Introduction

Faba bean (Vicia faba L. 2n = 12) is an annual herbal plant that belongs to the Fabaceae family and is considered one of the most important legume crops used in human and animal nutrition because of its high seed protein content (25%–30%) and nutritional value (Crépon et al. 2010; Hou et al. 2015). This crop is widely cultivated globally, where the main producer of beans in the world is China (34.5%), followed by Ethiopia, Australia, France and Morocco (FAOSTAT 2016). According to FAOSTAT (2016), the total cultivated area of faba beans reached approximately 2.15 million hectares yielding more than 4.14 million tons. Faba bean production ranked seventh among all legume crops worldwide. However, in recent decades, the production has noticeably decreased, mainly due to biotic (diseases, weeds, and insects) and abiotic (drought, waterlogging, and marginal management) conditions (El-Fouly 1982). In the last decade, faba bean production was remarked with the lowest yield growth rate among all legume crops, which mainly attributed to different biotic and abiotic stresses (Maalouf et al. 2019). The most efﬁcient way to overcome the stress that limits faba bean production is to generate varieties appropriate to the cultivating conditions of faba beans. In years, faba bean breeders have succeeded in improving disease and pest resistance, quality, yield, and agronomic performance by utilizing excellent germplasm assets. For example, Sallam et al. (2016) identified new quantitative trait loci (QTL) associated with frost tolerance in faba bean using QTL mapping and GWAS analysis. Kaur et al. (2014) detected polymorphism in 45 worldwide faba bean accessions using 657 single nucleotide polymorphism markers and concluded that heterozygosity among accessions was high because of the allogamous nature of the species. However, because of the frequent use of a small number of elite germplasm lines, genetic diversity in faba bean is regularly diminished. Therefore, some studies indicated the declining genetic diversity of faba beans (Yadav et al. 2017). For example, the faba bean genetic diversity in China (the largest producer of this crop) was found to be very narrow as based on the expressed sequence tag marker results of different accessions from Europe and China (Gong et al. 2011). Similarly, in the recent 50 years, the genetic diversity of faba beans in Tunisia was reported to be declining (Babay et al. 2020). Hence, enriching the faba bean genetic diversity becomes critical to the development process of new varieties characterized by tolerance/resistance to abiotic and biotic stresses (Alghamdi et al. 2015, 2017; Ammar et al. 2015). Recently, Nurmansyah et al. (2020) examined the genetic diversity and population structure of 120 faba bean samples, including 116 mutants, using amplified fragment length polymorphism (AFLP) and found that these samples have narrow genetic diversity, and the mutant did not affect the genetic distances between samples.

The existence of genetic diversity to characterize and understand the variation among plant resources is a pre-condition in the development of an effective plant breeding program (Upadhyaya et al. 2011). Previously, typical genetic diversity studies depended on agronomic and morphological traits (Ferguson and Robertson 1999). However, these traits are not sufficient to cover the whole genome diversity, sensitive to the change of environmental conditions and dependent on the developmental stage (Lee 2006). Consequently, it is important to identify the degree of diversity and relation among accessions through its characterization at the molecular level to support agro-morphological diversity characterization.

Molecular markers are not affected by environmental conditions, can be implemented quickly, and are safe for selecting remarkable agricultural traits. Various DNA markers, such as inter-simple sequence repeats (ISSR), have been developed and successfully used to characterize genetic diversity in various crop plants. Outstanding progress has been made throughout previous investigations to identify the genetic similarities or variation for the breeding programs of faba bean; RAPD (Alghamdi 2008; Yahia et al. 2014; Backouchi et al. 2015), ISSR (Alghamdi et al. 2011; Abdel-Razzak et al. 2012; Salazar-Laureles et al. 2015), AFLP (Ammar et al. 2015; Akash et al. 2017; Alghamdi et al. 2017), SSR (Akash et al. 2017; Göl et al. 2017; Rebaa et al. 2017), and SRAP (Alghamdi et al. 2012; Ammar et al. 2015; Alghamdi et al. 2017). However, previous studies examining the worldwide genetic diversity and relationships between different accessions are lacking. Such studies are critical for the development of new varieties and utilization of genetic diversity resources available all over the world. Therefore, the current study was performed to examine the genetic variability, the intra- and inter-genotypic relationships, and population structure of 92 faba bean accessions originated from 43 countries within five continents based on ISSR markers. The results of the current study will surely help in identifying and selecting genetically contrasting accessions to assist in broadening the germplasm base for future faba breeding programs.

