Mol Biol Rep DOI 10.1007/s11033-010-0547-7
High efficiency and reliability of inter-simple sequence repeats (ISSR) markers for evaluation of genetic diversity in Brazilian cultivated Jatropha curcas L. accessions Clı´cia Grativol • Catarina da Fonseca Lira-Medeiros Adriana Silva Hemerly • Paulo Cavalcanti Gomes Ferreira
•
Received: 1 July 2010 / Accepted: 17 November 2010 Ó Springer Science+Business Media B.V. 2010
Abstract Jatropha curcas L. is found in all tropical regions and has garnered lot of attention for its potential as a source of biodiesel. As J. curcas is a plant that is still in the process of being domesticated, interest in improving its agronomic traits has increased in an attempt to select more productive varieties, aiming at sustainable utilization of this plant for biodiesel production. Therefore, the study of genetic diversity in different accessions of J. curcas in Brazil constitutes a necessary first step in genetic programs designed to improve this species. In this study we have used ISSR markers to assess the genetic variability of 332 accessions from eight states in Brazil that produce J. curcas seeds for commercialization. Seven ISSR primers amplified a total of 21,253 bands, of which 19,472 bands (91%) showed polymorphism. Among the polymorphic bands 275 rare bands were identified (present in fewer than 15% of the accessions). Polymorphic information content (PIC), marker index (MI) and resolving power (RP) averaged 0.26, 17.86 and 19.87 per primer, respectively, showing the high efficiency and reliability of the markers used. ISSR markers analyses as number of polymorphic loci, genetic diversity and accession relationships through UPGMA-phenogram and MDS showed that Brazilian accessions are closely related but have a higher level of
C. Grativol A. S. Hemerly P. C. G. Ferreira (&) Laborato´rio de Biologia Molecular de Plantas, Instituto de Bioquı´mica Me´dica, Universidade Federal do Rio de Janeiro Avenida Brigadeiro Trompowski, CCS Bl.H, 21941-590 Rio de Janeiro, RJ, Brazil e-mail:
[email protected] C. da Fonseca Lira-Medeiros Diretoria de Pesquisa Cientı´fica, Instituto de Pesquisas Jardim Botaˆnico do Rio de Janeiro, Rua Pacheco Lea˜o 915, 22460-030 Rio de Janeiro, RJ, Brazil
genetic diversity than accessions from other countries, and the accessions from Natal (RN) are the most diverse, having high value as a source of genetic diversity for breeding programs of J. curcas in the world. Keywords Jatropha curcas ISSR Genetic diversity Molecular makers
Introduction Jatropha curcas L. is a perennial shrub that belongs to the large Euphorbiaceae family. The origin of the genus Jatropha remains controversial, but various sources have identified South/Central America as a center of origin [1–4]. For many years the Portuguese transported this specie to the Cape Verde Islands and Guinea, whence it spread throughout Asia and Africa [5]. Nowadays, J. curcas is found in all tropical regions and has gained lot of attention for its potential in biodiesel production [6]. According to Fairless [1], J. curcas has one of the highest yields in liters of oil per hectare (1,300 l), second only to oil palm (Elaeis oleifera), with a yield of 2,400 l per hectare. Moreover, the oil extracted from J. curcas is of excellent quality [2]. In addition to producing high quality oil in large amounts, J. curcas has many other important characteristics, such as: (a) growing readily under saline conditions; (b) drought tolerance; (c) low nutrient requirements; (d) high potential in medicine, pharmaceutical and bio-pesticide applications; and (e) no competition with food production (Kumar and Sharma [7]; Achten et al. [2]; Becker and Makkar [3]; Makkar et al. [8]; Jongschaap et al. [9]). The domestication of J. curcas is necessary in order to fully develop its culture in Brazil, once there are still limitations to its cultivation such as lack of knowledge of agricultural practices; absence
123
Mol Biol Rep
of established cultivars (genetic variability unknown); inefficient management of pests and diseases; asynchronous maturity that leads to a requirement for continuous harvesting over 4–6 months; and occurrence of toxic organic remains containing phorbol esters, curcin and allergens (Fairless [1]; Makkar et al. [8]). In Brazil, the planting of J. curcas was initially promoted in the Northeast, where there are reports that this plant is well adapted to local climate conditions, since it has been planted for some time in this region [10]. In recent years, J. curcas has been grown and marketed in several regions of Brazil. However, there have been few efforts at breeding, and the commercialized seeds are bought from small local producers. For the establishment of J. curcas as a commercially viable crop in Brazil it is crucial to determine the genetic variability of the available accessions in order to introduce these plants into breeding programs designed to obtain elite varieties, capable of sustaining high crop production in different agro-climatic zones [11]. In other countries, especially India, recent studies have assessed the genetic diversity of J. curcas using different molecular markers, RAPD markers (Gupta et al. [12]; Ram et al. [13]; Pamidiamarri et al. [14]; Ikbal et al. [15]), ISSR (Basha, et al. [4]; Basha and Sujatha, [16], Kumar et al. [17]), AFLP (Tatikonda et al. [18]), SSR (Sun et al. [19]) and SPAR (Ranade et al. [11]). The majority of these studies revealed low genetic diversity among accessions from the same country. Despite the large array of molecular markers available to estimate genetic diversity in J. curcas, dominant molecular markers such as AFLP and ISSR have been used more frequently due to their ability to detect high levels of polymorphism [20]. Described by Zietkiewicz et al. [21], ISSR amplification was also used to evaluate the genetic diversity in J. curcas [4, 12, 16, 17]. As the choice of molecular-marker techniques depends on the genetic information they can detect, it is important to know whether
Table 1 Accessions code, collection sites, samples size and sites coordinates of J. curcas accessions examined in the ISSR analysis
123
the molecular marker technique chosen is the appropriate tool for estimating genetic variability in a reliable way [22]. In this study we describe the use of ISSR markers to evaluate the extent of genetic relationships and richness among 332 commercialized accessions of J. curcas in Brazil, in order to identify target accessions to use in genetic improvement of J. curcas. In addition, for a critical assessment of the potential of ISSR markers to provide reliable estimates of genetic diversity of J. curcas in Brazil, parameters including polymorphic information content (PIC), marker index (MI) and resolving power (RP), were analyzed. Our study shows that the ISSR markers used were highly efficient and suitable for diversity analysis of J. curcas in Brazil, generating sufficient polymorphic data to identify the accessions of greatest diversity for future genetic improvement of this biofuel plant.
