0.75 | 0.42 | 0.39 |
ERF2 | GCSGAGATCCGSGACCC | 50.00 | 6.00 | 0.39 | 0.77 | 0.74 |
KNOX1 | AAGGGSAAGCTSCCSAAG | 50.00 | 2.00 | 0.39 | 0.68 | 0.62 |
MADS-1 | ATGGGCCGSGGCAAGGTGC | 50.00 | 6.00 | 0.63 | 0.55 | 0.51 |
WRKY-R3 | GCASGTGTGCTCGCC | 50.00 | 6.00 | 0.42 | 0.76 | 0.74 |
Mean | | | 3.89 | 0.48 | 0.67 | 0.63 |
Total | | | 35.00 | | | |
Polymorphism analysis of SCoT markers
For the genetic diversity analysis in 59 barley accessions, twelve SCoT markers were used. A total of 101 scorable and sharp polymorphic bands were generated across all barley accessions. The minimum and maximum number of polymorphic bands were obtained by SCoT12 (3 bands) and SCoT32 (15 bands), respectively. The average gene diversity was 0.81, with the SCoT3 primer having the lowest (0.48) and the SCoT16 and SCoT32 primers having the highest (0.97). PIC values for twelve SCoT primers ranged from 0.46 (SCoT3) to 0.97 (SCoT16 and SCoT32) with a mean value of 0.80 per primer (Table 4). The PIC values found in the SCoT primers were ranked in descending order as follows: SCoT16 and 32 > SCoT6 and 29 > SCoT23 > SCoT22 > SCoT7 > SCoT13 > SCoT2 > SCoT36 > SCoT12 > SCoT3. Recently, the relationship between 82 Iranian barley accessions was determined using 10 SCoT markers byAhmed et al. (2021), who scored 54 polymorphic bands. The PIC value for the used marker was ranged between 0.23 (SCoT11) to 0.43 (SCoT28), with an average value of 0.33 per primer. Lately, the relationship between 48 Aegilops triuncialis accessions was determined using 14 SCoT markers byKhodaee et al. (2021), who scored 147 polymorphic bands. The PIC value for the chosen marker ranged from 0.14 (SCoT3) to 0.42 SCoT14, with a mean of 0.26 per primer, which was less than the PIC value in our study.Pour-Aboughadareh et al.(2018) similarly evaluated the molecular genetic diversity and relationships among some Triticum and Aegilops species by using 15 ScoT markers. In total, 164 polymorphic bands were detected in their investigation, and the PIC value was ranged from (0.41) to (0.50) with an average of 0.48 per primer.
Table 4
List of Scot primers used and the information obtained in the barley tested.
SCoT markers | Sequences (5–3) | Annealing temperature (°C) | No. of polymorphism | Major allele frequency | Gene diversity | PIC |
SCoT2 | CAACAATGGCTACCACCC | 50.70 | 4.00 | 0.53 | 0.68 | 0.66 |
SCoT3 | CAACAATGGCTACCACCG | 51.30 | 7.00 | 0.71 | 0.48 | 0.46 |
SCoT6 | CAACAATGGCTACCACGC | 52.10 | 8.00 | 0.24 | 0.89 | 0.89 |
SCoT7 | CAACAATGGCTACCACGG | 51.30 | 9.00 | 0.22 | 0.88 | 0.87 |
SCoT12 | ACGACATGGCGACCAACG | 55.90 | 3.00 | 0.46 | 0.66 | 0.59 |
SCoT13 | ACGACATGGCGACCATCG | 55.40 | 8.00 | 0.31 | 0.86 | 0.85 |
SCoT16 | ACCATGGCTACCACCGAC | 54.10 | 13.00 | 0.10 | 0.97 | 0.97 |
SCoT22 | AACCATGGCTACCACCAC | 51.90 | 6.00 | 0.19 | 0.89 | 0.88 |
SCoT23 | CACCATGGCTACCACCAG | 52.40 | 8.00 | 0.17 | 0.90 | 0.89 |
SCoT29 | CCATGGCTACCACCGGCC | 57.90 | 14.00 | 0.27 | 0.90 | 0.90 |
SCoT32 | CCATGGCTACCACCGCAC | 55.90 | 15.00 | 0.05 | 0.97 | 0.97 |
SCoT36 | GCAACAATGGCTACCACC | 51.50 | 6.00 | 0.44 | 0.69 | 0.65 |
Mean | | | 8.42 | 0.31 | 0.81 | 0.80 |
Total | | | 101.00 | | | |
The allelic abundance of accessions in plants is a measure of their genetic variation resource, often exploited by informative molecular markers that identify populations for screening, breeding, and conservation (Igwe et al. 2021; Vinceti et al. 2013). The allelic count and frequency ranges produced in this research were significant, confirming the informative nature of this collection of primers in barley accessions. The major allele frequency in three markers was in the following order: SCoT (0.31) < ISSR (0.36) < CDDP (0.48), demonstrating the usefulness of SCoT and ISSR markers for determining the allelic diversity of this important crop. The gene diversity reported was arranged in the following order: SCoT (0.81) > ISSR (0.77) > CDDP (0.67), proving the utility of SCoT and ISSR markers in