Materials and Methods

Plant material

A total of 92 faba bean accessions were used in this study (Table 1), of which 89 were obtained from the United States Department of Agriculture, Agricultural Research Service (USDA, ARS), Western Regional Plant Introduction Station, Pullman, Washington, United States, while the remaining three accessions were obtained from College of Agriculture, King Saud University, Riyadh, Saudi Arabia. The country of origin and phenotypic characteristics of each accession are shown in supplementary Table (S1).

DNA extraction

Genomic DNA was extracted from 3-week-old leaves from each faba bean accession using a modified cetyltrimethylammonium bromide (CTAB) method (Khan et al. 2007). Genomic DNA was quantified on nanodrop 8000- spectrophotometer at 260/280 nm, while the integrity was further confirmed on 1% agarose gel electrophoresis.

ISSR amplifications

Table 1: Faba bean accessions from geographical origins worldwide

Origin (continent)	No. of accessions	No. of countries	Origin (country)
Asia	32	15	Saudi Arabia (3), Yemen (2), Syria (2), Lebanon (2), Jordan (2), Iraq (2), China (2), India (2), Iran (2), Japan (2), Kyrgyzstan (2), Pakistan (3), Taiwan (2), Nepal (2), Australia (2).
Africa	16	7	Egypt (4), Sudan (2), Tunisia (2), Morocco (2), Kenya (2), South Africa (2), Ethiopia (2).
Europe	26	13	Turkey (2), Cyprus (2), Finland (2), France (2), Germany (2), Greece (2), Sweden (2), United Kingdom (2), Spain (2), Poland (2), Italy (2), Hungary (2), Belgium (2).
North America	8	3	United States (4), Canada (2), Mexico (2).
South America	10	5	Ecuador (2), Chile (2), Colombia (2), Bolivia (2), Peru (2).

ISSR primers were obtained from Metabion International AG, Germany. Suitability and reproducibility for genetic diversity of forty-one ISSR were examined. However, 11 primers only showed reproducible and unambiguous bands. The annealing temperature of each primer was optimized based on its melting temperature. Primer sequences and melting temperatures are shown in the supplementary Table (S2). PCR amplification was performed for the extracted DNA from each genotype after dilution to 50 ng/μL. PCR reaction volume was adjusted to 25-μL using PCR Master mix (GoTaq® Green Master Mix, 2X, Promega, USA) on a T100^TM PCR Thermal Cycler (Bio-Rad, USA). Electrophoresis at 5 V/cm of the obtained PCR products was performed using 1.3% agarose gel supplemented with 0.5 μg/mL ethidium bromide in 1× TBE buffer. Pictures of the separated PCR products in agarose gels were captured utilizing a Syngene bio-imaging gel documentation system (Syngene, USA).

ISSR banding patterns analysis

The bands generated via electrophoresis separation for each genotype using ISSR primers were used to assess the genetic diversity based on the pattern of these bands, whether they are present or absent in each lane. On this basis, absent bands were scored a “0” while present ones were scored a “1”. This scoring system was applied for the most distinct and prominent bands only. The scoring matrix produced by this method was analyzed using POPGENE 1.32 (Yeh et al. 2010) to investigate the genetic diversity among the studied genotypes based on several parameters, e.g., Nei’s gene diversity index (He), population diversity (H_S), the Shannon index (I), estimation of gene flow (Nm), inter-population differentiation (G_ST), total gene diversity (H_T) within populations and percentage of polymorphic loci (PPL) (Nei 1973). Partitioning of the genetic variation within and among the different studied population was examined via non-parametric analysis of variance (AMOVA) using GenAlEx 6.503 software (Peakall and Smouse 2006, 2012). Population genomic analyses were performed via GenAlEx through running principal component analysis (PCA) using the haploid binary genetic distance matrix generated from highly confident ISSR to reveal the genetic relationships for the geographic region tested.

