Caryologia. International Journal of Cytology, Cytosystematics and Cytogenetics 75(1): 131-140, 2022 Firenze University Press www.fupress.com/caryologia ISSN 0008-7114 (print) | ISSN 2165-5391 (online) | DOI: 10.36253/caryologia-1428 Caryologia International Journal of Cytology, Cytosystematics and Cytogenetics Citation: Huang Jing, Somayeh Esfandani-Bozchaloyi (2022) Genetic diversity and gene-pool of Medicago polymorpha L. based on retrotrans- poson-based markers. Caryologia 75(1): 131-140. doi: 10.36253/caryolo- gia-1428 Received: October 19, 2021 Accepted: March 31, 2022 Published: July 6, 2022 Copyright: © 2022 Huang Jing, Somayeh Esfandani-Bozchaloyi. This is an open access, peer-reviewed article pub- lished by Firenze University Press (http://www.fupress.com/caryologia) and distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distri- bution, and reproduction in any medi- um, provided the original author and source are credited. Data Availability Statement: All rel- evant data are within the paper and its Supporting Information files. Competing Interests: The Author(s) declare(s) no conflict of interest. Genetic diversity and gene-pool of Medicago polymorpha L. based on retrotransposon-based markers Huang Jing1,*, Somayeh Esfandani-Bozchaloyi2 1 Department of information and Electronic Engineering, Hunan City University, Hunan 413000, China 2 Faculty Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran *Corresponding author. E-mail: hyrwss@163.com Abstract. The genus Medicago L. (Fabaceae) comprises approximately 87 different spe- cies of herbs and shrubs widespread from the Mediterranean to central Asia. Medicago polymorpha is a herbaceous legume that can be a useful pasture plant, in particular, in regions with a Mediterranean climate. It had aroused great interest due to high nutri- tious quality, highly palatability and N-fixing plan in neutral soil. There is no informa- tion on its population genetic structure, genetic diversity, and morphological variability in Iran. Due to the medicinal importance of this species, a genetic variability and pop- ulations’ structure study is performed studying 15 geographical populations of Medica- go polymorpha. Therefore, we used six inter-retrotransposon amplified polymorphism (IRAP) markers and 15 combined IRAP markers to reveal within and among popula- tion genetic diversity in this plant. AMOVA test produced significant genetic difference (PhiPT = 0.46, P = 0.010) among the studied populations and also revealed that, 66% of total genetic variability was due to within population diversity while, 34% was due to among population genetic differentiation. Mantel test showed positive significant correlation between genetic distance and geographical distance of the studied popula- tions. STRUCTURE analyses and population assignment test revealed some degree of gene flow among these populations. PCoA plot of populations was in agreement with UPGMA clustering of molecular data. These results indicated that geographical popu- lations of Medicago polymorpha are well differentiated based on (IRAP) markers. Keywords: gene flow, IRAP, Medicago polymorpha, population differentiation. INTRODUCTION Knowledge of spatial genetic structures provides a valuable tool for inferring the evolutionary forces such as selective pressures and drift (bi et al, 2021; cheng et al, 2021; khayatnezhad and gholamin, 2020, 2021a, 2021b). Low gene flow due to spatial isolation of populations may even increase the degree of local differentiation (karasakal et al, 2020a, 2020b; huang et al, 2021; hou et al, 2021, guo et al, 2021). Nevertheless, phenotypic plastic- ity rather than genetic differentiation may be an alternative way of matching 132 Huang Jing, Somayeh Esfandani-Bozchaloyi genotypes to environment; indeed increasing environ- mental variation favors higher levels of plasticity (MA et al, 2021; peng et al, 2021; Si et al., 2021; sun et al., 2021; miao et al 2018; zou et al, 2019; wang et al 2020; xiao- long et al, 2021; hou et al, 2021). The genus Medicago L. (Fabaceae) comprises approx- imately 87 different species of herbs and shrubs wide- spread from the Mediterranean to central Asia (Small, 2010), including the widely cultivated forage crop and weedy species M. sativa L. (commonly named alfalfa or Lucerne) and the legume model species M. truncatula Gaertn. (Steele et al., 2010). The annuals species collec- tively known as “medics” are naturally distributed over a very wide range of environmental conditions in the Mediterranean basin. Some medics have been intro- duced to regions of Australia, Chile, South Africa and United States with Mediterranean-type climate. Med- ics, as well as other annual pasture legumes, have a high feeding