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ISSN : 1225-8504(Print)
ISSN : 2287-8165(Online)
Journal of the Korean Society of International Agriculture Vol.24 No.5 pp.547-556

SSR 마커를 이용한 불가리아 고추 유전자원의 다양성 평가

오세종, 송재영, 이정란, 이기안, 고호철, T. Stoilova*, L. Krasteva*, 김연규, 이주희, 곽재균, 노나영, 허온숙, 이명철
농촌진흥청 국립농업과학원, *불가리아 식물유전자원연구소

Evaluation of Genetic Diversity of Red Pepper Landraces (Capsicum annuum L.) from Bulgaria Using SSR Markers

Myung-Chul Lee, Se-Jong Oh, Jae-Young Song, Jeongran Lee, Gi-An Lee, Ho-Cheol Ko, T. Stoilova*, L. Krasteva*, Yeon-Gyu Kim, Ju-Hee Rhee, Jae-Gyun Gwag, Na-Young Ro, On-Sook Hur
National Agrobiodiversity Center, National Academy of Agricultural Science, RDA
*Institute of Plant Genetic Resources


The amount of genetic variability of a species is essential for its survival and adaptation indifferent environments, and studies of genetic diversity using molecular markers are necessary to understandthe genetic structure of a population and to orientate effective strategies of germplasm conservation.The aim of current study was to determine the SSR markers that can be used rapidly and reliablyto evaluated the pepper of Bulgaria landraces, and applied the markers to assessment of introducegenetic diversity of the pepper germplasm. We used 22 polymorphic microsatellite markers to analysis ofgenetic diversity within 61 pepper collection of Bulgaria landraces germplasm, all SSR primers pairsproduced 82 polymorphic and reproducible amplification fragments. Results showed a relatively lowlevel of genetic diversity among pepper accessions from Bulgaria for both molecular and morphologicalmarkers. An average value of polymorphic information contents (PIC) were 0.334 with a range of 0.061to 0.63. The mean values of observed (HO) and gene diversity (GD) were 0.383 and 0.154, respectively,indicating a considerable amount of polymorphism within this collection. A genetic distance-based phylogenygrouped into three distinct groups according to genetic difference among the accessions. An averageday of flowering time was 56.9 days with a range of 48 to 65 days. The average of fruit length andwidth were 6.9cm with a range 2.6 to 13.9cm, and 1.9cm with a range 0.5 to 4.2cm, respectively.

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The genus Capsicum include 22 wild species and five domesticated species, The five domesticated species are C. annuum L., C. frutescens L., C. chinese Jacq., C. baccatum L., and C. pubescens R. &P (Eshbaugh, 1980). Pepper (Capsicum annuum L.) is one of the most important vegetable crops in both worldwide and South Korea. It includes all cultivated chili peppers, which are variously called red or hot pepper. Most of the present-day commercial cultivas are F1 hybrids of C. annuum L. species, because hybrid breeding of pepper has become popular during the past 30 years. The genetic diversity of landrace germplasm is considered as one of the criteria for selection of parents for pepper breeding, because of their competitive advantage. Accoring to Tanksley and McCouch (1997), the narrowing of the genetic base occurred firstly when changing the wild species into a domesticated species and secondly when landraces were replaced by modern cultivars. Landraces have the higher genetic variability among the different groups of germplasm, as well as the better environmental adaptation, and are an irreplaceable source of highly co-adapted genotypes (Lanteri and Barcaccia, 2006).

 The landraces of crop constituted, until recently, a dynamic and essential component of the overall agricultural biodiversity which has been valued almost exclusively as a source of traits that can be used in scientific breeding programs and to improve the productivity of new crop varieties. A comparison of morphological markers, such as flower and fruit morphologies in pepper is the simplest approach for the detection of genotypes and the assessment of genetic diversity. However, the outcrossing rate ranges from 7.6 to 38.6% under field conditions (Ballester and Vicente, 1998) and phenotypic evaluation is influenced by environment and might not distinguish between closely related genotypes (Geleta et al., 2005; Rodriguez et al., 1999). However, molecular DNA marker analyses have been beneficial by being large in number and not affected by the environment, more reproducible and objective than morphological analysis for the determination of genetic similarity among genotypes. For this reason, molecular markers are very useful in complementing the morphological characterization to describe the genetic diversity available in the center of diversification, and to allow the detection of additional sources of genetic variation useful for crop improvement (Gilbert et al., 1999).