Materials and methods Plant materials A set of 332 accessions of J. curcas from eight states in Brazil was selected for analysis of genetic diversity and it is listed in Table 1. This set consisted predominantly of accessions obtained commercially. Seeds were obtained from eleven J. curcas farmers located in the cities of Ariquemes (RO), Sa˜o Paulo (SP), Arac¸atuba (SP), Buritama (SP), Janau´ba (MG), Dom Bosco (MG), Rio Verde (GO), Eldorado (MS), Sorriso (MT), Natal (RN) and Arcoverde (PE) (Fig. 1). From Janau´ba (MG) we acquired seeds of possibly five varieties (Janau´ba A; Janau´ba B; Janau´ba N; Janau´ba J; Janau´ba U), which were analyzed as different accessions. Together with the accessions from Arcoverde (PE), we received seeds of unknown origin that were analyzed separately.
Accession code
Collection site
State (Abbrev.)
Sample size
Site coordinates
ARI
Ariquemes
21
63°140 S 9°500 W
ARA
21
50°300 S 21°120 W
SP
Arac¸atuba Sa˜o Paulo
Rondoˆnia (RO) Sa˜o Paulo (SP) Sa˜o Paulo (SP)
21
46°440 S 23°260 W
SO
Sorriso
21
55°440 S 12°290 W
RV
Rio Verde
Mato Grosso (MT) Goia´s (GO)
21
50°560 S 17°440 W
DB
Dom Bosco Janau´ba
Minas Gerais (MG)
20
46°180 S 16°310 W
20 21
43°100 S 15°430 W 50°250 S 23°470 W
21
50°300 S 21°070 W
JA/JN/JB/JU/JJ EL
Eldorado
BU
Buritama
Minas Gerais (MG) Mato Grosso do Sul (MS) Sa˜o Paulo (SP)
NAT
Natal
Rio Grande do Norte (RN)
21
35°250 S 5°480 W
ARC
Arcoverde
Pernanbuco (PE)
20
37°070 S 8°180 W
UN
Unknown origin
–
21
–
Mol Biol Rep Fig. 1 Map showing the cities in eight states of Brazil where J. curcas seeds were acquired. Cities are marked with a black circle and numbered from 1 to 11
Seeds from each locality were germinated directly in gerboxes with 100 g vermiculite and distilled water, after scarification and sterilization. The gerboxes were maintained in an oscillating temperature of 20–30°C, with a photoperiod of 10 h of light provided by cold white lamps. Leaves of 2-week-old plantlets from each locality were collected and dried in silica gel for DNA extraction.