quality, determined by higher protein, min- eral and vitamin contents (Keivani et al., 2010). Due to their capacity to fix atmospheric nitrogen and improve soil fertility in symbiosis with soil bacteria collectively known as ‘rhizobia’, Medicago species do not need cost- ly and polluting chemical nitrogen fertilizer (Small and Jomphe, 1989). The genus Medicago in Iran has been revised by dif- ferent authors. Boissier (1872), in his Flora Orientalis, published 11 Medicago species for Iran. Parsa (1948), Moussavi (1977) and Heyn (1984) recognized 14, 16 and 11 species in Iran, respectively. Mehregan & al. (2001) reported 18 species of the genus Medicago from Iran. Two main reasons can be accounted for the disagree- ments over the taxonomic status of this genus in Iran: (1) incomplete collecting; and (2) taxonomic confusions encountered in Medicago. Medicago polymorpha L. is an annual herbaceous and can be a useful pasture plant, in particular, in regions with a Mediterranean climate, self-compatible and diploid (2n = 14) (Salhi Hannachi et al., 1998). It had aroused great interest due to high nutritious qual- ity, high palatability and N-fixing capability in neutral soil (Abdelkefi et al., 1996). M. polymorpha is a spe- cies of Mediterranean origin, but its species range is wide spread throughout the world. The wide diffusion and adaptability can be explained by its low sensitiv- ity to photoperiod and vernalization (Aitken, 1981). Three botanical varieties of this species were identified by Heyn (1963): brevispina; polymorpha and vulgaris. In Iran, M. polymorpha grows in a range of environments from humid to arid. In recent years, molecular marker systems such as randomly amplified polymorphic DNA (RAPD), ampli- fied fragment length polymorphism (AFLP), inter sim- ple sequence repeat (ISSR), simple sequence repeat (SSR) and inter-retrotransposon amplified polymorphism (IRAP) have been used to measure genetic variation and relationships in cultivars and landraces of Medicago spe- cies. For instance, Genetic diversity among and within 10 populations of Iranian alfalfa, from different areas of Azarbaijan was analyzed by screening DNA from seeds of individual plants and bulk samples (Mohammadza- deh et al, 2011). Morpho-phenological diversity among natural populations of Medicago polymorpha of different Tunisian ecological areas (Badri et al, 2016). Their results from analysis of variance (ANOVA) showed that differ- ences among populations and lines existed for all traits, with population explaining the greatest variation for measured traits. Genetic relationships of 98 alfalfa (Med- icago sativa L.) germplasm accessions examined using morphological traits and SSR markers from Europe, USA, Australia, New Zealand and Canada ( Cholastova, Knotova, 2012). Moreover, due to extensive morphologi- cal variability of this species in the country, there is pos- sibility of having infra-specific taxonomic forms. There- fore, we carried out population genetic analysis and morphometric study of 15 geographical populations for the first time in the country. For genetic study, we used the inter-retrotransposon amplified polymorphism (IRAP) method that displays insertional polymorphisms by amplifying the segments of DNA between two retrotransposons. It has been used in numerous studies of genetic diversity (Smykal et al., 2011). The objectives of this research were to study genetic diversity among Medicago polymorpha cultivars/popula- tion with a different geographical origin by inter-retro- transposon amplified polymorphism (IRAP) method, to determine genetic variation among and within materials using IRAP markers. MATERIALS AND METHODS Plant materials A total of 89 individuals were sampled representing 15 natural populations of Medicago polymorpha in East Azerbaijan, Lorestan, Kermanshah, Gilan, Mazandaran, Golestan and Ardabil Provinces of Iran during July- Agust 2019-2020. Fresh leaves of 5-8 individuals from each population, were collected, and immediately dried in Silica Gel. Different references were used for the cor- rect identification of species (Medicago polymorpha) (Boissier ,1872; Parsa 1948). 