 The genetic diversity among and within the Capsicum species has been investigated in several studies using DNA markers such as RFLP, RAPD, AFLP, and micro-satellites. (Paran et al., 1998; Rodriguez et al., 1999; Minamiyama et al., 2006; Lanteri et al., 2003; Rao et al., 2003; Toquica et al., 2003; Portis et al., 2004; Geleta et al., 2005; Ben-Chaim et al., 2001; Oyama et al., 2006; Kwon et al., 2005). With many other crop species, SSR markers are being widely used in pepper studies (Minamiyama et al., 2006; Kwon et al., 2005, Lee et al., 2004), because this method represent highly polymorphic, reproducible, co-dominant, and multiallelic types of variation for plant variety characterization or diversity assessment of germplasm (Becher et al., 2000).

 The Capsicum germplasm has a great diversity for morphological traits like fruit size, shape and color. This diversity offers opportunities to develop unique cultivars for agronomic applications. Knowledge of genetic variation and relationships among collected germplasm will be useful and effective tool to screen accessions and to identify superior parents in breeding programs. The objective of this study was to determine the genetic diversity of C. annuum collection from Bulgaria based on both morphological and SSR markers approaches.


Plant materials and DNA extraction

 Agronomic traits and molecular analyses were performed on 61 pepper (Capsicum annuum L.) accessions introduced from Bulgaria collections (Fig. 1). The list of germplasm accessions used in this study is given in Table 1. Genomic DNA was extracted from the young leaves of C. annuum using Plant DNAzol reagent (Invitrogen). The DNA concentration was determined using a UV–Vis spectrophotometer (ND-1000; NanoDrop, Wilmington, DE, USA). The DNA solution was then diluted to a working concentration with distilled water and stored at −20˚C until use.

Fig. 1. Locations of the 61 pepper landraces introduced from Bulgaria surveyed in the present study.

Table 1. List of 61 red pepper (Capsicum annuum L.) accessions introduced from Bulgaria.

Table 1. Continued .

Table 2. Means, standard deviations of nine quantitative characters for 61 red pepper accessions introduced from Bulgaria.

Assess of microsatellite markers

 Twenty-two previously published SSR primer pairs (Table 3) were used in this study. The M13F-tail PCR method of Schuelke (2000) was used to measure the size of PCR products. PCR amplification was carried out in a total volume of 20 ul containing 2 ul of genomic DNA (10 ng/ul), 0.2 ul of the specific primer (10 pmol/ul), 0.4 ul of M13 universal primer (10 pmol/ul), 0.6 ul of normal reverse primer, 2.0 ul of 10 × PCR buffer (Takara, Tokyo, Japan), 1.6 ul of dNTP (2.5 mM), and 0.2 ul of Taq polymerase (5 unit/ul; Takara). The reaction mixture was subjected to the following conditions: initial denaturation at 94˚C for 3 min, followed by 30 cycles of denaturation at 94˚C for 30 s, annealing at 52-55˚C for 45 s, then 15 cycles at 94˚C for 30 s, 53˚C for 45 s, and extension at 72˚C for 45 s and final extension at 72˚C for 10 min. PCR was carried out in PTC-220 thermocycler (MJ Research, Waltham, MA, USA). The PCR products were then run on an ABI PRISM 3130xl Genetic Analyzer according to the manufacturer’s instructions (Applied Biosystems, USA). Fragments were sized and scored into alleles using GeneMapper v4.0 software (Applied Biosystems, USA).

Table 3. Characterization of the 22 microsatellite loci among red pepper based on 61 germplasm accessions introduced from Bulgaria.