DNA extraction Total genomic DNA was extracted from leaves using the CTAB method [23] with minor modifications. Two-hundred milligrams of dried leaf tissue were macerated in the Retsch MM 301 Mixer Mill for 2 min at 30 Hz. To the powder, 1 mL of CTAB buffer (0.1 M Tris–HCl pH 8.0, 1.4 M NaCl, 2% CTAB, 0.02 M EDTA pH 8.0, 1% PVP 40.000) and 20 ll b-mercaptoethanol (2%) was added and the samples were immediately stirred using a vortex mixer. Following incubation at 65°C for 1 h, the samples were extracted with 700 ll of chloroform: isoamyl alcohol (24:1) followed by homogenization by inversion for 10 min and centrifugation at 12,0009g for 15 min. DNA precipitation was carried out with the addition of 400 ll isopropanol, agitation by inversion and incubation for 30 min at -20°C, followed by centrifugation at 12,0009g for 15 min. The precipitate was washed with 500 ll of 70% ethanol and centrifuged at 12,0009g for 10 min. The supernatant was discarded and the pellet was resuspended in 150 ll of 10 mM Tris–HCl pH 8.0. The samples were incubated at 37°C for 2 h with 100 lg/ml RNase A to eliminate RNA contamination. The quality and quantity of DNA were
estimated electrophoretically using k DNA as standard, and the DNA was stored at -20°C. ISSR procedure Thirty-two ISSR primers (UBC primer set no. 9, University of British Columbia, Canada) were tested with J. curcas DNAs. Seven ISSR primers were selected based on polymorphism and robustness of the bands obtained (Table 2). The PCR reactions were performed in a total volume of 20 ll containing 10 ng DNA, 50 mM KCl, 10 mM Tris– HCl (pH 8.0), 1.5 mM MgCl2, 0.2 mM dNTP (PromegaÒ, USA), 1U Taq DNA Polymerase (New England BiolabsÒ) and 200 nM ISSR primer. The amplifications were carried out using 96-well plates on a thermocycler, with initial denaturation at 94°C for 5 min followed by 18 touchdown Table 2 ISSR primer numbers of the UBC set and sequences of each primer used for ISSR analysis of J. curcas accessions in Brazil ISSR primer
Sequence
807
50 AGA GAG AGA GAG AGA GT 30
834
50 AGA GAG AGA GAG AGA GYaT 30
836
50 AGA GAG AGA GAG AGA GYaA 30
848
50 CAC ACA CAC ACA CAC ARbG 30
856
50 ACA CAC ACA CAC ACA CYaA 30
873
50 GAC AGA CAG ACA GAC A 30
889
50 DcBdDc ACA CAC ACA CAC AC 30
a
C or T
b
A or G
c
A, G or T
d
C, G or T
123
Mol Biol Rep
cycles with steps of 30 s at 94°C; 30 s annealing temperature, starting at 54°C and decreasing 0.5°C per cycle; and 1 min at 72°C. The program continued for another 25 cycles with annealing temperature fixed at 45°C. Amplified products were electrophoresed in 0.59 TBE at 300 V using 2.5% agarose gel with a 200-bp ladder as standard (PromegaÒ, USA). Data scoring and analysis ISSR bands were scored as present (1) or absent (0) in agarose gels and entered into a binary matrix representing the ISSR profile of each accession. Initially, the potential of ISSR markers for estimating genetic variability of J. curcas was examined by measuring the marker informativeness through the counting of bands. The bands were counted as: total number of amplified bands (TNB); number of polymorphic bands (NPB), i.e. bands that were not amplified in all accessions; number of monomorphic bands (NMB), i.e. bands amplified in all accessions; number of rare bands (NRB), i.e. bands amplified in fewer than 15% of accessions; number of shared bands (NSB), i.e. bands amplified in up to 70% of accessions; and number of similar bands (NSiB), i.e. bands amplified in more than 70% of accessions. The classification of bands was made according to Tatikonda et al. [18]. To analyze the suitability of ISSR markers to evaluate genetic profiles of J. curcas, the performance of the markers was measured using three parameters: polymorphic information content (PIC), marker index (MI) and resolving power (RP). The PIC value for each locus was calculated as proposed by Roldan-Ruiz et al. [22]: PICi ¼ 2fi ð1 fi Þ; where PICi is the polymorphic information content of the locus i, fi is the frequency of the amplified fragments (band present) and 1 - fi is the frequency of non-amplified fragments (band absent). The frequency was calculated as the ratio between the number of amplified bands at each locus and the total number of accessions (excluding missing data). The PIC of each primer was calculated using the average PIC value from all loci of each primer. The marker index (MI) was calculated as described by Varshney et al. [20]: MI ¼ EMR PIC; EMRðEffective multiplex ratioÞ ¼ n b; where n is the average number of bands amplified by accession to a specific system marker (multiplex ratio) and b is estimated from the number of polymorphic loci (np)
123
and the number of non-polymorphic loci (nnp), b = np/ (np ? nnp). The resolving power (RP) of each primer was calculated according to Prevost & Wilkinson [24]: PR ¼ RIb ; where Ib represents the informative fragments. The Ib can be represented on a scale of 0-1 by the following formula: Ib ¼ 1 ½2 ð0:5 pÞ; where p is the proportion of accessions containing the band. A binary matrix was used to calculate the percentage of polymorphic loci, Nei’s genetic diversity [25], Shannon’s Information Index [25] and Nei’s unbiased measures of genetic distance [26], utilizing the program PopGene 3.2 [27]. The Nei’s genetic distance matrix was subjected to cluster analysis by the hierarchical clustering method UPGMA (Unweighted pair group method with arithmetic average) and a phenogram of genetic relationships among accessions from each location was generated by the program NTSYS-pc 2.11 software package [28]. The degree of genetic relationship among accessions was also assessed by the method of multivariate analysis, using multidimensional scaling (MDS). The binary matrix was also used to estimate the partitioning of genetic variance using AMOVA [29] on the Arlequin 3.1 program (Excoffier [30]). The AMOVA analysis was performed for each locus separately according to Excoffier [30]. The genetic variance components analyzed were the average variance among groups, the average variance among J. curcas sites within groups and the average variance within J. curcas sites. The degrees of freedom were calculated and showed for each genetic variance components, except within sites that presented variation on degrees of freedom in each locus analyzed. The U statistics values were generated by AMOVA and a non-parametric permutation test was made to estimate the significance level of variance components with random 1000 permutations. The linear correlation coefficient (Pearson’s r), the significant divergences (Student’s unpaired t-test) and the multivariate analysis (Multidimensional Scaling) were performed using the XLSTAT Pro 7.5, Microsoft Office ExcelÓ, add-in.