Population structure analysis

STRUCTURE 2.3.4 (Pritchard et al. 2000) is a software used for studying population structure using multi-locus genotype data. Structure analysis was performed for the 92 studied faba bean accessions using principal component and Bayesian model-based analyses. First, the optimum number of clusters (K) for whole data was determined via running 10 testing runs of STRUCTURE setting K from 1 to 10. In this stage, correlated allele frequencies and admixture model were assumed in each run, consisting of a burn-in period of 10,000 and Markov Chain Monte Carlo (MCMC) replicates of 100,000. Second, after estimating the optimum K, STRUCTURE software was used again to perform 10 runs setting K from 1 to 5. Nevertheless, each run consisted of a burn-in period of 100,000, and MCMC replicates of 750,000 with correlated allele frequencies and the admixture model assumed. Finally, the most likely K was chosen using the STRUCTURE HARVESTER website (Earl and vonHoldt 2012) depending on the ΔK method (Evanno et al. 2005) and the plateau criterion (Pritchard et al. 2000). The run with the highest likelihood estimates to assign cluster proportions to individuals was used for further representation based on the output. DISTRUCT 1.1 software (Rosenberg 2004) was used for graphical representation of population structure using the output files of STRUCTURE software. The estimated membership cut-off value was determined to 0.8, and all the members with values less than that cut-off were assigned to the mixed group. Afterward, data were fragmented based on estimated membership values, and STRUCTURE analysis was performed again to reveal a lower level of population structure. The same procedure described above was applied again for each fragment of the dataset, starting with testing runs and ending with a graphical presentation using DISTRUCT. Nevertheless, K settings were different for the fragments of the dataset based on the testing runs results.

Results

ISSR polymorphism

A preliminary experiment with a bulk of ten various accessions from different countries was performed to screen a total of 41 ISSR primers. This preliminary analysis showed that only 11 primers produced and amplified polymorphic, reproducible, and unambiguous bands (Table 2). Therefore, these 11 primers were selected to characterize further the 92 different genotypes examined in this study. Further analysis using selected ISSR primers produced 189 repeatable and unambiguous bands, of which 109 bands were polymorphic, and thus the calculated average of polymorphic fragments for each primer was 10. The average percentage of polymorphic bands obtained for all genotypes was 56.75%, and the values ranged from 40.0% to 72.2%. The minimum scored fragment size was 100 bp, and the maximum was 1,800 bp. The average PIC value for each primer was 0.3484, and the different PIC values ranged from 0.089 to 0.715. The clearest patterns and best polymorphism were obtained using the dinucleotide repeats (CA)₈RG (primer FB18) and (AC)₈TG (primer FB46).

Genetic diversity

Table 3 shows the different genetic diversity parameters calculated for each geographical location (continent). The PPL for germplasm from different continents ranged from 21.88% (South America) to 82.81% (Asia). On the other hand, Nei’s gene diversity (He) of germplasm from different geographical locations ranged from 0.0900 (North America) to 0.2065 (Asia). Similarly, Shannon’s information index (I) ranged from 0.1125 (South America) to 0.3233 (Asia). It is evident that the Asian group had the highest genetic diversity, while the South American germplasm group had the lowest genetic diversity. Moreover, African and European accessions had significantly similar genetic diversity parameters indicating high genetic diversity and possible relation between these groups.

Values of Nei’s gene diversity index, Shannon’s information index, and PPL for the germplasm from different countries were 0.116, 0.172, and 53.282%, respectively. The coefficient of gene differentiation (GST) is considered one of the most reliable gene differentiation methods. In this study, GST values for germplasm from different countries were 0.256. Total diversity and population diversity among different countries were 0.116 and 0.086, respectively. Nei’s unbiased genetic distance analysis for all accessions revealed that Kyrgyzstani (Kg 1 - Kg 2), Canadian (Ca 1 - Ca 2) and Bolivian and Peruvian (Bo 1 - Pe 1) germplasm had the lowest genetic distance (0.0237). On the other hand, several accessions showed the maximum genetic distance (0.4212) including, for example, Japanese (Jp 2) with South African (Za 2) and Belgian (Be 2), Ecuadorian with (Ec 2) Iraqi (Iq 2) and Taiwanese (Tw 2), and Iraqi (Iq 1) with Ethiopian (Et 2), Swedish (Se 2), and Ecuadorian (Ec 1). The full matrix of genetic identity and distances between the 92 studied accessions are shown in the supplementary excel sheet (supplementary Excel S1).