estimating the allelic diversity of barley accessions. The PIC provided proficiency of marker systems, which helped determine the potential and value of the primers used in the process of fingerprinting (Serrote et al. 2020). In the present investigation, the SCoT marker technique revealed a higher mean of PIC (0.80) than ISSR (0.74) and CDDP (0.63), which explains the precise application of the SCoT technique in the assessment of accessions diversity. Based on the above results, the discrimination power of the three approaches for assessing allelic diversity in barley accessions was as follows: SCoT > ISSR > CDDP, exhibiting that SCoT functional gene-based markers were both informative and effective at assessing genetic diversity. In addition to SCoT and ISSR performance, CDDP markers demonstrated a poor ability to differentiate barley accessions. Finally, it was suggested that genetic analyses based on SCoT and ISSR markers would be extremely useful for crop improvement programs, including QTL mapping, genetic diversity estimation, linking maps, and genotype identification, as SCoT markers were derived from the functional region of the genome.
Clustering and population structure analysis of barley accessions
Cluster analysis of different barley accessions
Multivariate statistical approaches are critical in studying genetic diversity. Cluster analysis, one of the multivariate statistical techniques, separates individuals into graphs based on intervals. A marker profile data aims to maintain a genetic relationship between genotypes under investigation, using distance measures that express accessions' relationship. The dendrogram was constructed based on 45 ISSR markers to better estimate the genetic distance among 59 Barley accessions (Fig. 1). The UPGMA method and Jaccard coefficient dissimilarity were performed for analyzing the ISSR markers data set. Two major clades within 7 subgroups at the dissimilarity threshold were indicated (Fig. 1A). The first clade included almost all barley accessions, which comprised 38 tested accessions. This group contains 5 subgroups including the barley accessions: Bhoos-912, BA4, BN6, Bhoos-244, BN2R, CANELA, ABN, A1, MORA, MSEL) grouped in the first subgroup, and the second subgroup consisted of 10 barley accessions named Al-warka, 16 HB, GOB, Numar, Furat 9, Boraak, Radical, Arivat, Rafidain-1, Al-khayr, which mostly spread and cultivated in South of Iraq. In addition, 13 barley accession clustered in the third subgroup, including Black-Bhoos-B, Arabi aswad, Victoria, Scio/3, Acsad strain, Acsad strain, Samr, Irani, White-Halabja, Abiad, White-Zaxo, Black-Zaxo, Black-Bhoos Akre, while the fourth subgroup had 4 barley accession: Al-amal, IBAA-99, IBAA-265, White-Akre. However, Bhoos-H1 was isolated in the fifth subgroup, demonstrating its genetic divergence from the other accessions. Whereas, in the second clade, two main sub-clusters were exhibited. The first sub-cluster comprised 5 accessions (Rehaan, Sameer, Warka-B12, Al-Hazzar, IBAA-995), which are mainly distributed over the South of Iraq, and the rest of the barley accessions were grouped in the second sub-cluster. The clustering pattern for ISSR data showed that barley accessions can be separated into two major groups according to geographical origin. Therefore, to enhance the appearance of heterosis, the breeder may use genetic distance data to make informed decisions about crossing accessions from distinct groups or subgroups for population development, or to promote the analysis of various parents to cross in hybrid combinations. The present outcome supports previous reports on the correlation between ISSR markers and eco-geographical distribution of the accessions (Etminan et al. 2016; Wang et al. 2009; Yongcui et al. 2005). Our results confirm the effectiveness of ISSR markers in evaluating the genetic diversity reported before by Wang et al. (2009), who scored 145 polymorphic bands using 11 ISSR primers.