Results To investigate the genetic variability and genetic relationships of J.curcas commercialized accessions in Brazil, a set of ISSR primers was used to screen 332 cultivated accessions from twelve locations in the states of Pernambuco, Rio Grande do Norte, Minas Gerais, Sa˜o Paulo, Mato Grosso, Mato Grosso do Sul, Goia´s and Rondoˆnia. First,
Mol Biol Rep Table 3 Total number of bands, monomorphic bands, polymorphic bands, rare bands, shared bands and similar bands amplified by seven ISSR primers in J. curcas accessions ISSR primer
TNBa
807
2,873
834 836
NMBb
NPBc
% Polymorphism
NRBd
NSBe
NSiBf
913
1,960
0
2,873
100
0
3,174
0
3,174
100
41
497
2,636
3,636
812
2,824
77
0
637
2,187
848
3,210
324
2,886
89
121
812
1,953
856
2,877
318
2,559
88
61
1,090
1,408
873
2,559
0
2,559
100
27
518
2,014
2,924 21,253
327 1,781
2,597 19,472
88 91
25 275
812 5,279
1,760 13,918 1,408
889 Total Minimum
2,559
0
2,559
77
0
497
Maximum
3,636
812
3,174
100
121
1,090
2,636
Average
3,036
254
2,781
92
39
754
1,988
a
Total number of bands,
b
monomorphic bands, c polymorphic bands,
d
rare bands, e shared bands, f similar bands
Table 4 Marker parameters calculated for each ISSR primer used with J. curcas ISSR primer
PICa
EMRb
MIc
RPd
807
0.31
64
19.95
18.09
834
0.23
64
15.07
22.12
836
0.19
77
15.21
25.69
848
0.28
68
19.22
20.57
856
0.30
68
20.75
18.48
873
0.25
64
16.07
15.96
889 Minimum
0.27 0.19
69 64
18.75 15.07
18.24 15.96
Maximum
0.31
77
20.75
25.69
Average
0.26
67
17.86
19.87
a
Polymorphism information content
b
Effective multiplex ratio,
c
Marker index
d
Resolving power
we verified whether the ISSR primers used were suitable molecular tools for estimating genetic variability of this material. Two main aspects were evaluated: marker informativeness (polymorphism and overall efficiency of informative band detection) (Table 3) and marker performance (overall efficacy of primer set used in determining polymorphism level, genetic diversity and discriminatory power) (Table 4). Marker infomativeness A set of seven ISSR primers generated 104 loci with a total of 21,253 bands (TNB) amplified in all accessions, of
Fig. 2 Example of electrophoresis gel of DNA obtained by amplification of accessions using the ISSR primer 848. The column designated by the MW is the standard molecular weight (200 bp ladder) and the numbered columns are the accessions from Ariquemes (RO)
which 19,472 (91%) were polymorphic (NPB) and 1,781 (8%) were monomorphic bands (NMB) (Table 2). ISSR profiles of a representative gel of primer 848 amplification are shown in Fig. 2. The mean number of polymorphic bands per primer was 2,781. The percentage of polymorphism ranged from 77% for ISSR primer 836 to 100% for ISSR primers 807, 834 and 873, with an average of 92% polymorphism per primer. The frequency of polymorphic bands at each locus showed that the 30% of loci presented a large number of polymorphic bands (frequency class of 0.90–0.99) (Fig. 3). Polymorphic bands of the ISSR primers reported in fewer than 15% of the accessions were considered rare bands (NRB). A total of 275 rare bands were observed in accessions of J. curcas with an average of 39 bands per
123
Mol Biol Rep
Fig. 3 Frequency distribution of polymorphic ISSR bands with respect to the number of polymorphic bands amplified by ISSR primers in the accessions of J. curcas. The frequencies were classified from 0.0 to 1.0. The numbers in parentheses represent the percentage of all polymorphic loci that fall within each class of frequency of polymorphic bands
ISSR primer (Table 2). The ISSR primer 848 generated the largest number of rare bands (121), while rare bands for the ISSR primers 807 and 836 were not observed. A survey of rare bands across all accessions revealed higher and lower numbers of rare bands in the accessions from Natal (RN) and from Janau´ba JA (MG), respectively. All localities showed rare bands. Polymorphic bands observed at a given locus in up to 70% of accessions were considered as shared bands (NSB). A total of 5,279 shared bands were observed in accessions with an average of 754 shared bands per ISSR primer (Table 2). The maximum number of shared bands was 1,090, amplified by ISSR primer 856, and the minimum was 497, amplified by ISSR primer 834. The percentage of shared bands analyzed among accessions ranged from 17.29 to 68.45%. Polymorphic ISSR bands present at a given locus in more than 70% of accessions were considered similar bands (NSiB). A total of 13,918 similar bands were observed in accessions with an average of 1,988.2 similar bands per primer (Table 2). The ISSR primers 834 and 856 showed the highest and lowest number of similar bands with 2,636 and 1,408, respectively.