Data analyses of genetic diversity and population structure

 Population structure analysis and assign individuals to populations for the 61 red pepper accessions were performed based on the 22 SSR markers with STRUCTURE software (Pritchard et al., 2007) using a burn-in of 100,000, run length of 100,000 and a model allowing for admixture and correlated allele frequencies. The number of populations (K) was set from 1 to 10, with 3 independent runs each. We then estimated the number of subpopulations on the basis of the Evanno criterion, which are the most probable value of (K) corresponds to the peak in the (K), which is an ad-hoc statistic (K), assisted with L(K), L’(K) and L”(K) (Evanno et al., 2005). The (K) perceives the rate of change in log probability of the data with respect to the number of groups inferred by STRUCTURE.

 The total number of alleles, alleles frequency, gene diversity and polymorphism information content (PIC) per each SSR locus were calculated with the PowerMarker version 3.25 analysis (Liu and Muse, 2005). An unweighted pair group method with arithmetic mean (UPGMA) hierarchical clustering were performed based on the matrix of genetic similarity estimates according to procedures of the Power-Marker software. The tree to visualize the phylogenetic distribution of accessions was constructed using the software MEGA (Tamura et al., 2007).

 The data were analyzed using two multi variable analysis methods, principal coordinate analysis (PCoA) and PCA using Genalex program (Peakall and Smouse, 2006) and Multibase program (


Morphological characterization

Agronomic characters

 The Capsicum annuum L. were estimated to evaluate the agronomic characteristics of the 61 accessions. The days from sowing to germination, flowering, fruiting and maturity, and plant height, length of fruit, diameter of fruit, thickness of fruit, weight of fruit are given in Table 1, Fig. 2 and Fig. 3. An average day of germination, flowering, fruiting and maturing were 16.9, 56.9, 72.3 and 90.7 days with a range of 11 (A9E0445) to 26 (A9E0561), 48 (A9E0417)to 65 (A9E0494), 62 (A9E0417) to 81 (A9E0697) and 73 (A9E0417) to 98 (A9E0697) days, respectively (Table 1). For plant height, the accession A9E0446 (71.8 cm) was the tallest, whereas the accession A9E0566 was the smallest (24.5 cm), the pepper populations having tall plant height type (more than 61 cm, 34 accessions), moderate type (41-60 cm, 22 accessions) and short type (21-40 cm, 5 accessions) (Fig. 3(a)). For the four economic characters, such as fruit weight, fruit diameter, fruit length and fruit shape, most of accessions in fruit weight were surveyed between least 1 g and 20.9 g. For fruit length, the accession A9E0446 (13.9 cm) was the longest, whereas the accession A9E0446 (2.6 cm) was the shortest. All accessions except one accession (A9E0594) were recorded less than 4 cm in the fruit diameter, the average of diameter were 1.9 cm with a range 0.5 cm(A9E0696) to 4.2 cm (A9E0594) (Table 1 and Fig. 3).

Fig. 2. Frequency distributions of days to germination, flowering, fruiting and maturity in 61 pepper landraces introduced from Bulgaria.

Fig. 3. Frequency distributions of morphological traits in 61 pepper landraces introduced from Bulgaria.

Principal component analysis

 To observe the general pattern for variation of traits, pepper germplasm of Bulgaria was analyzed for principal components. The results of PCA conducted with eight quantitative characters for sixty-one pepper accessions were presented in Fig. 4. The first three PCs explained 13.5%, 12 % and 4 % of the variation, respectively for the quantitative variables. Days from sowing to flowering were relatively more important than other variables in constructing PC1 (Fig. 4(a)). The analysis indicated that the three groups, which are Blue (Early maturity), Green (Moderate maturity) and Red (Late maturity) accessions, could be easily separated. Overall, the Blue group was located on the left part of the PCA graph, while the Red group was on the right part. The remaining accessions of Green group were located on the middle part of the scatter plot of PCA (Fig. 4(b)). This separation was mostly caused by the differences in plant height, length and width of fruit among the accessions (Krasteva, 2000). Although the separation was not classified in this study (data not shown), the PCA graph was clearly separated by days to flowering even though some Blue and Green accessions were grouped together, and some Green and Red accessions were located closely.