133Genetic diversity and gene-pool of Medicago polymorpha L. based on retrotransposon-based markers DNA extraction and IRAP assay Fresh leaves were used randomly from 5-10 plants in each of the studied populations. These were dried by silica gel powder. CTAB activated charcoal protocol was used to extract genomic DNA. The quality of extracted DNA was examined by running on 0.8% agarose gel. A set of six outward-facing LTR primers (Smykal et al., 2011; Table 1) were used for IRAP analysis. We also used 15 different combinations of outward-facing LTR pair primers. PCR reactions were carried in a 25μl volume containing 10 mM Tris-HCl buffer at pH 8; 50 mM KCl; 1.5 mM MgCl2; 0.2 mM of each dNTP (Bioron, Ger- many); 0.2 μM of a single primer; 20 ng genomic DNA and 3 U of Taq DNA polymerase (Bioron, Germany). The thermal program was carried out with an initial denaturation for 1 min at 94°C, followed by 40 cycles in three segments: 35 s at 95°C, 40s at 47°C and 55s at 72°C. Final extension was performed at 72°C for 5 min. The amplification products were observed by running on 1% agarose gel, followed by the ethidium bromide stain- ing. The fragment size was estimated by using a 100 bp molecular size ladder (Fermentas, Germany). Molecular analyses The IRAP profiles obtained for each samples were scored as binary characters. Parameter like Nei’s gene diversity (H), Shannon information index (I), number of effective alleles, and percentage of polymorphism were determined (Weising et al., 2005). Nei’s genetic distance among populations was used for Neighbor Joining (NJ) clustering and Neighbor- Net networking (Huson and Bryant, 2006). Mantel test checked the correlation between geographical and genet- ic distance of the studied populations (Podani, 2000). These analyses were done by PAST ver. 2.17 , DARwin ver. 5 (2012) and SplitsTree4 V4.13.1 (2013) software. AMOVA (Analysis of molecular variance) test (with 1000 permutations) as implemented in GenAlex 6.4 (Peakall and Smouse, 2006), and Nei,s Gst analysis as implemented in GenoDive ver.2 (2013) (Meirmans and Van Tienderen, 2004) were used to show genetic differ- ence of the populations. Moreover, populations, genetic differentiation was studied by G’ST est = standardized measure of genetic differentiation (Hedrick, 2005), and D_est = Jost measure of differentiation (Jost, 2008). The genetic structure of populations was studied by Bayesian based model STRUCTURE analysis (Pritchard et al. 2000), and maximum likelihood-based method of K-Means clustering of GenoDive ver. 2. (2013). For STRUCTURE analysis, data were scored as dominant markers. The Evanno test was performed on STRUC- TURE result to determine proper number of K by using delta K value (Evanno et al., 2005). In K-Means cluster- ing, two summary statistics, pseudo-F, and Bayesian Information Criterion (BIC), provide the best fit for k. Gene flow was determined by (i) Calculating Nm an estimate of gene flow from Gst by PopGene ver. 1.32 (1997) as: Nm = 0.5(1 - Gst)/Gst. This approach consid- ers equal amount of gene flow among all populations. (ii) Population assignment test based on maximum like- lihood as performed in Genodive ver. in GenoDive ver. 2. (2013). The presence of shared alleles was determined by drawing the reticulogram network based on the least square method by DARwin ver 5. (2012). RESULTS Populations, genetic diversity Genetic diversity parameters determined in 15 geo- graphical populations of Medicago polymorpha are pre- sented in Table 2. The highest value of percentage poly- morphism (53.75%) was observed in Ardabil, Khalkhal- Asalem Road (population No.1) which shows high value for gene diversity (0.32). and Shanon, information index (0.39). Population Kermanshah: Ghasre-Shirin, 5 km from Paveh to Nusod (No.9) has the lowest value for per- centage of polymorphism (31.43%) and the lowest value for Shanon, information index (0.030), and He (0.011). Population genetic differentiation AMOVA (PhiPT = 0.74, P = 0.010), and Gst analysis (0.367, p = 0.001) revealed significant difference among the studied populations (Table 3). It also revealed that, 66% of total genetic variability was due to within pop- ulation diversity and 34% was due to among popula- tion genetic differentiation. Pairwise AMOVA produced significant difference among the studied populations. Table 1. M. polymorpha IRAP primers based on Smykal et al. (2011) study. IRAP Sequence (5´-3´) GU735096 ACCCCTTGAGCTAACTTTTGGGGTAAG GU980589 AGCCTGAAAGTGTTGGGTTGTCG GU929878 GCATCAGCCTGGACCAGTCCTCGTCC GU735096 CACTTCAAATTTTGGCAGCAGCGGATC GU929877 TCGAGGTACACCTCGACTCAGG GU980590 ATTCTCGTCCGCTGCGCCCCTACA 134 Huang Jing, Somayeh Esfandani-Bozchaloyi Moreover, we got high values for Hedrick standardized fixation index after 999 permutation (G’st = 0.367, P = 0.001) and Jost, differentiation index (D-est = 0.176, P = 0.001). These results indicate that the geographical popu- lations of Medicago polymorpha are genetically differen- tiated from each other. Populations, genetic affinity In UPGMA tree, plant samples of each populations, were grouped together and formed separate cluster. In the studied specimens we did not encounter inter- mediate forms. These results showed that IRAP data can differentiate the populations of Medicago polymor- pha in two different major clusters or groups (Figure 1). The first