Caryologia. International Journal of Cytology, Cytosystematics and Cytogenetics 75(3): 159-167, 2022 Firenze University Press www.fupress.com/caryologia ISSN 0008-7114 (print) | ISSN 2165-5391 (online) | DOI: 10.36253/caryologia-1560 Caryologia International Journal of Cytology, Cytosystematics and Cytogenetics Citation: Masoud Sheidai, Moham- mad Mohebi Anabat, Fahimeh Kooh- dar, Zahra Noormohammadi (2022). Iden- tifying potential adaptive SNPs within combined DNA sequences in Genus Crocus L. (Iridaceae family): A mul- tiple analytical approach. Caryologia 75(3): 159-167. doi: 10.36253/caryolo- gia-1560 Received: January 31, 2022 Accepted: October 02, 2022 Published: April 5, 2023 Copyright: © 2022 Masoud Sheidai, Mohammad Mohebi Anabat, Fahimeh Koohdar, Zahra Noormohammadi. This is an open access, peer-reviewed article published by Firenze University Press (http://www.fupress.com/caryo- logia) and distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, 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. Identifying potential adaptive SNPs within combined DNA sequences in Genus Crocus L. (Iridaceae family): A multiple analytical approach Masoud Sheidai1,*, Mohammad Mohebi Anabat1, Fahimeh Koohdar1, Zahra Noormohammadi2 1 Department of Plant Sciences and Biotechnology, Faculty of Life Sciences and Biotech- nology, Shahid Beheshti University, Tehran, Iran 2 Department of Biology, School of Basic Science, Science and Research Branch, Islamic Azad University, Tehran, Iran *Corresponding author. E-mail: msheidai@sbu.ac.ir Abstract. The genus Crocus L. of Iridaceae family contains about 160 species and is considered as a complex group of plant taxa with regard to evolutionary and phylo- genetic events. Inter-specific hybridization and gene flow contribute to species genetic homogeneity in one hand and high within species genetic variability and species genet- ic content overlaps caused species resolution a problem. In spite of extensive molecular phylogenetic studies in this genus, nothing is known about DNA sequences or Single nucleotide polymorphisms (SNPs) which are of adaptive nature. Moreover, nothing is known about which geographical or environmental factors plays role in species local adaptation and speciation events within Crocus L. genus. Therefore, the present study was conducted to answer the above said questions. We used a combined molecular data set of internal transcribed spacer (ITS) nuclear gene and trnL-F intergenic spacer (trnL-F) sequences of chloroplast genome. A multiple analytical method of Canoni- cal correlation (CCA), Redundency analysis (RDA), and Latent Factor Mixed Model (LFMM)identified a few potential adaptive SNPs. Moreover, population criterions like Tajimas’ D, molecular clock test, as well as skyline-plot revealed a smooth and con- tinuous genetic changes for most of the Crocus species, but the occurrence of a sudden deep nucleotide substitution for Crocus taxa of Iran. The impact of latitude was signifi- cantly higher on nucleotide changes compared to that of longitudinal distribution of Crocus species. Keywords: Crocus, adaptive divergence, SNPs, speciation. 1. INTRODUCTION The genus Crocus L. (family Iridaceae), has about 100 species and con- tains an economically important species Crocus sativus L., the edible saffron. The species of this genus are distributed from Western Europe and north- 160 Masoud Sheidai et al. western Africa to Western China. Though the Asia Minor is the center of genus diversity (Sheidai et al., 2017), many species grow in the Mediterranean region (Saxena, 2010). Several studies were concerned with molecular phylogeny and DNA barcoding of this genus which produced valuable information on different molecular aspects of genus. Aghighiravan et al. (2019), reported that ITS barcode is the best molecular marker for phy- logenetic investigation on Crocus L. genus. Similarly, Sheidai et al. (2017), reported a high degree of genetic variability both within and among the studied species in the genus and that ISSR molecular markers are useful in Crocus species delineation. Along with the species rela- tionships, these authors also reported population frag- mentation and inter-specific gene flow in these taxa. In a recent investigation, Mohebi et al. (2021), pre- sented both DNA barcode and chromosome number variation in the genus. These authors suggested that molecular events like horizontal gene transfer (HGT) and deep coalescence may be associated with geographi- cal distribution and Crocus