Regarding the clustering patterns for CDDP primers, two main groups were optioned, but with different sub-clusters. In general, two main sub-clusters were determined in the first group. The first sub-cluster included only Ukranian-Zarayan accession, while the second sub-cluster in the same group separated the barley accessions to the two sets of arrangements. The first sub-sub-cluster was composed of three accessions (Black-Akre, Black-Garmiyan, Black-Chiman), which were cultivated by the farmer in the North of Iraq, and the remaining barley accessions were arranged in the second sub-sub-cluster based on genomic similarity. However, group 2 included only two barley accessions, namely Al-Hazzar and IBAA-995, indicating their genomic differences from the rest of the accessions (Fig. 1B). The number of groups detected by CDDP markers in this study was lower than in the reporting of Ahmed et al. (2021), who exhibited three clusters in 82 Iranian barley accessions. This could be due to the type of markers and the number of accessions used.
Clustering of barley accessions using the SCoT molecular dataset revealed two major classes (Fig. 1C). The first class included most barley accessions categorized into two different sub-clusters. One of them comprised three barley accessions, namely (A1, Arabi Aswad, and Clipper), while the other sub-cluster distributes the most barley accession into three sub-sub-clusters. The first sub-sub-cluster comprised four barley accessions (16 HB, Furat 9, Al-warka, and Black-kalar), which were mostly distributed and cultivated in the South of Iraq. The second sub-sub-cluster included five barley accessions (Black-Akre, Black-Garmiyan, Black-Chiman, White-Zarayan, and Bujayl 2-Shaqlawa). The farmer widely cultivated these barley accessions in North of Iraq, while forty-two barley accessions based on genomic dissimilarity were arranged in the third sub-sub-cluster in this particular group. However, the second class involved only five distinct barley accessions, namely Rehaan, Sameer, Warka, B12, Al-Hazzar, and IBAA-995, which originated from the South of Iraq. The number of clusters formed by the SCoT approach in this study was smaller than that previously reported by Ahmed et al. (2021).
Intriguingly, the general dendrogram constructed using the combined data of all molecular markers used in our investigation (ISSR, CDDP, and SCoT) (Fig. 1D) divided barley accessions into two major clusters. Almost all barley accessions were well distributed in the first cluster, which included fifty barley accessions. In this particular cluster, it is clear that some barley accessions based on genetic distance were grouped, especially for the accessions of Arabi aswad and Clipper, while the rest of the forty-eight accessions were grouped in six sub-sub-clusters. Conversely, the second cluster consisted of nine barley accessions, namely Black-Akre, Black-Garmiyan, Black-Chiman, Bujayl2-Shaqlawa, Rehaan, Sameer, Warka-B12, Al-Hazzar, IBAA-995. Accordingly, our finding supported the available suggestion that many molecular techniques could either be applied individually or in combination with other molecular marker techniques to find reliable information about genetic relationships and assess the genetic variation, which would support strategies for effective collection of barley germplasm and knowing their conservation. Insufficient genetic differentiation could imply a high level of gene flow. Stimulatingly, taking a close look at the dendrogram using two different marker systems, a similar pattern of alignments for most barley accessions can be found. Similarly, a group of researchers, Naceur et al. (2012), worked on 31 barley accessions to reveal genetic distance, and obtained 9 classes demonstrating wide diversity among studied accession. This is probably due to the collection of barley germplasm from three countries (Egypt, Algeria, Tunisia). Whereas, compared to our previous work, seven clusters were obtained using the SSRs marker technique (Tahir et al. 2020). This type of molecular technique was now widely used by another research groups to determine the genetic diversity of plant species (Liu et al. 2020; Saidi et al. 2018; Talebi et al. 2018). For many plants, all three markers have been effectively used to determine genetic relationships and diversity. In addition, DNA analysis using three methods has proved to be an inexpensive and efficient way to provide molecular data for evaluating genetic differences (Amom and Nongdam 2017; Hajibarat et al. 2015; Khodaee et al. 2021; Tiwari et al. 2016). It has been noted the larger the distance between accessions, the greater the likelihood of accumulating wider genetic diversity, which also defines their places on clusters (Skroch and Nienhuis 1995).