AMOVA analysis was performed to validate the G_ST values obtained. This analysis showed that the majority of genetic variation (48%) between different studied germplasm was due to genetic variation among different countries (Table 4). However, only 27% of the variation was due to variance among different geographical regions (continents), and 25% of the variation could be attributed to variance within countries.

Table 2: Amplification result and polymorphism of ISSR primers used in this study

Primer	Sequence	Tm	Bands No.	Polymorphic bands No.	Polymorphic bands (%)	PIC
1	(AG)₈T	50	15	6	40.0	0.5157
2	(GA)₈A	50	18	11	61.1	0.4173
3	(AG)₈YA	53	22	15	68.2	0.1137
4	(AC)₈YG	55	14	6	42.9	0.6116
5	(CA)₈RG	55	18	13	72.2	0.3184
6	(GAG)₄RC	48	17	9	52.9	0.1075
7	(AG)₈GC	56	15	9	60.0	0.7151
8	(AC)₈AG	54	16	8	50.0	0.2148
9	(AC)₈CG	56	18	10	55.6	0.0898
10	(AC)₈GA	54	16	9	56.3	0.4167
11	(AC)₈TG	54	20	13	65.0	0.3122

Tm: Annealing temperature; PIC: Polymorphism information content

Table 3: Statistical analysis of genetic diversity in each geographical location using the ISSR marker (standard deviations in parentheses)

Geographical origin	# of Polymorphic Loci	PPL	He	I
Asia	106	82.81	0.2065 (0.1828)	0.3233 (0.2506)
Africa	80	62.50	0.1765 (0.1825)	0.2747 (0.2604)
Europe	82	64.06	0.1716 (0.1831)	0.2677 (0.2600)
North America	45	35.16	0.0900 (0.1461)	0.1444 (0.2198)
South America	28	21.88	0.0742 (0.1518)	0.1125 (0.2233)
Average	68.2	53.282	0.14376	0.22452

PPL: percentage of polymorphic loci; He: Nei’s gene diversity; I: Shannon’s information index

Table 4: Distribution of genetic variability within and between faba bean countries and regions, measured by AMOVA analysis of the ISSR data

	Source of variance	Df	Sum of square	Variance component	% total of variance	Significance
Groups based on geographical origin	Variance among regions	4	362.182	4.045	27%	P ≤ 0.001
	Variance among countries	38	723.013	7.209	48%
	Variance within countries	49	185.750	3.791	25%
	Total	91	1270.946	15.044	100%

Table 5: Nei’s unbiased measures of genetic identity and distance between regions through the ISSR analysis

Region *	Asia	Africa	Europe	North America	South America
Asia	****	0.9512	0.9182	0.8667	0.8499
Africa	0.05	****	0.9321	0.8736	0.8654
Europe	0.0854	0.0704	****	0.9034	0.9103
North America	0.143	0.1352	0.1015	****	0.897
South America	0.1626	0.1446	0.094	0.1087	****

*Nei’s genetic identity (above diagonal) and genetic distance (below diagonal)

Genetic relationships between different continents

ISSR allele frequencies were used to calculate Nei’s unbiased genetic identity measures and distances between different geographical regions (Table 5). The obtained results showed that the studied faba bean germplasm had a genetic identity ranging between 0.8499 (Asia and South America) and 0.9512 (Asia and Africa). Furthermore, pairwise genetic distances between the studied geographical locations ranged from 0.05 (Asia and Africa) and 0.1626 (Asia and South America).