taxa diversification. Due to importance of this genus and also lack of knowledge on geographical association of the genetic differences in Crocus species, we carried out a detailed bioinformatic analyses of a combined molecular data set of ITS nuclear DNA sequences and trnL-F chloroplast sequences, to : 1- Identify discriminating nucleotide sequences among Crocus species, 2- Illustrate if these sequences are sig- nificantly associated with geographical coordinates, 3- Identify nucleotide sequences with phylogenetic impor- tance. For bioinformatic studies, we used different ana- lytical approaches like discriminate analysis of principal components analysis (DAPC), which is suitable for SNP sequences, as well as both CCA (Canonical correspond- ence analysis), and RDA (Redundancy analysis). Moreo- ver, some data on Crocus species expansion were also produced by using population genetics analysis methods of Tajimas’ D value, molecular clock test, and mismatch nucleotide pair test. The findings of this research are new to Crocus science. 2. MATERIAL AND METHODS In this study, ITS nuclear DNA and trnL-F sequenc- es of 68 Crocus species were obtained from National Center for Bioinformatic Information (NCBI). In addi- tion, we used two species of the genus Romulea as out- group taxa because of the high similarity to Crocus (Goldblatt et al., 2006; Petersen et al., 2008) (Table 1). 2.1. Data analyses Sequence alignment and curation was done by MUSCLE program implemented out in molecular evolu- tionary genetics analysis (MEGA) 7 program. Mismatch analysis and skyline plot was constructed in R package 4.2. These sequences were then used to construct Maxi- mum likelihood phylogenetic tree (ML tree), by MEGA 7 program based on Kimura-Two parameters distance. The following analyses were performed to identify the SNPs which show association with geographical coordinates of Crocus species distribution. We should mention that these analytical approaches have different assumptions and may differ to some extent in their results. Therefore, comparing obtained results are important for drawing a solid conclusion. 2.2. Canonical correspondence analysis In the first approach we used CCA (Canonical cor- respondence analysis). This method is based on regres- sion of the SNPs and ecological features and uses an approach similar to principal components analysis (PCA), but it is utilized for discrete characteristics like SNPs (Podani, 2000; Sheidai et al., 2020). This method differs from PCA in the way that, PCA tries to maxi- mize the variance of data in a reduced space, while CCA tries to maximize the association of data (SNPS), to ecological features studied (Podani, 2000; Sheidai et al., 2020). CCA was performed in PAST ver. 4., program. 2.3. Latent Factor Mixed Model (LFMM) Latent factor mixed model is a method for testing associations between loci and environmental gradients using latent factor mixed models. LFMM implements an MCMC algorithm for regression analysis in which the confounding variables are modeled with unobserved (latent) factors. The program estimates correlations between environmental variables and allelic frequencies, and simultaneously infers the background levels of popu- lation structure (Frichot et al., 2013, Frichot and Francois, 2015). LFMM was performed by LFMM package in R. 4.2. 2.4. Redundency analysis (RDA) Redundancy analysis (RDA), a form of constrained ordination which is fit for genomic data sets, where we are interested in understanding how the multivariate environment shapes patterns of genomic composition across geographical areas. RDA is based on multivariate 161Identifying potential adaptive SNPs within combined DNA sequences in Genus Crocus Table 1. The accession numbers and chromosome number of taxa in for the genus Crocus and outgroup representatives. Number Taxa Accession number(ITS) Accession number(trnL-F) chromosome number Country 1 C. veneris HE801061.1 HE864222.1 2n= 16 cyprus 2 C. etruscus HG518187.1 HG518216.1 2n= 8 Italy 3 C. kosaninii HG518189.1 HG518206.1 2n= 14 Serbia 4 C. baytopiorum LS398370.1 LT991646.1 2n= 28 Turkey 5 C. scardicus HE663961.1 HE864166.1 2n= 36 Macedonia 6 C. versicolor HE801142.1 HE864249.1 2n= 26 Italy 7 C. malayi HE801170.1 HE864246.1 2n= 30 Croatia 8 C. imperati HE801131.1 HE864231.1 2n= 26 Italy 9 C. minimus HE801140.1 HE864247.1 2n= 24 Italy 10 C. corsicus HE801096.1 HE864241.1 2n= 18 Italy 11 C. cambessedesii HE801105.1 HE864228.1 2n= 16 Spain 12 C. nudiflorus HE801146.1 HE864253.1 2n= 