major cluster that was supported with sig- nificant bootstrapping values of higher than 50%, was divided into two main sub-clusters so that plants of Ardabil,Meshkin shahr, hatam Forest, Ardabil, Meshkin shahr, Sabalan MT, Shahbil, Qotursooi Villageand and Ardabil: Germi, 20 km from Germi to Pars-Abad (P8- P 9, 12; Province Ardabil) and West Azerbaijan, Kaleybar and Azarbaijan (E): Ahar, 45 Km from Meshkin-Shahr Table 2. Genetic diversity parameters in the studied populations Medicago polymorpha (N = number of samples, Na= number of dif- ferent alleles; Ne = number of effective alleles, I= Shannon’s infor- mation index, He = gene diversity, UHe = unbiased gene diversity, P%= percentage of polymorphism, populations). Pop Na Ne I He UHe %P Pop1 0.341 1.058 0.39 0.32 0.31 53.75% Pop2 0.455 1.077 0.277 0.24 0.22 55.05% Pop3 0.499 1.067 0.24 0.23 0.24 49.26% Pop4 0.555 1.020 0.22 0.25 0.28 43.53% Pop5 0.431 1.088 0.20 0.22 0.25 41.53% Pop6 0.255 1.021 0.25 0.28 0.22 47.15% Pop7 0.261 1.024 0.292 0.23 0.23 43.15% Pop8 0.886 1.183 0.184 0.116 0.122 44.29% Pop9 0.686 1.157 0.030 0.011 0.022 31.43% Pop10 0.643 1.173 0.154 0.102 0.109 30.00% Pop11 0.243 1.033 0.026 0.018 0.029 34.29% Pop12 0.400 1.087 0.076 0.051 0.057 40.29% Pop13 0.286 1.046 0.040 0.027 0.032 37.14% Pop14 0.400 1.112 0.090 0.062 0.069 35.71% Pop15 0.576 1.144 0.122 0.083 0.095 39.18 Figure 1. UPGMA clustering of populations in Medicago polymorpha based on IRAP data. Bootstrap value from 1000 replicates are indi- cated below branches (Population numbers are according to Table 1). Table 3. Analysis of molecular variance (AMOVA) of the studied species. Source df SS MS Est. Var. % ΦPT Among Pops 20 216.576 21.327 9.082 66% 66% Within Pops 59 114.767 9.530 1.530 34% Total 79 321.342 10.613 100% df: degree of freedom; SS: sum of squared observations; MS: mean of squared observations; EV: estimated variance; ΦPT: proportion of the total genetic variance among individuals within an accession, (P < 0.001). 135Genetic diversity and gene-pool of Medicago polymorpha L. based on retrotransposon-based markers to Ahar (P10,15; Province West Azerbaijan ) comprised the first sub-cluster due to morphological similarity, while the plants of Ardabil, Khalkhal-Asalem Road (P1) formed the second sub-cluster. Similarly, the second major cluster included two sub-clusters too: the first sub- cluster contained Lorestan: Khorram-Abad, 60 km from Pol-Dokhtar to Khorram-Abad (P11) and Kermanshah: Paveh, Paveh Shahid Kazemi Forest Park (P3) , while plants of Gilan, Mazandaran and Golestan Provine (Northen Iran) (P2- 4,5,6,7,13,14) were grouped into the second sub-cluster. Genetic divergence and separation of populations Lorestan (P11) and Kermanshah (P3) as well as P8- P 9, 12 (Province Ardabil) from the other populations is evi- dent in PCoA plot of IRAP data after 900 permutations (Figure.3). The other populations showed close genetic affinity. Mantel test after 5000 permutations produced significant correlation between genetic distance and geo- graphical distance in these populations (r = 0.48, P = 0.001). Therefore, the populations that are geographically more distant have less amount of gene flow, and we have isolation by distance (IBD) in Medicago polymorpha. Populations genetic structure K = 3 reveal the presence of 3 genetic group. Simi- lar result was obtained by Evanno test performed on STRUCTURE analysis which produced a major peak at k = 3 (Figure.3). Both these analyses revealed that Medicago polymorpha populations show genetic strati- fication. STRUCTURE plot based on k = 3, revealed genet- ic difference of populations 11 and 12 (differently colored), as well as 13 and 14 (Figure.4). But it showed genetic affinity between populations 1-10 and 15 (simi- larly colored). The mean Nm = 0.654 was obtained for all IRAP loci, which indicates low amount of gene flow among the populations and supports genetic strati- fication as indicated by K-Means and STRUCTURE analyses. Population assignment test also agreed with Nm result and could not identif y significant gene flow among these populations. However, reticulogram obtained based on the least square method (Figure not included), revealed some amount of shared alleles among populations 1 and 5, and between 13 and 6 and 7, also between 8, and 9. This result is in agreement with grouping we obtained with PCoA plot, as these popula- tions were placed close to each other. As evidenced by STRUCTURE plot based on admixture model, these shared alleles comprise very limited part of the genomes in these populations and all these results are in agree- ment in showing high degree of genetic stratification within Medicago polymorpha populations. In total 120 IRAP bands (loci) were obtained, out of