To determine PIC values of each ISSR primer we analyzed the mean of PIC values for all loci of each ISSR primer. As result, we obtained high values of PIC for the ISSR primers 807 (0.31) and 856 (0.30) and a low PIC value for the ISSR primer 836 (0.19) (Table 4). The average value of PIC per primer was 0.26. Comparison of the frequency of polymorphic bands at each locus with the average PIC value of each locus showed that a greater number of polymorphic bands were associated with lower values of PIC. Bands with a frequency from 0.4 to 0.6 had higher polymorphic information content (average PIC was 0.49), than those with a frequency of 0.6–0.7 (average PIC was 0.45) (Fig. 4). When the PIC values were compared with the number of polymorphic loci we observed that 23 of 98 polymorphic loci had PIC values above 0.45, being highly informative (Fig. 5), while 15 polymorphic loci had low levels of PIC (\0.05). The other 60 polymorphic loci had PIC values distributed between 0.05 and 0.45. The PIC values for all polymorphic loci ranged from 0.006 to 0.499. With a large number of fragments detected by primers, the ISSR marker system has a high multiplex ratio (n = 64.0). The ISSR effective multiplex ratio depends on the fraction of polymorphic fragments (b). In this study, the highest effective multiplex ratio (EMR = 77) was observed with the ISSR primer 836, with a mean EMR of 67 per primer (Table 4). To determine the general usefulness of the system of markers used, we calculated the MI (marker index) for each ISSR primer (Table 4). The highest MI was observed with the ISSR primer 856 (20.75) and lowest in the ISSR primer 834 (15.07). There was a positive correlation between the values of MI and PIC (r2 = 0.909, P \ 0.05). The resolving power (RP) is a parameter that indicates the discriminatory potential of the primers chosen. The
Marker performance The information on the genetic profile of each accession obtained using the seven ISSR primers was used to assess the marker performance through evaluation of three parameters: polymorphic information content (PIC), marker index (MI) and resolution power (RP).
123
Fig. 4 Average polymorphic information content (PIC) compared to frequency classes of polymorphic bands amplified by ISSR primers in the accessions of J. curcas
Mol Biol Rep
Fig. 5 Average polymorphic information content (PIC) values for number of polymorphic loci obtained by ISSR primers in the accessions of J. curcas
average RP to was 19.87 per ISSR primer (Table 4). The highest RP value was observed with the ISSR primer 836 (25.69) and the lowest with the ISSR primer 873 (15.96). There was no significant correlation (P [ 0.05) between the values of RP and MI. Genetic variability of J. curcas accessions The bands obtained from amplification of ISSR primers were used to assess the genetic variability of J. curcas Table 5 Accession group analysis of J. curcas in Brazil, showing the number and percentage of polymorphic loci, Nei’s genetic diversity (H) and Shannon Information Index (I)
a
S.D. standard deviation
accessions through percentage of polymorphic loci, Nei’s genetic diversity and Shannon Information Index analysis. The ISSR amplification of 332 accessions from 12 locations identified 104 total ISSR loci, of which 94% were polymorphic. The proportion of polymorphic and monomorphic loci varied with the locality (Table 5). The accessions from Sa˜o Paulo (SP), Arac¸atuba (SP) and Sorriso (MT) showed low percentage of polymorphic loci (17, 18 and 18% respectively). In contrast, the accessions from Natal (RN) and those of unknown origin had the highest percentage of polymorphic loci, with 43 and 39%, respectively. The percentages of polymorphic loci in the accessions of the remaining localities are showed on Table 5. The Nei’s genetic diversity index and the Shannon Information Index calculated for each of the 104 ISSR loci and for each location showed that accessions from Sorriso (MT) had the lowest genetic diversity (are: ±S.D. of 0.0498 ± 0.1242 and 0.0782 ± 0.1842 for Nei’s and Shannon’s diversity index, respectively) and the accessions of unknown origin had the highest genetic diversity (0.1669 ± 0.2094 and 0.2450 ± 0.2975 for Nei’s and Shannon’s diversity index, respectively) (Table 5). The Nei’s genetic diversity index and the Shannon Information Index of accessions of the remaining localities are showed on Table 5. The accessions with the highest values of Nei’s and the Shannon index were those from Natal (RN) and of unknown origin (0.1593 and 0.2335, 0.1669 and 0.2450, respectively). The t-test showed that the values of Nei’s
Accession group
Number of polymorphic loci
% Polymorphic loci
Mean H (S.D.)a
Mean I (S.D.)a
Ariquemes (RO)
21
20
0.0664 (0.1522)
0.0995 (0.2188)
Arac¸atuba (SP) Sa˜o Paulo (SP)
19
18
0.0508 (0.1288)
0.0785 (0.1895)
18
17
0.0601 (0.1408)
0.0906 (0.2071)
Sorriso (MT)
19
18
0.0498 (0.1242)
0.0782 (0.1845)
Rio Verde (GO)
29
27
0.1041 (0.1790)
0.1540 (0.2597)
Dom Bosco (MG) Janau´ba JN (MG)
39
37
0.1204 (0.1813)
0.1824 (0.2628)
31
29
0.0968 (0.1736)
0.1454 (0.2500)
Janau´ba JA (MG) Janau´ba JB (MG) Janau´ba JU (MG)
38
36
0.1328 (0.1884)
0.1987 (0.2735)
37
35
0.1155 (0.1842)
0.1734 (0.2639)
32
30
0.1242 (0.1936)
0.1817 (0.2787)
Janau´ba JJ (MG)
39
37
0.1270 (0.1867)
0.1909 (0.2696)
Eldorado (MS) Buritama (SP)
36 30
34 28
0.1210 (0.1798) 0.1200 (0.1900)
0.1824 (0.2636) 0.1763 (0.2741)
Natal (RN)
41
39
0.1593 (0.2056)
0.2335 (0.2949)
Arcoverde (PE)
34
32
0.1241 (0.1881)
0.1844 (0.2724)
Unknown
45
43
0.1669 (0.2094)
0.2450 (0.2975)
Minimum
19
17
0.0498 (0.1242)
0.0782 (0.1845)
Maximum
45
43
0.1699 (0.2094)
0.2450 (0.2975)
All groups
98
94
0.2841 (0.1675)
0.4340 (0.2202)
123
Mol Biol Rep