48 Spain 13 C. serotinus HE801125.1 HE864225.1 2n= 22 Portugal 14 C. niveus HE801081.1 HE864219.1 2n= 28 Greece 15 C. goulimyi HE801130.1 HE864230.1 2n= 12 Greece 16 C. ligusticus HE801167.1 HE864234.1 2n= 24 Italy 17 C. kotschyanus HE664000.1 HE864256.1 2n= 8 Turkey 18 C. scharojanii HE801135.1 HG518229.1 2n= 8 Russia 19 C. vallicola HE801168.1 HE864238.1 2n= 8 Russia 20 C. gilanicus HE801172.1 HE864255.1 2n= 24 Iran 21 C. sativus HE801172.1 LT991682.1 2n= 24 Iran 22 C. pallasii sub sp. hausknechtii LS398387.1 LT991663.1 2n= 14 Iran 23 C. thomasii LS398411.1 LT991688.1 2n= 16 Italy 24 C. cartwrightianus LS398376 LT991648.1 2n= 16 Greece 25 C. moabiticus LS398392.1 LT991669.1 2n= 14 Jordan 26 C. oreocreticus LS398397.1 LT991674.1 2n= 16 Greece 27 C. asumaniae LS398366.1 LT991641.1 2n= 26 Turkey 28 C. mathewii HE801089.1 HE864217.1 2n= 70 Turkey 29 C. reticulatus LM993447.1 LM993633.1 2n= 10 Moldova 30 C. cvijicii LT222444.1 HE864276.1 2n= 18,20,22 Albania 31 C. dalmaticus HE801137.1 HE864242.1 2n= 24 Croatia 32 C. sieberi subsp. sieberi HE663966.1 HE864171.1 2n= 22 Greece 33 C. robertianus HE801134.1 HE864236.1 2n= 20 Greece 34 C. cancellatus subsp. pamphylicus HE801128.1 HE864229.1 2n= 12 Turkey 35 C. hermoneus HE801163.1 HE864268.1 2n= 12 Jordan 36 C. abantensis HE664019.1 HE864239.1 2n= 8,16 Turkey 37 C. angustifolius HE801136.1 LM993589.1 2n= 20 Russia 38 C. ancyrensis HE663987.1 LM993597.1 2n= 10 Turkey 39 C. gargaricus sub sp. gargaricus HE801138.1 HE864243.1 2n= 30 Turkey 40 C. sieheanus HE801157.1 HE864263.1 2n= 16 Turkey 41 C. rujanensis LT222441.1 HE864280.1 2n= 22 Serbia 42 C. biflorus sub sp. biflorus HE801121.1 HE864220.1 2n= 8 Italy 43 C. almehensis HE801162.1 HE864271.1 2n= 20 Iran 44 C. danfordiae HE664007.1 HE864201.1 2n= 8 Turkey 45 C. pestalozzae HE801141.1 HE864248.1 2n= 28 Turkey 46 C. cyprius HE663962.1 HE864168.1 2n= 10 Greece 47 C. hartmannianus HE801173.1 HE864264.1 2n= 20 Cyprus 48 C. leichtlinii LN864711.1 HE864277.1 2n= 20 Turkey 49 C. kerndorffiorum HE801159.1 HE864213.1 Turkey 162 Masoud Sheidai et al. regression, and models linear combinations of the envi- ronmental predictors that explain linear combinations of the SNPs. This method effectively identifies covering loci associated with the multivariate environmental features (Legendre and Legendre, 2012). Redundency analysis is a highly flexible framework, and produce answers on: 1- What environmental condi- tions cause genetic divergence among the studied taxa? and 2. What is the genetic basis of local adaptation to the environment? RDA identifies linear relationships among the response and predictor matrices; if non-lin- ear relationships are expected, other statistical frame- works may be more suitable. RDA was performed in Paleontological statistics (PAST) ver. 4, program. Mantel test was performed with 1000 times per- mutations as implemented in PAST ver. 4., program to study correlation between genetic distance and geo- graphical distance of the studied species. Phylogenetically important SNPs was determined by character mapping of 110 SNPs obtained based on parsimony criterion as performed in Mesquite 3.6 pro- gram. We performed Tajima’s D test (Tajima, 1989) to reveal if Crocus species DNA sequences evolved ran- domly (“neutrally”), or under a non-random process, including directional or balancing selection, demo- graphic expansion or contraction. Moreover, we also carried out the molecular clock test, to show if SNP changes occurred in accordance with a uniform clock rate model of evolution during Crocus genus speciation events. These tests were performed by MEGA 7 pro- gram. 3. RESULTS 3.1. The species genetic difference The preliminary analysis of combined sequences obtained after sequence alignments and curation, pro- duced a DNA segment of 110 base pair length. The average p dis of the studied species was 0.126. Based on Kimura 2-parameters, the studied taxa differed in genetic distance from 0. 