which 34 bands were private. Populations 1-7, 8, 14 and 15 contained 1-4 private bands. Figure 2. PCoA plot of populations in Medicago polymorpha based on IRAP data. 136 Huang Jing, Somayeh Esfandani-Bozchaloyi DISCUSSION Population genetics analyses are important in genetic and breeding studies (Kizzie-Hayford et al 2021; Wasana et al 2021; Sawadogo et al., 2021; Paul et al 2021; Mieso & Befa et al 2020). They provide information on the levels of genetic variation, partitioning of genetic variability within/between populations, inbreeding or outcrossing, effective population size and popula- tion bottleneck (GHOLAMIN and KHAYATNEZHAD, 2020a; 2020b, 2020c). The advent of molecular markers has greatly improved population genetic studies. These markers have been used to identify potentially novel genotypes among the many Medicago polymorpha acces- sions. In recent years, molecular marker systems such as randomly amplified polymorphic DNA (RAPD), ampli- fied fragment length polymorphism (AFLP), inter sim- ple sequence repeat (ISSR), simple sequence repeat (SSR) and inter-retrotransposon amplified polymorphism (IRAP) have been used to measure genetic variation and relationships in cultivars and landraces (ren and khay- atnezhad 2021; khayatnezhad and Nasehi 2021, i et al., 2021; jia et al, 2021). Transposable elements, particu- larly retrotransposons, comprise most of plant genomes. Their replication generates genomic diversity and makes them an excellent source of molecular markers (Smykal et al., 2011). The inter-retrotransposon amplified poly- morphism (IRAP) method displays insertional polymor- phisms by amplifying the segments of DNA between two retrotransposons. It has been used in numerous studies of genetic diversity (Smykal et al., 2011). In China a population genetic study of two species, M. lupulina and M. ruthenica, reported that these types germplasm were valuable resources for improving med- icago forage crops (Badri et al., 2011). This information has different applications, and from pure understand- ing of biology of the species to conservation of endan- gered species, choosing of proper parents for hybridiza- tion and breeding and phylogeography and mechanism of invasion. In this study, we investigated the genetic diversity of M. polymorpha populations. The main aim of our study was to evaluate the genetic diversity of M. polymorpha genotypes. To reach this objective, and to be able to detect segregating populations, we used the available inter-retrotransposon amplified polymorphism (IRAP) marker. The results of Diwan et al. (2000) study showed that SSR markers produced by M. truncatula are valuable genetic markers for the genus Medicago. These markers will be useful in establishing the genomic rela- tionships important forage such as alfalfa and other annual medics. Among the 120 studied lines of M. poly- morpha, there was no spineless line. Our studies showed that the average number of 6.7 alleles per locus may be due to the high level of homozy- gous nature of M. polymorpha. Acording to Flajoulot et al. (2005) the number of allels per locus ranging were 3_24 in Medicago sativa. In contrast to work by Baquer- izo et al. (2001) used six simple sequence repeat to ana- lyse the genetic diversity and relationships between individuals of Medicago truncatula, showed to be high- ly diverse with an average of 25 alleles per locus. As a result, our studies emphasize that genetic variation has Figure 3. Evanno test of Medicago polymorpha populations based on k = 3 of IRAP data. Figure 4. STRUCTURE plot of Medicago polymorpha populations based on k = 3 of IRAP data. (Population numbers are according to Table 1). 137Genetic diversity and gene-pool of Medicago polymorpha L. based on retrotransposon-based markers been effective in determining population relationships and the AMOVA results was level of among populations diversity (66%) is higher than within populations diver- sity (34%). The markers used in this study were highly effective in detecting the level of genetic diversity in the polymorphic and studied populations. Also the Arda- bil, Khalkhal-Asalem Road population was high gene diversity and high polymorphism percentage. Min et al. (2017) investigated the extensive development of genes with micro-RNA-based SSR markers in M. trunculata. The mean value of information content of their polymor- phisms was 0.71, indicating a high level of information. In other study the average of polymorphism information was 78.75% in M. trunculata and a total of 24 alleles were amplified with an average of 3 alleles per locus (Jafari et al. 2013). Also in other study reported informations pol- ymorphism by SSR marckers indicating a high level of polymorphism (> 70%) for M. trunculata