index for the accessions from Natal (RN) were significantly different (P \ 0.047) from those of unknown origin. The values of the Shannon index were not significantly different between the two locations (P [ 0.05). Genetic relationships of J. curcas accessions All 104 polymorphic loci obtained from seven ISSR primers were used to estimate the genetic distance among accessions by calculating the Nei’s genetic distance. The genetic distance matrix was used to construct a phenogram of genetic relationships based on the UPGMA clustering method (Fig. 6). The values of genetic distance ranged from 0.07 to 0.47, suggesting a great genetic base. The 332 accessions were grouped into three main clusters: Cluster I containing 42 accessions from the localities of Ariquemes (RO) and Arac¸atuba (SP); Cluster II containing a total of 228 accessions from Dom Bosco (MG), Buritama (SP), Eldorado (MS), Janau´ba (JU, JJ, JA, JB and JN) (MG), Rio Verde (GO), Sorriso (MT) and Sa˜o Paulo (SP); and Cluster III containing 62 accessions from Natal (RN), Arcoverde (PE) and of unknown origin. In cluster II we found 3 sub-clusters formed by the accessions from Sa˜o Paulo (SP) and Sorriso (MT) (sub-cluster I), from Rio Verde (GO), Dom Bosco (MG), Janau´ba JJ (MG) and Eldorado (MS) (sub-cluster II) and from Janau´ba (JN, JA, JB, JU) (MG) (sub-cluster III). The accessions from Buritama (SP) were not grouped in any of the sub-clusters. The phenogram shows accessions from the states of Rondoˆnia and Sa˜o Paulo in cluster I, Sa˜o Paulo, Minas Gerais, Mato
Fig. 6 Phenogram based on UPGMA cluster analysis of ISSR data, showing three main clusters formed among 332 accessions of J. curcas from 12 localities in Brazil. The scale represents Nei’s genetic distance values
123
Grosso, Mato Grosso do Sul and Goia´s in cluster II, and Rio Grande do Norte and Pernambuco in cluster III. The matrix of genetic relationships based on Nei’s genetic distance was subjected to multidimensional scaling (MDS) analysis (Fig. 7). The test showed the same grouping of accessions into three main clusters as seen for the phenogram in Fig. 5 (with a significance level of P \ 0.05). In a MDS analysis the shorter distance between the groups of the same cluster, the greater genetic relationship. Thus in cluster II, accessions from Eldorado (MS), Janau´ba (JB and JJ) (MG), Dom Bosco (MG) and Buritama (SP) are more closely related; accessions from Rio Verde (GO) and Janau´ba (JU) (MG), Sa˜o Paulo (SP), Sorriso (MT) and Janau´ba (JN) (MG) are closer genetically; and Janau´ba (JA) (MG) is equidistant from the other sub-clusters. In cluster III, accessions from Natal (RN) and of unknown origin were closer, as also observed in the phenogram of Fig. 5, suggesting that the accessions of unknown origin may have came from Natal (RN). The estimative of the genetic variance components for all 104 loci obtained by ISSR analysis was performed using AMOVA [29]. This estimative of partitioning genetic variance revealed higher variance among groups, among sites and within sites (Table 6). Relatively, lower genetic variance within collection sites (1197.3) than genetic variance among collection sites within groups (1243.0) was observed. The U-statistics were highly significant (P [ 0,001) in all three U categories (Table 6). The U values among groups (UCT), among sites (USC) and within sites (UST) revealed high genetic differentiation of J. curcas
Mol Biol Rep
ISSR system efficiency The efficiency of a molecular marker system can be measured by the amount of polymorphism that can be detected among the accessions under investigation. All seven ISSR primers used in this work revealed extensive polymorphism: in 3,036 bands (average number of bands generated per ISSR primer), 2,781 were polymorphic (Table 2). The total polymorphism deduced from seven ISSR primers was 91%, a result comparable to another study of the genus Jatropha also using ISSR markers, which yielded 100% polymorphism with eight primers [17]. The accounting for rare, shared and similar polymorphic bands detected by ISSR primers in this study is novel, and has not been described in other studies using this system of markers. The detection of rare polymorphic bands is very useful for selecting accessions for genetic improvement of J. curcas, as well as to identify a particular accession. Shared and similar polymorphic bands are important for understanding the relationships and similarities between the accessions and can are be used for describing the molecular profiles of accessions from different locations as well as for inferring a likely geographic origin of accessions with uncertain origin [18]. A comparison of rare, shared and similar polymorphic bands showed that two ISSR primers (848 and 856) were more effective in detecting rare bands and that all primers were able to detect shared and similar bands (Table 2). Most wild genotypes have unique alleles that are frequently lost by cultivation, and it is interesting to note that the higher frequency of rare bands identified in accessions from Natal (RN) may be an indication that this group suffered very little or no human intervention or selection, being the wildest among the groups studied. The efficiency of ISSR markers can also be evaluated through parameters such as PIC (polymorphic information content, described by Roldan-Ruiz et al. [22] for AFLP), MI (marker index, described by Varshney et al. [20] for SNP markers, SSR and AFLP) and RP (resolution power, described by Prevost and Wilkinson [24] for ISSR). Parameters such as PIC have been used increasingly for assessing the informative potential of ISSR markers in
Fig. 7 Multidimensional scaling (MDS) analysis based on ISSR data showing the three main clusters formed among 332 accessions of J. curcas from 12 localities in Brazil
accessions (UST = 0.709) and an initial genetic structuring in J. curcas sites (UCT = 0.374).