01 to 0.30. The paired mis- match plot of nucleotide difference is presented in Fig. 1. This plot shows a normal distribution in genetic dif- ference of these species, which indicates that genetic divergence occurred in a continuous and steady mode in evolution of the genus Crocus L. Skyline-plot (Fig. 2), of the same species also revealed a smooth and con- tinuous species expansion in the genus Crocus, with two sudden changes in population demography and sequence change which are related to the speciation events in Iran Crocus taxa. Number Taxa Accession number(ITS) Accession number(trnL-F) chromosome number Country 50 C. nerimaniae HE663977.1 HE864181.1 2n= 10 Turkey 51 C. korolkowii HE801139.1 HE864244.1 2n= 20 Uzbekistan 52 C. michelsonii KY797650.1 HE864278.1 2n= 20 Iran 53 C. caspius HE801171.1 HE864266.1 2n= 24 Iran 54 C. alatavicus HE801116.1 HE864273.1 2n= 20 Uzbekistan 55 C. naqabensis LS398395.1 LT997016.1 2n= 14 Jordan 56 C. antalyensis HE664015.1 HE864209.1 2n= 8 Turkey 57 C. olivieri HE8011 HE864216.1 2n= 6 Turkey 58 C. candidus HE663981.1 HE864186.1 2n= 6 Turkey 59 60 C. hyemalis HE801060.1 HE864215.1 2n= 6 Plestin 61 C. aleppicus HE801175.1 HE864267.1 2n= 16 Jordan 62 C. veneris HE801062.1 HE864222.1 2n= 16 Cyprus 63 C. carpetanus HE801071.1 HE864265.1 2n= 64 Turkey 64 C. nevadensis HE663960.1 HE864170.1 2n= 28, 30 Spain 65 C. fleischeri HE663983.1 HE864188.1 2n= 20 Turkey 66 C. pulchellus HE801145.1 HE864252.1 2n= 12 Greece 67 C. laevigatus HE801166.1 HE864233.1 2n= 30 Greece 68 C. banaticus HE801147.1 HE864254.1 2n= 26 Romania 69 Romulea ramiflora HE664012.1 HE864206.1 2n= 36 Turkey 70 R. bulbocodium HE664012.1 HE864202.1 2n= 34,36,42 Turkey 163Identifying potential adaptive SNPs within combined DNA sequences in Genus Crocus 3.2. Genetic grouping of taxa ML (Maximum likelihood) phylogenetic tree of the studied Crocus species based on combined molecu- lar data set and the species geographical distribution, is presented in Fig. 3. We can place the studied species in three to four major clades. At the first glance, it is evi- dent that species with Mediterranean distribution and those of South-West Asia (Iran, Iraq, and Afghanistan), and the neighboring regions, comprise adjacent clades, while the species growing in Europe are showing closer genetic affinity. Genetic grouping of these species by Linear discrim- inating analysis (LDA), as performed in DAPC analysis is provided in Fig. 4. This plot also supports the presence of four genetic groups in the studied taa. The assignment test for the studied Crocus species based on DAPC anal- ysis identified the species with genetic affinity (Fig. 5). The species n1-n68, are scattered in four major genetic groups as revealed by different cluster colors. Linear discriminating analysis revealed that the first three discriminating analysis (DA) axes, comprise about 80% of total variation, and the first two axes have signif- icant contribution with high Fst value (Fig. 6). DA load- ing obtained revealed that SNPs 74, 75 have the highest Figure 1. Mismatch plot of nucleotide difference among Crocus species. Figure 2. Skyline-plot of Crocus species based on combined sequence data set. Figure 4. Genetic groups identified based on LDA analysis. Figure 3. ML phylogenetic tree of the studied Crocus species and their geographical distribution. (n1-n70, as in Table 1). Figure 5. Assignment plot of Crocus species based on DAPC analy- sis (Individuals from left to wright are n1 to n 68 of Table 1). 164 Masoud Sheidai et al. discriminating power in the first LDA axis, followed by SNPs 31, and 109, in the second axis. Similarly, SNP 53 has high discriminating power in the third DA axis. The following analyses were performed to identify the SNPs which show association with geographical coordinates of Crocus species distribution. We should mention that these analy tical approaches have dif- ferent assumptions and may differ to some extent in their results. Therefore, comparing obtained results are important for drawing a solid conclusion. 3.3. Canonical correspondence analysis CCA plot of Crocus species and 110 SNPs used is provided in Fig. 7. The analysis produced two CCA axes with Eigenvalue% of 99.97and 0.028, respectively. Dis- tribution of 110 SNPs used shows association between SNPS 31, 70, 71, 74, and 75, with latitude distribution of Crocus species of these three SNPS. viz. 31, 74, and 75, were identified as discriminating loci among Crocus taxa, by DAPC analysis. These SNPs have high associa- tion value as are placed in the first CCA axis. The SNPs 2, 93 and 94 of the second CCA axis, show a lower degree of association with longitude distribution of the studied Crocus taxa. From these results, we may con- clude that, genetic changes of Crocus species towards latitude distribution was accompanied to these SNPs, which probably were associated with some important adaptive genes during Crocus speciation. It becomes interesting when we plot the selected countries (geographical regions), by CCA (Fig. 8). We observe that countries like Iran, Russia, and