for M. truncu- lata and other annual medics (Diwan et al. 2000). Genetic diversity is of fundamental importance to the survival of a species (sun and khayatnezhad 2021; tao et al, 2021; wang et al, 2021; xu et al., 2021; yin et al., 2021; zhang et al, 2021). Degree of genetic variability within a species is highly correlated with its reproduc- tive mode, the higher degree of open pollination/cross breeding generally producing higher levels of genetic variability. According to Hamrick and Godt (2012) spe- cies that have selfing or mixed mating systems have lower levels of genetic variability then predominantly outcrossed species and 51% of their total genetic diver- sity is apportioned between populations in comparison to 10% for outcrossed species. Our study indicated a low level of heterozygosity (He = 0.01-0.32) in M. polymor- pha. The substantially higher selfing rate in M. polymor- pha likely contributed to a lower overall level of esti- mated heterozygosity. Like this our study a low level of He reported 0.246 in M. lupulina (Badri et al., 2011). The our study degree average of selfing rate ( 18.78) levels outcrossing (-10.78). The mean Nm = 0.654 was obtained for investigated IRAP loci, which indicates low amount of gene flow among the populations and supports genet- ic stratification as indicated by STRUCTURE analyses. By examining the biological results, it can be observed that the smaller the genetic distance between popula- tions, they are more similar to each other, because of the shape of the seed of the species studied, it is light and easy to move and propagated by wind and other factors. This confers diversity, which results in AMOVA analysis showing that percentages within and among populations are relative, and since M. polymorpha is a selfing plant and regeneration occurs within the species population, which causes. Among-population differentiation in phe- notypic traits and allelic variation can be the result of drift, founder effects and local selection. According to Badri et al. (2016), among the 120 studied lines of M. polymorpha that they studied, envi- ronmental variance was higher than genetic variance for most traits and consequently had a relatively low average of heritability. Also they showed that there was no sig- nificant association between population differentiation and geographical distances. These results are consistent with previous findings showing an absence of significant correlation between geographical distance and population differentiation in annual Medicago species (Badri et al. 2008, 2010; Zheng, et al., 2021; Zhu et al, 2021) and Brachypodium hybri- dum Catalán, Joch. Müll., Hasterok & Jenkins (Neji et al. 2014). ACKNOWLEDGMENT The authors thank anonymous reviewers for valu- able comments on an earlier draft. REFERENCES Abdelkefi, A., M. Boussaid, A. Biborchi, A. Haddioui, A. Salhi-Hanachi and M. Marrakchi, 1996. Genetic diversity inventory and valuation of spontaneous spe- cies belonging to Medicago genus in Tunisia. Cahiers Options méditerranéennes, 18: 143-150. Aitken Y (1981). Temperate herbage grasses and leg- umes. In Handbook of Flowering. Halevy, CRC, Boca Raton, Florida. Baker, H.G., Geranium purpureum Vill. and G. rober- tianum L. in the British flora. II: Geranium purpure- um, Watsonia, 1955, vol. 3, pp. 160–167. Boissier, E. 1872: Medicago in Flora Oriental is 2: 90-105.-Rep. 1975, by A. Asher & Co. B. V., Amster- dam. Badri M, Zitoun A, Soula S, Ilahi H, Huguet T, Aouani ME (2008). Low levels of quantitative and molecular genetic differentiation among natural populations of Medicago ciliaris Kroch. (Fabaceae) of different Tuni- sian eco-geographical origin. Conserv. Genet. 9:1509- 1520. Badri M, Arraouadi S, Huguet T, Aouani ME (2010). Comparative effects of water deficit on Medicago lac- iniata and M. truncatula lines sampled from sympa- tric populations. J. Plant Breed. Crop Sci. 2:192-204 Badri M, Chardon F, Huguet T, Aouani ME (2011). Quantitative Trait Loci associated with drought tol- 138 Huang Jing, Somayeh Esfandani-Bozchaloyi erance in the model legume Medicago truncatula. Euphytica 181:415-428. Badri, Najah Ben Cheikh, Asma Mahjoub and Ched- ly Abdelly (2016). Morpho-phenological diversity among natural populations of Medicago polymorpha of different Tunisian ecological areas., Watsonia, Vol. 15(25), pp. 1330-1338. Bi, D., C. Dan, M. Khayatnezhad, Z. Sayyah Hashjin, Z. Y. Ma (2021): Molecular Identification And Genetic Diver- sity In Hypericum L.: A High Value Medicinal Plant Using Rapd Markers Markers. Genetika 53(1): 393-405. Cheng, X., X. Hong, M. Khayatnezhad, F. Ullah (2021): Genetic diversity and comparative study of genomic DNA extraction protocols in Tamarix L. species.” Caryologia 74(2): 131-139. Evanno, G, S., Regnaut, J., Goudet (2005): Detecting the number of clusters of individuals using the soft- ware STRUCTURE: a simulation study. Mol. Ecol., 14:2611-2620. Gholamin, R. M. Khayatnezhad (2020a): Assessment of the Correlation between Chlorophyll Content and Drought Resistance in Corn Cultivars (Zea Mays). Helix 10(05): 93-97. Gholamin, R. M. Khayatnezhad (2020b): The effect of dry season stretch on Chlorophyll Content and RWC of Wheat Genotypes (Triticum Durum L.). Bioscience Biotechnology Research Communications 13(4): 1833-1829. Gholamin, R. M. Khayatnezhad (2020c): Study of Bread Wheat Genotype Physiological and Biochemical Responses to Drought Stress. Helix 10(05): 87-92. Guo, L.