Population structure profile in barley accessions
STRUCTURE software and the Bayesian statistical index were used to perform effective population structure assessment, reliable population grouping, and identification of mixed genotypes. Separate and combined data of all molecular markers ISSRs, CDDP, and SCoT were used to estimate the population structure of 59 barley accessions. To measure the level of genetic stratification in a multi-locus data set, the program STRUCTURE has become one of the most commonly used programs. However, this program has its limitation, as it cannot interfere with the number of clusters (k) that best fit the data set (Pritchard et al. 2000). To solve this limitation, the STRUCTURE harvester, a web-based program, has been generated for quickly analyzing and summarizing the data outcome from the STRUCTURE program. Therefore, detecting numbers of K groups that best fit the population will be selected (Evanno et al. 2005). In this investigation, to evaluate the likely number of inferred populations, the likelihood of K equals 1 to 10, with each K-value replicated three times (Earl 2012).
The optimal value of k is 2 in all three used markers (Figs. 2 and 3). The STRUCTURE outcomes suggested the population could be divided into two main populations in all case scenarios. The grouping was mildly following the geographic background of the barley accessions. Although two populations may be practical for our panel, based on the size of the population and the difference in numbers of barley accessions representing three main areas in Iraq from which they were collected. The optimal value of k = 2 occasionally misrepresents the true structure in the population, and could mean that either the STRUCTURE program unsuccessfully identified the underlying genetic structure of the collection, or there is no definite population structure (Cullingham et al. 2020). Therefore, cluster analysis was performed, as mentioned previously, to truly understand the genetic structure of this population. Based on the results of these analyses, it was observed that there are no differences between the two analyses and completely match the cluster analysis obtained by the molecular markers data set. This meets our expectations better, as it was observed that the accessions from the neighboring locations were mostly clustered together compared with the populations derived by STRUCTURE.
Remarkably, the result from STRUCTURE Harvester for all molecular markers demonstrated that (k) value had the maximum peak at K = 2, inferring that the probable number of genetic clusters in the population incorporates all individuals from 59 accessions with the highest likelihood (Figs. 2 and 3). This was observed when the mean of the log of posterior probability was graphed, demonstrating that two populations can be observed, which were visualized in two distinct colors (Green and Red). Based on the membership fractions, the accessions with a probability of 80% or above were assigned to matching populations. with others characterized as an admixture, and indicating the purity of tested materials. The combination of the two mentioned colors represents barley accessions in which they possess different genetic structures.