Georgia, become separated from the other studied countries towards latitude. That means SNPs’ changes occurred in these regions. The Skyline plot presented before also revealed a sudden change in nucleotide substitution and population size in Iran. 3.4. Latent Factor Mixed Model (LFMM) Manhattan plot of LFMM analysis is presented in Fig. 9. It identified SNPs 2, 9, 63, and 79, showed a sig- nificant association with environmental features. Figure 8. CCA plot of geographical regions showing separation of countries towards altitude and longitude Crocus species. Figure 7. CCA plot of Crocus species showing association of few SNPs with geographical factors. Figure 6. F-statistics of LDA analysis showing significant contribu- tion of the first two axes in discriminating Crocus species. Figure 9. Manhattan plot of LFMM analysis identifying four SNPs associated with environmental features. 165Identifying potential adaptive SNPs within combined DNA sequences in Genus Crocus 3.5. Redundency analysis (RDA) Redundancy analysis (R DA) was performed to detect the roles of geographical variables in Crocus spe- cies genetic subdivision, as well as the relative contribu- tion of each variable to the population genetic structure. RDA plot is presented in Fig. 10. The SNPs 22, 71, 74, 75, 98, and 103, show association with latitude which occurs in RDA axis one with about 85% of total variance, fol- lowed by SNPs 9, 93 and 94, associated with longitude and second RDA axis with only 14% of total variance. Therefore, if we consider different association approaches utilized in this study, we can consider a few SNPs which are significantly associated with geographical factors studied. These SNPs occurred during species divergence within the genus Crocus. A negative Tajima’s D = -1.2, was obtained for the studied SNPs in Crocus species. This signifies an excess of low frequency polymorphisms relative to expecta- tion, indicating population size expansion after a bot- tleneck or a selective sweep, which result in reduction in genetic diversity and formation of adaptive genotypes (species), in different geographical areas. The molecular test showed that SNP changes within the genes Crocus did not occurred under uniform rate of evolution and different phylogenetic clades differed in their genetic changes. This results also agree with the earlier result of skyline plot showing a deep change in SNP substitution and population size change in Crocus species of Iran and neighboring regions. Mantel test performed after 1000 times permuta- tions, produced non-significant correlation between genetic distance and geographical distance of the stud- ied species (r = - 0.03, P = 0.7). This result indicates that nucleotide difference and change in Crocus taxa is not due to mere geographical distance and as indicated by different analyses reported here, genetic changes are. mainly associated with latitude distribution of these taxa. 3.6. Phylogenetically important SNPs Character mapping of the SNPs (Fig. 11), based on parsimony criterion, revealed that some of these SNPs are of phylogenetic importance as they differentiate almost a particular clade of the studied species. Interest- ing enough, SNPs 74 and 75. are also among these phy- logenetic important SNPs. These two SNPs were identi- fied as discriminating SNPs among Crocus species and also they are associated with latitude distribution of taxa, particularly Crocus species of Iran and neighboring areas. 4. DISCUSSION Speciation within the genus Crocus is complex. A combination of diploid and polyploidisation events as well as inter-specific hybridization have been postulated for Crocus genus evolution (Mosolygo-L et al., 2016). Com- plexity at the species level has been reported by Seberg and Petersen (2009), as these authors could not delineate Crocus species even by utilizing different barcoding mark- ers. However, some authors, could resolve Crocus species of Balkan (Mosolygo-L et al., 2016) and Iran (Sheidai et al., 2018), by using different molecular markers. Figure 10. RDA plot of Crocus species showing association of few SNPs with geographical factors. Figure 11. Character mapping of SNPs by parsimony criterion showing that these SNPs can differentiate different phylogenetic clades. 