-N., C. She, D.-B. Kong, S.-L. Yan, Y.-P. Xu, M. Khayatnezhad F. Gholinia (2020). Prediction of the effects of climate change on hydroelectric generation, electricity demand, and emissions of greenhouse gases under climatic scenarios and optimized ANN model. Energy Reports 7: 5431-5445. Hou, R., S. Li, M. Wu, G. Ren, W. Gao, M. Khayatnezhad. F. Gholinia (2019: Assessing of impact climate parameters on the gap between hydropower supply and electricity demand by RCPs scenarios and opti- mized ANN by the improved Pathfinder (IPF) algo- rithm. Energy 237: 121621 Huson, D.H., D., Bryant (2006): Application of Phyloge- netic Networks in Evolutionary Studies. Mol. Biol. Evol., 23: 254−267. Heyn CC (1963). The Annual Species of Medicago. Scrip- ta hierosolymitana Hebrew University Press Jerusa- lem. Heyn, C. C. 1984: Medicago in, K. H. Rechinger (Ed.), Flora Iranica 157: 253-271. -Akadernische Druch- u. Verlagsanstalt, Graz. Huang, D., J. Wang, M. Khayatnezhad (2020): Estimation of Actual Evapotranspiration Using Soil Moisture Balance and Remote Sensing” Iranian Journal of Sci- ence and Technology, Transactions of Civil Engineer- ing: 1-8. I, A., X. Mu, X. Zhao, J. Xu, M. Khayatnezhad and R. Lalehzari (2020). Developing the non‐dimensional framework for water distribution formulation to eval- uate sprinkler irrigation. Irrigation and Drainage. Jia, Y., M. Khayatnezhad, S. Mehri (2020). Population dif- ferentiation and gene flow in Rrodium cicutarium: A potential medicinal plant. Genetika 52(3): 1127-1144. Jost, L. (2008): GST and its relatives do not measure dif- ferentiation. Mol. Ecol., 17: 4015−4026. Keivani, M. S. Sanaz Ramezanpour, H. Soltanloo, R. Choukan, M. Naghavi and M. Ranjbar, “Genetic diversity assessment of alfalfa (Medicago sativa L.) populations using AFLP markers,” Australian Journal of Crop Science,” vol. 4, no. 7, pp. 491-497, 2010. Karasakal, A., M. Khayatnezhad, R. Gholamin (2020a). The Durum Wheat Gene Sequence Response Assess- ment of Triticum durum for Dehydration Situations Utilizing Different Indicators of Water Deficiency. Bioscience Biotechnology Research Communications 13(4): 2050-2057. Karasakal, A., M. Khayatnezhad, R. Gholamin (2020b): The Effect of Saline, Drought, and Presowing Salt Stress on Nitrate Reductase Activity in Varieties of Eleusine coracana (Gaertn). Bioscience Biotechnol- ogy Research Communications 13(4): 2087-2091. Khatamsaz, M. (1995): Caprifoliaceae. In: Assadi, M. & al. (eds), Flora of Iran, no. 13. Tehran. Khayatnezhad, M. R. Gholamin (2020): Study of Durum Wheat Genotypes’ Response to Drought Stress Con- ditions. Helix, 10(05): 98-103. Khayatnezhad, M. And R. Gholamin (2021a): The Effect of Drought Stress on the Superoxide Dismutase and Chlorophyll Content in Durum Wheat Genotypes. Advancements in Life Sciences, 8(2): 119-123. Khayatnezhad, M. And R. Gholamin (2021b): Impacts of Drought Stress on Corn Cultivars (Zea mays L.) At the Germination Stage. Bioscience Research 18(1): 409-414. Khayatnezhad, M. F. Nasehi (2021): Industrial Pesticides and a Methods Assessment for the Reduction of Associated Risks: A Review.” Advancements in Life Sciences 8(2). Kizzie-Hayford, N., Ampofo-Asiama, J., Zahn, S., Jaros, D., Rohm, H. (2021). Enriching Tiger Nut Milk with Sodium Caseinate and Xanthan Gum Improves the Physical Stability and Consumer Acceptability. Jour- nal of Food Technology Research, 8(2), 40–49. 