Fig. 4. Principal component analysis on the six phenotypic traits of the 61pepper introduced from Bulgaria accessions. PC 1 and PC 2 refer to the first and second principal components, respectively.

Molecular analysis

Population structure and principal coordinate analysis

 A fundamental understanding of population structure provides effective management and conservation strategies for C. annuum germplasm management. To evaluate the genetic structure of the pepper entries, the 61 accessions were grouped using 22 microsatellite markers by software Structure 2.3.3. Using the standard established by Evanno et al. (2005), an optimum of ΔK was calculated for K = 3, which are showed that the entire pepper population was to divide into three groups. The 3 groups were defined as group I, group II, and group III. Based on the membership probabilities were greater than 0.80, sixty-one cultivars of C. annuum were divided into three populations. Group I was comprised twelve of all populations, 23 and 9 cultivars was contained to Group II and Group III, respectively. The remaining 17 cultivars were assigned into admixed group (Fig. 5).

Fig. 5. Population structure for 61 red pepper accessions introduced from Bulgaria. Bar plots for individual pepper accessions generated by STRUCTURE 2.2 using the admixture model with independent allele frequency model based on 22 SSR markers. The groups are represented by different colors. A horizontal bar is partitioned into segments indicating its genetic composition and the longer the segment, the more an accession resembles one of the groups. The labels below the bar plots indicate the regions of origin of accessions.

 Additionally, the separation of the populations was also estimated using principal coordinate analysis (PCoA). The first and second principal coordinates explained 22.98% and 19.98% are of the genetic variation (Fig. 6). The result indicated that the entire 61 accessions were distinctly categorized into three groups according to the clustering by Structure with K = 3. This result also augments the choice of a ΔK with K values of 3 because the PCoA analysis generated similar results.

Fig. 6. Principal coordinate analysis (PCoA) of the 61 red pepper accessions introduced from Bulgaria based on genetic distance estimates.

Profile of microsatellite markers

 The characteristics of the microsatellite loci used in the present study are summarized in Table 3. The 22 SSR markers detected 82 alleles among the 61 pepper accessions with an average of 3.727 alleles per locus. The number of allele per SSR marker locus (NA) ranged from 2 (AGi33, CAMS101, CAMS420, HpmSE31 and HpmSE143) to 5 (AGi86, AGi96, CAMS117, CAMS142, CAMS153, CAMS 398 and CAMS606). The CAMS606 marker produced 5 alleles that were the highest number of alleles of markers and the highest PIC value observed was 0.636. The frequency of major alleles (MAF) per locus varied from 0.418 (CAMS606) to 0.967 (AGi33) with an average allele frequency of 0.693. The values of observed heterozygosity (HO) and gene diversity (GD) ranged from 0.00 at AGi121 and HpmSE143 to 0.951 at CAMS153 (mean, 0.161) and from 0.063 at AGi33 to 0.692 at CAMS606 (mean, 0.400), respectively. Average values of polymorphic information contents (PIC) were 0.349 with ranged from 0.061 (AGi33) to 0.636 (CAMS606) (Table 2).

Genetic diversity and phylogenetic relationships

 A genetic distance-based analysis was performed by calculating the shared allele frequencies among 61 accessions and constructing an unweighted pair group mean average (UPGMA) dendrogram. The colors of dendrogram were used to section according to the results of groups by Structure analysis. All pepper accessions were shown about 43% diversity, but they were not distinctly placed in the phylogenetic tree, and showed clustering into three groups. Although the dendrogram showed the complex distribution pattern for pepper populations, the UPGMA cluster was similar to the results of model-based cluster analysis that correlated with grouping of the accessions (Fig. 7). As shown in Fig. 7, the accessions were colored according to the group pattern appeared by Structure.

Fig. 7. An UPGMA dendrogram constructed from a matrix of shared allele frequencies. Dendrogram showing genetic relationships among 61 red pepper accessions introduced from Bulgaria.