Concerning the separate and combined analysis of structure for all marker data set conducted in this investigation, the first population indicated in red color including individuals of pure genetic make-up (with the probability of ≥ 80% ), which comprised 16 (Bujayl 2-Shaqlawa, Sameer, Black-Akre, Black-Garmiyan, Black-Chiman, Warka-B12, IBAA-995, Bujayl 3-Shaqlawa, Rehaan, Bujayl 1-Shaqlawa, Ukranian-Zarayan, Al-Hazzar, Abrash, Shoaa, Gk-Omega, and Qala-1), 28 ( Boraak, Bhoos-244, Scio/3, Acsad strain, Al-warka, IBAA-99, Numar, Radical, IBAA-265, Victoria, Rafidain-1, Al-amal, Irani, White-Akre, 16 HB, Samr, Arivat, Black-Bhoos Akre, Black-Bhoos-B, Saydsadiq, Bhoos-H1, Furat 9, BN6, Gk-Omega, White-Halabja, Acsad-14, Black-Zaxo, and Bhoos-912) 12 (Sameer, A1, Clipper, Warka-B12, Black-Garmiyan, Black-Chiman, Arabi aswad, Rehaan, Al-Hazzar, IBAA-995, White-Zarayan, and Bujayl 2-Shaqlawa), and 15 (Sameer, Bujayl 2-Shaqlawa, Black-Akre, Black-Garmiyan, Warka-B12, IBAA-995, Rehaan, Al-Hazzar, Bujayl 3-Shaqlawa, Bujayl 1-Shaqlawa, Abrash, Black-Chiman, White-Zarayan, and Gk-Omega) barley accessions for ISSR, CDDP, SCoT, and combined molecular dataset, respectively. Furthermore, the second population, depicted in green, included barley accessions with a pure genetic background (with the probability of ≥ 80%). This distinct population basically contained 22 (CANELA, Scio/3, Acsad-14, Bhoos-244, IBAA-265, Victoria, ABN, Acsad strain, Bhoos-H1, Boraak, Al-amal, Bhoos-912, MSEL, Irani, BN2R, IBAA-99, White-Halabja, Rafidain-1, Black-Zaxo, Arabi aswad, Black-Bhoos-B, BA4, A1, Abiad, Black-Bhoos Akre, Al-khayr, BN6, MORA, White-Zaxo, and Numar), 26 (Ukranian-Zarayan, White-Zarayan, Al-Hazzar, Black-Garmiyan, Black-Chiman, Rehaan, Abrash, Bujayl 1-Shaqlawa, GOB, Warka-B12, Qala-1, Black-Akre, ABN, Bujayl 3-Shaqlawa, Black-kalar, IBAA-995, BA4, White-Zaxo, MORA, Bujayl 2-Shaqlawa, White-kalar, BN2R, Abiad, MSEL, Sameer, and CANELA), 38 (Bhoos-244, White-Akre, White-Halabja, Abiad, Qala-1, Black-Bhoos-B, IBAA-265, White-Zaxo, CANELA, MSEL, Scio/3, Victoria, Ukranian-Zarayan, Irani, BA4, Al-khayr, Saydsadiq, Black-Bhoos Akre, Black-Zaxo, Boraak, Samr, White-kalar, ABN, MORA, BN2R, Bujayl 1-Shaqlawa, Acsad strain, Rafidain-1, Bhoos-912, Numar, Al-amal, GOB, Shoaa, Radical, Bujayl 3-Shaqlawa, IBAA-99, Bhoos-H1, and Arivat), and 29 (Bhoos-244, IBAA-265, CANELA, Scio/3, Victoria, Al-amal, White-Halabja, Acsad strain, Boraak, MSEL, ABN, Rafidain-1, Acsad-14, IBAA-99, Bhoos-912, Black-Zaxo, Black Bhoos-B, Irani, Bhoos-H1, Al-khayr, BN2R, BA4, Abiad, Black-Bhoos Akre, BN6, MORA, Radical, White-Zaxo, and Numar) barley accessions in ISSR, CDDP, SCoT as well as all markers data, respectively, while, the remaining barley accessions (with the probability of < 80%) are considered admixture of genome from other populations. Interestingly, in all cases, the combination of two populations in numbers of individuals was much higher than in the admixture form, which showed the uniformity of tested accessions. This finding confirms that the lemma and palea remain tightly closed during the period of pollen release in barley. This phenomenon is known as cleistogamy (Nair et al. 2010). However, admixture probably occurred due to breeding lines developed through random mating by the breeders, for it is a specific trait improvement (Hernandez et al. 2020).