Discussion Jatropha curcas has gained popularity in recent years as a plant of great potential for biodiesel production. Nevertheless, a major limitation to its large-scale cultivation in Brazil is the lack of knowledge of its genetic variability, essential for the establishment and development of breeding and seed production programs. So far, all J. curcas cultivated in Brazil is derived from seeds bought by commercial sellers from small local farmers. Therefore, as a first step toward developing elite plants, capable of sustaining cultivation of J. curcas in different agroclimatic zones, a genetic analysis of different accessions was carried out. The choice of the appropriate molecular marker type for genetic analysis of this particular species depends on the ability of the markers to estimate the genetic diversity in a reliable way [22]. Hence, in the present investigation we first assessed the efficiency of ISSR markers for the detection of genetic diversity of J. curcas Brazilian accessions and their profile of ISSR genetic variation.
Table 6 Summary of AMOVA analysis of genetic variance components for 332 accessions of J. curcas representing 16 collection sites Source of variation
d.f.
Among groups
2
Sum of squares 977.4
Variance 5.20
Percentage of variation
U values
P-value
37.4
UCT = 0.374
\0.001
Among sites within groups
13
1243.0
4.65
33.4
USC = 0.535
\0.001
Within sites
–
1197.3
4.03
29.0
UST = 0.709
\0.001
Total
331
3318.0
13.89
–
–
–
d.f. degrees of freedom Level of significance is based on 1000 iteration
123
Mol Biol Rep
different cultivated species (Patra et al. [31]; Thimmappaiah et al. [32]; Gomes et al. [33]; Muthusamy et al. [34]). The PIC values of each marker represent the probability of finding this marker in two random accessions from the same locality. These values can range from 0.0 for monomorphic markers to 0.5 for markers that are present in 50% of accessions and absent in the other 50%. The system of ISSR primers used in this study generated 23 highly informative polymorphic loci with PIC values greater than 0.45 among the total of 98 polymorphic loci (Fig. 5). In the analysis of the primers the largest PIC value was found for the ISSR primer 807 (0.31) (Table 4). The average PIC value per primer (0.26) is comparable to that found by Thimmappaiah et al. [32] using ISSR markers in cashew (0.29) and Tatikonda et al. [18] using AFLP markers in J. curcas (0.26). For its ability to provide high polymorphic information content, the set of ISSR primers used was considered efficient and suitable for the analysis of J. curcas accessions from Brazil. The analysis of the frequency of polymorphic bands for each locus showed that the greater number of polymorphic bands was observed in most of the loci (30% of polymorphic loci showed frequency of amplification of bands ranging from 0.90 to 0.99) (Fig. 3). These results contrast with the observation of Tatikonda et al. [18] with AFLP markers, where most of polymorphic bands were found in the frequency class 0.0–0.10. However, when the frequency of polymorphic bands was correlated with the mean values of PIC, the majority of the polymorphic bands had low PIC values. Thus, similar to the results found by Tatikonda et al. [18] for AFLP analysis, ISSR bands amplified with the frequency of 0.4 to 0.6 proved to be the most informative (average PIC of 0.49), followed by the frequency of 0.6 to 0.7 (average PIC of 0.45). The low values of PIC observed in polymorphic bands amplified more often (higher frequency) is to be expected because these bands were more prevalent among accessions, tending towards monomorphism. On the other hand, for loci where bands are amplified with lower frequency (as in the case of AFLP markers), the absence of bands would be more prevalent among accessions, also tending toward monomorphism. Nevertheless, these bands are important for understanding the relationships and similarities between the accessions. The marker index (MI), which can be considered to be a general measure of efficiency in detecting polymorphism, varied between 15.07 and 20.75 (average 17.86) in different ISSR primers (Table 4). The average of MI values was much higher than those already described for ISSR markers in different crop species: 1.76, in cashew [32] and 8.86, in Canna generalis [31]. The high MI values found with the ISSR primers derive from its higher effective multiplex ratio in relation to other molecular marker techniques, and
123
from its great ability to detect polymorphism. This shows that the ISSR primers used were efficient and suitable for the genetic diversity analysis of J. curcas. The highly positive and significant correlation observed between the PIC and MI values (r2 = 0.909, P \ 0.05) indicates that both parameters can be used to compare the information content generated by the ISSR system. An important feature of a good marker system is the capacity to distinguish among different accessions. Prevost and Wilkinson [24] described the parameter resolving power (RP) as a measure of the discriminatory power of ISSR molecular markers. The values of resolving power in that study ranged from 4.6 to 12.5. In another study using ISSR markers to distinguish genotypes of barley, Ferna´ndez et al. [35] found RP values between 3.75 