 The description of morphological trait variation is important in the initiation of programs aimed at the selection of genotypes having quantitative traits, such as high yield, product size and shape, acceptable to consumers. The assessment of genetic variability among genotypes is useful for the conservation of genetic resources, for broadening the genetic basis of cultivars, and for cultivar protection(Yuzbaþýoglu et al., 2006). This research was performed in order to characterize and study the diversity and relationships about the landrace pepper from Bulgaria and to provide information for the conservation and management of C. annuum germplasm resources. In this study, we used various agronomic traits and 22 SSR markers to characterize 61 Bulgaria pepper accessions. Principal component analysis (PCA) is useful in analyzing genetic variation among plant accessions and determining the most important variables contributing to variation (Shankar et al., 2009). In the morphological part, the most representative variables for describing the phenotypic diversity of the red pepper germplasm accessions were defined by PCA. Although pepper germplasm accessions of Bulgaria were not well separated by agronomic traits used for morphological diversity, the PCA graph was clearly separated by only days to flowering. In the molecular part, the clustering and relationships of UPGMA, PCoA and STRUCTURE obtained from the SSR distance matrix indicated a rather high variability and the degree of genetic similarity among all 61 pepper accession. Although these accessions indicated overlapping regions among all pepper landraces, the genetic diversity of pepper germplasm accessions might be considered high, and show distinct division between groups. However, the analysis of UPGMA, PCoA and Structure with SSR markers did not observed clear grouping of the accessions according to morphological traits. Nevertheless, certain distribution of pepper germplasm accessions was observed in the PCoA diagram and Structure dendrogram using SSR markers. This result agrees morphological and molecular diversity studies in C. maxima in the previous study by Ferriol et al. (2004). The association between genetic similarity and geographic distance among landraces is not always clear (Sonnante and Pignone, 2007). As this result, the landrace genotypes in our study did not demonstrate significant association between agronomic traits and genetic diversity determined using SSRs. Also, the studies by Altintas et al. (2008) have shown that the estimated genetic divergence from agronomical traits is not completely similar to AFLP-based results. The differences between phenotypic traits and molecular profiles are the result of different gene expression of functional genomics regions controlling various traits (Vollmann et al., 2005; Eivazi et al., 2008). Therefore, DNA markers data does not necessarily reflect agronomic traits (Metais et al., 2000).

 In conclusion, more molecular markers are needed to confirm the genetic diversity based on morphologic characters and to characterize theses landraces for more detailed examination. This may help to emphasize the availability of these genetic resources for future breeding programs.

적 요

 본 연구는 농업유전자원센터에 보존되어있는 불가리아 재래종 고추 유전자원 61 점을 대상으로 농업형질을 조사하고, 22개의 분자마커(SSR marker)를 이용하여 불가리아 재래종 고추 유전자원의 유전적 다양성 및 집단분석을 통하여, 자원보존 및 효율적인 작물 육종을 위한 기초정보를 제공하고자 본 실험을 수행하였으며 결과는 다음과 같다.

 1. 파종 후 발아까지 소요일수는 최소 11일에서 최대 26 일, 평균 16.9 일이었고 개화 소요일수는 최소 48일, 최대 65일, 평균 56.9일이었으며 성숙까지의 소요일수는 최소 73일, 최대 98일, 평균 90.7일이었다.
 2. 농업형질의 특성을 바탕으로 PCA 분석을 이용하여 불가리아 고추의 다양성을 분석한 결과, 파종 후 개화까지의 소요일수에 따라 조생종, 중생종, 만생종 3개의 그룹으로 나눌 수 있었다.
 3. 61점의 고추자원에 대하여 22개 SSR 마커에 의해 나타난 대립유전자 (allele)수는 총 82개였다. 마커당 평균 allele수는 3.7 개였고, allele 수의 범위는 2개에서 5개로 확인되었다. 유전적 다양성을 나타내는 PIC 값의 범위는 0.061-0.636이었으며 평균 PIC 값은 0.349로 확인되었다.
 4. 분자마커(SSR)를 이용하여 UPGMA, PCoA, STUCTURE 분석을 통한 고추의 다양성 및 집단 구조를 분석한 결과, 3개의 그룹으로 나뉘어졌다.