166 Masoud Sheidai et al. Recently, Mohebi et al. (2020), provided DNA bar- code of Chloroplast DNA (trnH-psbA) region, which differentiated saffron genotypes of Iran from the oth- er imported genotypes. Moreover, the same authors (unpublished data), provided some DNA barcode which illustrate genetic differentiation between Crocus taxa growing in different geographical regions and not for a particular Crocus species. Nine Crocus L. species have been reported from Per- sia and some adjacent areas (Wendelbo, 1977; Matine, 1978). Taxonomy of the genus is controversial as evi- denced by difficulties in Crocus species delineation. In spite of extensive efforts on the phylogenetic aspects of Crocus genus, there has been now report on ecological or geographical association of the genetic or DNA sequence changes with speciation events in this genus. The present study revealed that DNA nucleotides of both nuclear and chloroplast origin can efficiently differentiate some of the phylogenetic clades of Crocus taxa. Moreover, some of these sequences may be associated with geographical distribution of Crocus species. Some nucleotide seems to be tightly associated with latitudinal distribution of these taxa. Tajima’s test of these sequences produced a negative Tajima’s D, which indicates an excess of low frequency polymorphisms relative to a selective sweep and spe- ciation events (Tamura and Nei, 1993). We also observed almost a continuous and gradual nucleotide substitu- tion for most of the species growing in other parts of the world, but a sudden deep change in DNA sequences of Iran Crocus species, which may be related to geographical adaptation as also evidenced by CCA and RDA analyses. Different approaches used to identify the nucleotides associated with geographical variables, revealed some degree of difference. It is due the fact that CCA and RDA methods are based on linear association (regression), with different approaches, while LFMM method is a Bayesian- Model approach (Podani, 2000; Frichot and Francois, 2015). It seems therefore, using different approaches may improve understanding of associated SNPs with geo- graphical and ecological variables. Such combined data evaluations, give insights into contemporary processes, and may explain how environmental factors influence selective and neutral genomic diversity within and among related species or different geographical populations with- in a single species (Segovia et al., 2020). Presence of heterogenous environmental conditions are known to cause changes in genetic diversity of plant species and result in local adaptations even in the popu- lations of a single species (Segovia et al., 2020). Under- standing the genetic basis of local adaptation is one of the main concern of evolutionary biologists, as identifying adaptive genetic loci or SNPs improves our knowledge of the genetic mechanism of local adaptation and probably species diversification within a genus (Zhang et al., 2019). Recent studies which are concerned with genetics of local adaptations try to answer two major questions: 1- which environmental variables play key role in the adap- tive genetic divergence of a species or different species within a particular genus and shape its landscape genet- ic structure, and, 2-which genes or loci on the genome undergo adaptive genetic differentiation (Li et al., 2017, Zhang et al., 2019). In general, populations’ local adaptation which leads to speciation thing a genus is the act of natural selection in oppose to continuous gene flow. In plant groups such as Crocus genus in which species differen- tiation is vague due to inter-specific hybridization and a high degree of genetic affinity, local adaptation, may be expected to happen for a few genes or nucleotides, as also we demonstrated in this study. The latitude occur- rence of nucleotide changes and species diversification in Crocus genus, may be related to a warmer and drier environmental conditions of Iran, and Afghanistan and neighboring regions in compare to those prevailing in Mediterranean countries and Europe. In a similar study, Ingvarsson et al. (2006), charac- terized patterns of DNAsequence variation at the puta- tive candidate gene phyB2 in 4 populations of European aspen (Populous tremula) and scored single-nucleotide polymorphisms in an additional 12 populations collect- ed along a latitudinal gradient in Sweden. They utilized a sliding-window scan of phyB2 and identified six putative regions with enhanced population differentiation and four SNPs showed significant clinal variation. Therefore, they suggested that the clinal variation at individual SNPs is an adaptive response in phyB2 to local photo- periodic conditions. 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