139Genetic diversity and gene-pool of Medicago polymorpha L. based on retrotransposon-based markers Ma, S., M. Khayatnezhad, A. A. Minaeifar (2021): Genetic diversity and relationships among Hypericum L. spe- cies by ISSR Markers: A high value medicinal plant from Northern of Iran. Caryologia, 74(1): 97-107. Mieso, B., Befa, A. 2020. Physical Characteristics of the Essential Oil Extracted from Released and Improved Lemongrass Varieties, Palmarosa and Citronella Grass. Agriculture and Food Sciences Research, 7(1), 65–68. Moussavi M. 1977: A help to Identification of Medicago species in Iran. -Ministry of Agriculture, Tehran. [In Persian]. Neji M, Geuna F, Taamalli W, Ibrahim Y, Smida M, Badri M, Abdelly C, Gandour M (2014). Morpho-phenolog- ical diversity among Tunisian natural populations of Brachypodium hybridum. J Agric. Sci. 153(6):1006-1016. Parsa, A. 1948: Medicago in Flora de l’Iran 2: 171- 181. - Publication du Ministere de r Education, Museum l’Histoire Naturelle de Tehran, Tehran. Paul, S., Ara, R., Ahmad, M.R., Hajong, P., Paul, G., Kob- ir, M.S., Rahman, M.H. (2021). Effect of Blanching Time and Drying Method on Quality of Black Pep- per (Piper nigrum). Journal of Food Technology Research, 8(1), 18–25. Peng, X., M. Khayatnezhad, L. Ghezeljehmeidan (2021): Rapd profiling in detecting genetic variation in stel- laria l. (caryophyllaceae). Genetika-Belgrade, 53(1): 349-362. Peakall, R., P.E., Smouse (2006): GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes, 6: 288−295. Podani, J. (2000): Introduction to the Exploration of Mul- tivariate Data. Backhuyes, Leiden, pp.407. Pritchard, J.K., M., Stephens, P., Donnelly (2000): Infer- ence of population structure using multilocus geno- type Data. Genetics, 155: 945−959. Ren, J. M. Khayatnezhad (2021): Evaluating the stormwa- ter management model to improve urban water allo- cation system in drought conditions. Water Supply. Sawadogo, Y.A., Cisse, H., Oumarou, Z., Nikiema, F., Traore, Y., Savadogo, A. (2021). Reduction of Afla- toxins and Microorganisms in the Koura-Koura Pro- duced in Burkina Faso with Spices and Aromatic Leaves. Journal of Food Technology Research, 8(1), 9–17. Si, X., L., Gao, Y. Song, M, Khayatnezhad, A.a. Minaei- far (2020): Understanding population differentiation using geographical, morphological and genetic char- acterization in Erodium cicunium. Indian J. Genet., 80(4): 459-467. Sun, Q., D. Lin, M., Khayatnezhad, M. Taghavi (2020): Investigation of phosphoric acid fuel cell, linear Fresnel solar reflector and Organic Rankine Cycle polygeneration energy system in different climatic conditions. Process Safety and Environmental Protec- tion, 147: 993-1008. Sun, X. And M. Khayatnezhad (2019): Fuzzy-probabilis- tic modeling the flood characteristics using bivariate frequency analysis and α-cut decomposition. Water Supply. Small E (2010). Alfalfa and relatives: Evolution and clas- sification of Medicago. NRC Research Press Ottawa Ontario Canada. Salhi Hannachi, A., M. Boussaid and M. Marrakchi, 1998. Genetic variability organisation and gene flow in natural populations of Medicago polymorpha L. prospected in Tunisia. Genetics Selection Evolution, 30(Suppl. 1): S121-S135. Steele K.P., Ickert-Bond S.M., Zarre S. and Wojciechows- ki M.F. (2010) Phylogeny and character evolution in Medicago (Leguminosae): evidence from analyses of plastic TRNK/MATK and nuclear GA3OX1 sequenc- es. American Journal of Botany 97(7): 1142–1155. Smykal, P., N., Bacova-Kerteszova, R., Kalendar, J., Corander, A.H., Schulman, M., Pavelek (2011): Genetic diversity of cultivated flax (Linum usitatis- simum L.) germplasm assessed by retrotransposon- based markers. TAG, 122: 1385–1397. Tao, Z., Z., Cui, J., Yu, M., Khayatnezhad (2021): Finite Difference Modelings of Groundwater Flow for Con- structing Artificial Recharge Structures. Iranian J. Sci. Techn., Transactions of Civil Engineering. Wang, C., Y. Shang, M. Khayatnezhad (2021): Fuzzy Stress-based Modeling for Probabilistic Irrigation Planning Using Copula-NSPSO. Water Resources Management. Wasana, W.L.N., Ariyawansha, R., Basnayake, B. 2021. Development of an Effective Biocatalyzed Organic Fertilizer Derived from Gliricidia Sepium Stem Bio- char. Current Research in Agricultural Sciences, 8(1), 11–30. Xu, Y.-P., P. Ouyang, S.-M., Xing, L.-Y., Qi, M., Khayat- nezhad, H., Jafari (2020): Optimal structure design of a PV/FC HRES using amended Water Strider Algo- rithm. Energy Reports, 7: 2057-2067. Yin, J., M. Khayatnezhad, A. Shakoor (2020): Evaluation of genetic diversity in Geranium (Geraniaceae) using rapd marker. Genetika, 53(1): 363-378. Zhang, H., M. Khayatnezhad, A. Davarpanah (2019): Experimental investigation on the application of car- bon dioxide adsorption for a shale reservoir. Energy Science & Engineering n/a(n/a). Zheng, R., S. Zhao, M. Khayatnezhad, S, Afzal Shah (2020): Comparative study and genetic diversity in 140 Huang Jing, Somayeh Esfandani-Bozchaloyi Salvia (Lamiaceae) using RAPD Molecular Markers. Caryologia, 74(2): 45-56. Zhu, K., L. Liu, S. Li, B., Li, M. Khayatnezhad, A. Sha- koor (2019): Morphological method and molecular marker determine genetic diversity and population structure in Allochrusa. Caryologia, 74(2): 121-130. Zhu, P., H. Saadati, M. Khayatnezhad (2020): Application of probability decision system and particle swarm optimization for improving soil moisture content. Water Supply.