Genetic differences within and among populations
The analysis of molecular variance (AMOVA) based on the results achieved from the molecular data from three different marker systems was suggested that 15,14, 9, and 14% of total variation were among the three populations (North, Middle, and South of Iraq) in which the sample was grown and collected (Table 1) based on the molecular data set from ISSR, CDDP, SCoT and the combined data of all three marker systems, respectively, while the analysis was revealed the variation was much higher within the studied individuals, which were 85, 86, and 91% depending on the data obtained from ISSR, CDDP, and SCoT, respectively (Table 5), whereas, the collective data from all three marker sets propose 86% of total differences were inside the population. These outcomes suggest that barley accessions from Iraq shared a common ancestry and are highly admixed, with high variation within populations from all marker systems in which this investigation was conducted. This result revealed a distinct genetic base for the 59 barley accessions. The AMOVA results confirmed clustering and structure analyses. The partitioning of molecular variance reported that the greatest divergence was detected among individuals in the same population. Genetic diversity within and between populations improves the selection of populations that account for the vast majority of extant variations. If genetic diversity is largely found within a population, it means fewer populations are needed to conserve and sustain the differences in accessions or populations. Conversely, if genetic diversity across populations is preserved, a greater number of preserved across populations, more populations should be emphasized for maintenance and utilization (Igwe et al. 2021).
Table 5
Analysis of molecular variance (AMOVA) showing genetic diversity in the Iraqi barley accessions.
Method | Source | df | SS | MS | Est. Var. | % | P-value |
ISSR data | Among Pops | 2 | 320.99 | 160.49 | 6.59 | 15** | 0.001 |
Within Pops | 56 | 2012.1 | 35.93 | 35.93 | 85** |
Total | 58 | 2333.09 | | 42.52 | 100 |
CDDP data | Among Pops | 2 | 36.76 | 18.38 | 0.74 | 14** | 0.001 |
Within Pops | 56 | 247.55 | 4.42 | 4.42 | 86** |
Total | 58 | 284.31 | | 5.16 | 100 |
SCoT data | Among Pops | 2 | 73.72 | 36.86 | 1.29 | 9** | 0.001 |
Within Pops | 56 | 703.13 | 12.56 | 12.56 | 91** |
Total | 58 | 776.85 | | 13.84 | 100 |
ISSR, CDDP, and SCoT data | Among Pops | 2 | 431.51 | 215.76 | 8.61 | 14** | 0.001 |
Within Pops | 56 | 2962.78 | 52.91 | 52.91 | 86** |
Total | 58 | 3394.29 | | 61.52 | 100% |
Df: degree of freedom, SS: sum of aquare, MS: mean os square, Est. Var: estimated variance, p-value: probability value
Correlation analysis between genetic dissimilarity achieved by three sets of markers data
To compare the genetic distance matrices produced by three marker systems (ISSRs, CDDP, and SCoT), the mantel test was performed. However, this analysis has its limitation, especially in the case of using two different markers (Vieira et al. 2007). In the case of our investigation, 45 ISSR, 9 CDDP, and 12 SCoT primers were conducted. Remarkably, mantel test correlation values revealed a positive significant correlation between and among all three different marker systems in which clusters with a general dendrogram were present (Table 6). The highest mantel value was observed between ISSR and CDDP markers, while the minimum was displayed between ISSR and SCoT markers. This demonstrates the novelty of current work and the possibility of composing reference collections of tested barley accessions using the information attained from genetic profiles tested by three different molecular methods. Natural selection could also clarify the appropriate relationship between the diverse patterns of these markers in the regions exacerbated by ISSR, CDDP, and SCoT markers. These findings are consistent with previous research of Ahmed et al. (2021), who detected significant associations between CDDP and SCoT markers.
Table 6
Mantel coefficient values for correlation between clusters obtained by three different molecular markers.
Markers | ISSR | CDDP | SCoT | Pooled markers data |
ISSR | - | 0.49** | 0.37** | |
CDDP | | - | 0.36** | 0.25** |
SCot | | | - | |