Clustering analysis

A dendrogram (Fig. 1) was generated based on the UPGMA method for analysis of clusters via POPGEN 1.32 and the studied accessions were separated/clustered into four main groups, where the first group included all Asian and some African accessions, the second group included the remaining African accessions, the third group included all European. The fourth group included accessions from South and North America. The Americas faba bean germplasms were clearly separated from others in UPGMA clustering analysis based on ISSR molecular marker data. It was found that faba bean accessions originated in Africa had close genetic relationships with those that originated in Asia. The obtained dendrogram showed that the Japanese accessions had the furthest relationship

Fig. 1: UPGMA dendrogram of faba bean germplasms from geographical origins worldwide based on haploid binary genetic distance matrix generated from highly confident ISSR. Shapes and colors vary based on geographical origin of each accession: Asian (green circles), African (red squares), European (purple triangles), North American (cyan flipped triangle), and South American (blue rhombus)

with the rest of the Asian accessions. Nevertheless, the Peruvian and Bolivian accessions, both from South America, had the closest relationships. Similarly, the largest genetic distance between studied accessions was observed in the Turkish accessions with the rest of European accessions. On the other hand, there was a significant genetic relationship between Greek and Italian accessions. Generally, accessions that originated in Africa showed a close genetic relationship with those that originated in Europe. Analysis of grouped accessions from each continent showed that accessions originated in Africa and Asia was clustered together. The outcomes of this study showed the faba bean resources from all continents are firmly linked to their geographical origin.

PCA analysis

The genetic relationship between individual accessions was further investigated via PCA after labeling each

Fig. 2: Two-dimension principal components analysis of faba bean accessions from geographical origins worldwide

Fig. 3: Population structure of 92 faba bean accessions using STRUCTURE and DISTRUCT software

studied accession based on its geographical origin. As shown in Fig. 2, accessions originated in Africa, and Asia showed a strong relationship with their geographical origins. Accessions from Asia, Europe, and the two Americas were quite distinct from each other.

Population structure analysis

In this study, STRUCTURE software was used to infer the population structure of the 92 studied faba bean accessions. The ΔK and plateau criterion methods were adopted to identify the number of clusters (K). Analyzing the whole data sets reveals that the data has two clusters (the highest value was for K=2). All accessions from Europe, North America, and South America (n = 42) were separated in one cluster; however, the remaining 50 African and Asian accessions were separated in the other cluster (Fig. 3). The cluster of Europe and the Americas contained accessions from Cyprus (2), Finland (2), France (2), Germany (2), Greece (2), Sweden (2), UK (2), Spain (2), Poland (2), Italy (2), Hungary (2), Belgium (2), Canada (2), Mexico (2), USA (4), Chile (2), Colombia (2), Ecuador (2), Bolivia (2) and Peru (2). On the other hand, Africa and Asia’s cluster contained accessions from Saudi Arabia (3), Yemen (2), Syria (2), Lebanon (2), Jordan (2), Iraq (2), China (2), India (2), Iran (2), Japan (2), Kyrgyzstan (2), Pakistan (3), Taiwan (2), Nepal (2), Australia (2), Egypt (4), Sudan (2), Tunisia (2), Morocco (2), Kenya (2), South Africa (2), Ethiopia (2) and, surprisingly, Turkey (2).

To reveal the lower levels of population structure, all the accessions of each cluster were analyzed independently using STRUCTURE software. Regarding the cluster of Europe and the Americas (with 42 accessions), STRUCTURE analysis revealed 3 different clusters. All accessions from European countries (Cyprus, Finland, France, Germany, Greece, Sweden, U.K., Spain, Poland, Italy, Hungary, Belgium) were clustered together. Similarly, accessions from North America (i.e., Canada, Mexico, USA) were clustered together. All the South American (Chile, Colombia, Ecuador, Bolivia, Peru) accessions were clustered together; however, Columbian accessions showed the lowest estimated membership value (0.813), which is still beyond the specified cut-off value.

On the other hand, Asian and African accessions showed clear separation into two clusters. All accessions from African countries (Egypt, Sudan, Tunisia, Morocco, Kenya, South Africa, Ethiopia) were separated into one cluster. In contrast, all the accessions from Asian countries (Saudi Arabia, Yemen, Syria, Lebanon, Jordan, Iraq, China, India, Iran, Japan, Kyrgyzstan, Pakistan, Taiwan, Nepal) were separated in the other cluster. Furthermore, Turkish and Australian accessions were separated in the Asian cluster.