 결론적으로, 농업형질 특성을 바탕으로 한 불가리아 재래종고추의 다양성과 분자학적 특성을 이용한 다양성 결과와는 차이가 있었다.


 This work was supported by the Research Program for Agricultural Science & Technology Development (PJ007506).


1.Altintas, S., F. Toklu, S. Kafkas, B. Kilian, A. Brandolini and H. Ozkan. 2008. Estimating genetic diversity in durum and bread wheat cultivars from Turkey using AFLP and SAMPL markers. Plant Breeding. 127: 9-14.
2.Ballester, J. and M.C. de Vicente. 1998. Determination of F1 hybrid seed purity in pepper using PCR-based markers. Euphytica. 103: 223-226.
3.Becher, S.A., K. Steinmetz, K. Weising, S. Boury and D. Peltier. 2000. Microsatellites for cultivar identification in Pelargonium. Theor. Appl. Genet. 101: 643-651.
4.Ben-Chaim, A., R.C. Grube, M. Lapidot, M. Jahn and I. Paran. 2001. Identification of quantitative trait loci associated with resistance to cucumber mosaic virus in Capsicum annuum. Theor. Appl. Genet. 102: 1213-1220.
5.Eivazi, A.R., M.R. Naghavi, M. Hajhaydari, S.M. Piseyedi, M.R. Ghaffari, S.A. Mohammadi, I. Majidi, G.H. Salekdeh and M. Mardi. 2008. Assessing wheat (Triticum aestivum L.) genetic diversity using quality traits, amplified fragment length polymorphisms, simple sequence repeats and proteome analysis. Ann. Appl. Biol. 152: 81-91.
6.Eshbaugh, W.H. 1980. The taxonomy of the genus Capsicum (Solanaceae). Phytologia. 47: 153-165.
7.Evanno, G., S. Regnaut and J. Goudet. 2005. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14: 2611-2620
8.Ferriol, M., B. Picó and F Nuez. 2004. Morphological and Molecular Diversity of a Collection of Cucurbita maxima Landraces. J. Amer. Soc. Hort. Sci. 129(1): 60-69.
9.Geleta, L.F., M.T. Labuschagne and C.D. Viljoen. 2005. Genetic variability in pepper (Capsicum annuum L.) estimated by morphological data and amplified fragment length polymorphism markers. Biodivers. Conserv. 14: 2361-2375.
10.Gilbert, J.E., R.V. Lewis, M.J. Wilkinson and P.D.S. Caligari. 1999. Developing an appropriate strategy to assess genetic variability in plant germplasm collections. Theor. Appl. Genet. 98: 1125-1131.
11.Kwon, Y.S., J.M. Lee, G.B. Yi, S.I. Yi, K.M. Kim, E.H. Soh, K.-M. Bae, K.M. Park, I.H. Song and B.D. Kim. 2005. Use of SSR Markers to Complement Tests of Distinctiveness, Uniformity, and Stability (DUS) of Pepper (Capsicum annuum L.) Varieties. Mol. Cells. 19(3): 1-8.
12.Krasteva, L. 2000. Watermelon Genetic Resources in Bulgaria. Acta Horticult. 510: 253-256.
13.Lanteri, S., A. Acquadro, L. Quagliotti and E. Portis. 2003. RAPD and AFLP assessment of genetic variation in a landrace of pepper (Capsicum annuum L.), grown in North-west Italy. Genet. Res. Crop Evol. 50: 723-735.
14.Lanteri, S. and G. Barcaccia. 2006. Molecular marker based analysis for crop germplasm preservation. In: Ruane, J.; Sonnino, A. (Ed.). The role of biotechnology in exploring and protecting agricultural genetic resources. Rome: FAO, p. 55-66.
15.Lee, J.M., S.H. Nahm, Y.M. Kim and B.D. Kim. 2004. Characterization and molecular genetic mapping of microsatellite loci in pepper. Theor. Appl. Genet. 108: 619-627.
16.Liu, K. and S.V. Muse. 2005. PowerMarker: an integrated analysis environment for genetic marker analysis. Bioinformatics. 21: 2128-2129.