The results obtained using STRUCTURE analysis were significantly aligned with the PCA analysis, which showed five main clusters; each one represents one continent with all the accessions from countries in each continent gathered in one cluster (Fig. 2). Furthermore, PCA showed that Turkish and Australian accessions were separated within the Asian cluster.

Discussion

In the current study, we analyzed the genetic diversity and relationship of 92 faba bean genotypes grown in different ecotypes distributed in different geographical regions (continents) worldwide. Development of high yield or tolerant crops depends mainly upon the availability of plant genetic resources that could be utilized in different breeding programs aiming to produce plats with superior characteristics (Yadav et al. 2017). Providing access to a wide range of genetic diversity significantly enhances plant breeders’ capacity to develop new elite cultivars (Galluzzi et al. 2020). However, genetic diversity is continuously diminished because of either natural process including domestication and dispersal or breeding programs that aims to selection of specific characteristics (Louwaars 2018). Nevertheless, diversity of genetic resources could be enriched in any crop by identification and detection of new polymorphic bands or new alleles via genetic diversity studies (Nurmansyah et al. 2020). The obtained results in the current study showed that Asian, European, and African germplasm characterized by the highest genetic diversity and the largest number of effective alleles among all other geographical locations, despite the small size samples of the European (n = 26) and African (n = 16) groups as compared to the Asian group (n = 32). The groups obtained from North America and South America showed the lowest genetic diversity than other groups. This could be attributed to the size of the sample as it was very small (North America: n = 8, South America: n = 10). It is well established that larger sample sizes retain higher number of polymorphic markers (Franco-Duran et al. 2019). Furthermore, the Asian and European accessions represented 15 and 13 different countries, respectively, which adds to the geographical range of the studied accessions and increases genetic diversity. Different growing regions of these accessions have different climate conditions, potentially adding more to the selection of genetic uniqueness and differentiation for the gene pool of faba beans. On the other hand, genotypes from North America and South America represented 3 and 5 countries, respectively, limiting the genetic selection and the genetic diversity among the studied accessions.

ISSR marker analysis was applied in the current study to compare different faba bean genotypes from different geographical areas using locations worldwide. A similar genetic diversity between accessions originated in Africa and Europe was observed in terms of PPL, Nei’s gene diversity, and Shannon’s information index. However, genetic diversity was less in genotypes of North America and South American groups. These results are in accordance with the results reported earlier by Zong et al. (2010). The genetic diversity parameters showed high values (e.g., PPL = 53.282%) among species. These results could be attributed to the collection of different species from different geographical locations (continents) worldwide and thus had significant genetic diversity. Geographical barriers between different countries lead to separation between germplasm and thus higher genetic diversity observed in germplasm collected from different countries/continents (Duc et al. 2010; Zong et al. 2010; Galluzzi et al. 2020). The results of the ISSR marker were further confirmed by AMOVA analysis. The obtained results showed that around 48% of the variation between the studied genotypes could be attributed to variance among countries. G_ST values for germplasm from different countries in this study was 0.256. In the studies of population genetic diversity, G_ST values are considered high if exceeded 0.15 (Nei 1978). The values obtained in this study indicate a great genetic variation between accessions from different geographical locations, confirming the results of other analysis and the results reported earlier (Alghamdi et al. 2011; Abdel-Razzak et al. 2012; Ammar et al. 2015; Akash et al. 2017; Alghamdi et al. 2017).

The American faba bean accessions were clearly separated from other continents’ accessions, in light of PCA and UPGMA grouping analysis. The gene pools from different regions with genetic uniqueness and differentiation might be related to reproductive separation because of wide geographic isolation and non-overlapping natural adaptation (Rajaram 1999; Zong et al. 2009). Polignano et al. (1999) studied the phenotypic variations among different faba bean germplasm and found that geographical origin has a significant relation to phenotypic variation. Moreover, the genetic diversity of Ethiopian and Afghani faba bean accessions were evaluated based on quantitative characters with an obvious relationship between geographic origin and phenotypic differences (Polignano et al. 1993).