17.Métais, I., C. Aubry, B. Hamon and R. Jalouzot. 2000. Description and analysis of genetic diversity between commercial bean lines (Phaseolus vulgaris L.). Theor. Appl. Genet. 101: 1207-1214.
18.Minamiyama, Y., M. Tsuro. And M. Hirai. 2006. An SSR-based linkage map of Capsicum annuum. Mol. Breed. 18: 157-169.
19.Oyama, K., S. Hernandez-Verdugo, C. Sanchez, A. Gonzales-Rodriguez, P. Sanchez-Pena, J. Garson-Tiznado and A. Casas. 2006. Genetic Structure of wild and domesticated populations of Capsicum annuum (Solanaceae) from northwestern Mexico analyzed by RAPDs. Genet. Res. Crop Evol. 53: 553-562.
20.Paran, I., E. Aftergoot. and C. Shifriss. 1998. Variation in Capsicum annuum revealed by RAPD and AFLP markers. Euphytica. 99: 167-173.
21.Peakall, R. and P.E. Smouse. 2006. GENALEX 6: genetic analysis in Excel Population genetic software for teaching and research. Mol. Ecol. Notes. 6: 288-295.
22.Portis, E., A. Acquadro, C. Comino. and S. Lanteri. 2004. Effect of farmer's seed selection on genetic variation of landrace population of pepper (Capsicum annuum L.), grown in North-west Italy. Genet. Res. Crop Evol. 51: 581-590.
23.Pritchard, J.K., X. Wen and D. Falush. 2007. Documentation for structure software: Version 2.2. Department of Human Genetics, University of Chicago; Department of Statistics, University of Oxford. Available at
24.Rao, G.U., A. Ben-Chaim, Y. Borovsky and I. Paran. 2003. Mapping of yield related QTLs in pepper in an interspecific cross of Capsicum annuum and C. frutescens. Theor. Appl. Genet. 106: 1457-1466.
25.Rodriguez, J.M., T. Berke, L. Engle and J. Nienhuis. 1999. Variation among and within Capsicum species revealed by RAPD markers. Theor. Appl. Genet. 99: 147-156.
26.Schuelke, M. 2000. An economic method for the fluorescent labeling of PCR fragments. Nat Biotechnol. 18: 233-234.
27.Shankar, R., B.G. Bagle and T.A. More. 2009. Diversity analysis of bitter gourd (Momordica charantia L.) germplasm from tribal belts of India. Asian & Australasian J. Plant Sci. Biotech. pp. 21-25.
28.Sonnante, G. and D. PIGNONE. 2007. The major Italian landraces of lentil (Lens culinaris Medik.): their molecular diversity and possible origin. Genet. Resour. Crop Evol. 54: 1023-1031.
29.Tamura, K., J. Dudley, M. Nei and S. Kumar. 2007. MEGA4: molecular evolutionary genetics analysis (MEGA) software version 4.0. Mol. Biol. Evol. 24: 1596-1599.
30.Tanksley, S.D. and S.R. McCouch. 1997. Seed banks and molecular maps: unlocking genetic potential from the wild. Science. 277: 1063-1066.
31.Toquica, S.P., F. Rodriquez, E. Martinez, M.C. Duque and J. Tohme. 2003. Molecular characterization by AFLPs of Capsicum germplasm from the Amazon Department in Columbia, characterization by AFLPs of Capsicum. Genet. Res. Crop Evol. 50: 639-647.
32.YÜZBAIOÐLU, E., S. ÖZCAN and L. AÇIK. 2006. Analysis of genetic relationships among Turkish cultivars and breeding lines of Lens culinatis Mestile using RAPD markers. Genet. Resour. Crop Evol. 53: 507-514.
33.Vollmann, J., H. Grausgruber, G. Stift, V. Dryzhyruk and T. Lelley. 2005. Genetic diversity in camelina germplasm as revealed by seed quality characteristics and RAPD polymorphism. Plant Breeding. 124: 446-453.