In the current study, population structure analysis using STRUCTURE software revealed the same results as the PCA. Several studies indicated PCA and Bayesian model-based STRUCTURE analysis showed the same results in different plants, e.g., Persian walnut (Mohsenipoor et al. 2010; Bernard et al. 2018), coconut (Odong et al. 2011), soybean (Torres et al. 2015), sweet cherry (Campoy et al. 2016), maize (Vigouroux et al. 2008), coffee (Cubry et al. 2013) and apples (Liang et al. 2015).

Moreover, numerous vital regional-based variations associated with various morphological trait combinations were detected in a collection of faba bean germplasm pure lines (Robertson and El-Sherbeeny 1991) and likewise the case for some traits relating to the tolerance of biotic and abiotic stresses (Duc et al. 2010). These morphological data showed extraordinary differentiation from different regions of faba bean genotypes. The current study results confirmed that the genetic differentiation between faba bean genotypes has a clear connection with their geographical origin. Consequently, divergent natural selection and reproductive isolation emerging from wide geographic separation might be a significant explanation behind the faba bean gene pools differentiation from different regions. These results will surely help in development of strategic breeding programs via providing the required information regarding the available genetic resources could be utilized.

Calculated Nei’s genetic identity based on ISSR allele frequencies showed a great identity between Asian, African, and European genotypes. The pairwise distances between the genotypes belonging to these geographical locations were lower than the distances between them, and the genotypes belong to North America or South America. Moreover, the results obtained from UPGMA analysis based on the continent that each country belongs to showed that North America and South America groups were significantly isolated from all the other geographical locations. There was a close genetic relationship between Asian, African, and European genotypes. Therefore, further breeding studies examining the potential application of crosses between Asian genotypes and other genetic materials collected from different continents with higher diversity including African and European genotypes may have an extraordinary importance in production of new cultivars or improvement of existing ones. In a previous report, the genetic diversity of 97 elite faba bean accessions from North Africa, Asia, Northern, and Southern Europe were examined using AFLP primers (Zeid et al. 2003). The clustering analysis based on PCA and Jaccard’s similarity coefficient showed that Asian lines were grouped, while North African and South European lines were grouped. Therefore, it was believed that faba bean mainly originated from the Middle or the Near East and then expanded to Central and Northwestern Europe and finally to North Africa moving to Asia (Zong et al. 2009, 2010). Additionally, some similarity was distinguished among North African and European germplasm in molecular data, which affirmed the spread courses (Cubero 1974). The results of the current study shed light on the global genetic diversity of faba bean germplasm and thus paved the way for future breeding programs aiming to utilize such diversity for production of or improvement of faba bean cultivars.

Conclusion

This study provided a comprehensive genetic diversity and population structure analysis among 92 faba bean accessions collected from different geographical using ISSR markers. The results obtained in the current study indicated that the genetic diversity and structure of the faba bean populations worldwide are significantly connected to their ecological distribution and geographical origin, useful for germplasm management and future breeding programs. However, there is a need to conduct more extensive studies and examinations of agronomical and morphological characters to affirm the distinct differentiation of gene pools between faba bean accessions from different parts of the world.

Acknowledgments

The authors are thankful to the Research Supporting Project number (RSP-2020/86), King Saud University, Riyadh, Saudi Arabia.

Author Contributions

Ahmed A. Qahtan: Conceptualization, Methodology, investigation, software, formal analysis, writing - original draft; Abdulrahman A. Al-Atar: Methodology, validation, funding acquisition, supervision, writing - review and editing; Eslam M. Abdel-Salam: Conceptualization, methodology, investigation, software, formal analysis, writing - original draft; Mohamed A. El-Sheikh: Methodology, validation, writing - review and editing; Abdel-Rahman Z. Gaafar: Conceptualization, methodology, formal analysis, writing - original draft; Mohammad Faisal: Methodology, formal analysis, writing - review and editing.

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Abstract

Introduction

Materials and Methods

Plant material

DNA extraction

ISSR amplifications

ISSR banding patterns analysis

Population structure analysis

Results

ISSR polymorphism

Genetic diversity

Genetic relationships between different continents

Clustering analysis

PCA analysis

Population structure analysis

Discussion

Conclusion

References