and 20.63, with an average of 9.79 per primer. Both studies found a linear relationship between the ability to distinguish accessions of a primer and the values of RP. In our study with ISSR primers, the RP values ranged from 15.96 to 25.69 (Table 4), suggesting that the set of ISSR primers used was capable of distinguishing among different accessions. The absence of significant correlation between the values of RP and MI, and the highly positive correlation between the values of PIC and MI observed in our study, suggest the use of MI and PIC parameters to compare the information content of polymorphic ISSR and the use of RP to select the most informative ISSR marker to distinguish among different accessions. Genetic information of Brazilian cultivated accessions To analyze the genetic information for the J. curcas accessions obtained from 12 localities, we examined the data for presence and absence of bands generated by seven ISSR primers. We calculated the percentage of polymorphic loci, Nei’s diversity index, the Shannon Information index and Nei’s genetic distance. From the values for genetic distance, a phenogram was generated based on the UPGMA clustering method and a two-dimensional graph was constructed based on multivariate (MDS) analysis. The polymorphism of a given set of accessions is caused by the existence of genetic variants, represented by the number of alleles per locus and by their frequency distribution in the group of accessions [12]. In our analysis of polymorphic loci of J. curcas accessions from different localities, it was observed that accessions from Natal (RN) and accessions of unknown origin (UN) had the highest percentage of polymorphic loci (39 and 43% respectively), suggesting that these accessions could be the wildest among all accessions analyzed (Table 5). The accessions from Sa˜o Paulo (SP), Arac¸atuba (SP) and Sorriso (MT) were the most genetically uniform accessions, probably because of more intensive cultivation.
Mol Biol Rep
In the estimates of genetic diversity from Nei’s or Shannon’s index, Brazilian accessions of J. curcas showed a great deal of genetic diversity, 0.2841 and 0.4340 by the two indices, respectively (Table 5) when compared with the diversity found by Ranade et al. [11] in J. curcas Indian accessions using RAPD and DAMD markers (0.2118 and 0.3433 for Nei’s and Shannon’s diversity indices, respectively). Furthermore, the genetic diversity computed for the different groups of Brazilian accessions showed that of 16 groups, 11 had high levels of genetic diversity (above 0.1 and above 0.15 for Nei’s and Shannon’s diversity indices, respectively). The accessions from Natal (RN) and the accessions of unknown origin had the highest genetic diversity among the localities analyzed, 0.1593 and 0.2335, 0.1669 and 0.2450, respectively, corroborating the data for percentage of polymorphic loci, and suggesting that these accessions have greater information content and can be used as target genotypes for the improvement of J. curcas in Brazil. In the analysis of genetic relationships among accessions using the values for Nei’s genetic distance we observed the same profile of principal clusters in both the UPGMA phenogram (Fig. 6) and the MDS analysis (Fig. 7), with some variation in sub-clusters. The accessions from geographically closer locations were grouped more closely in both cases (phenogram/MDS). Thus accessions from Janau´ba (MG) and Dom Bosco (MG) appeared in the same cluster. The phenogram and the MDS analysis revealed a higher genetic relationship among accessions from Ariquemes (RO) and Arac¸atuba (SP); among accessions from Sa˜o Paulo (SP) and Sorriso (MT); from Janau´ba JJ (MG) and Eldorado (MS); and from Natal (RN) and the accessions of unknown origin (UN), indicating that the more closely related accessions have a common origin although thus may have been distributed into different states. The highest percentage of polymorphism and diversity was observed in accessions from Natal (RN) and those of unknown origin (UN) and these accessions were more closely related in the phenogram and MDS analysis, suggesting that the accessions of unknown origin may very well have come from Natal (RN) and not from Arcoverde (PE), where these accessions were acquired. This is corroborated by the fact that there was no significant difference (P \ 0.05) in the Shannon index found for the accessions from these two places (Natal (RN) and unknown origin). The narrow genetic relationships and the low genetic differentiation among clusters (UCT = 0.374) found in J. curcas Brazilian accessions could indicate that the seeds have come from the same source with few introductions, as described by Basha et al. [4] when analyzing accessions of different countries in Asia, Africa and Central America. Nevertheless, the clusters II and III show a large degree of
separation that may have been provoked by the accumulation of genetic differences fixed by adaptation to different environments and also by geographical isolation (lack of gene flow). A similar level of genetic separation observed in clusters I and II could indicate that the Brazilian accessions were spread from the Northeast, where J. curcas has been planted for some time, to others regions in Brazil [10]. In Brazil an initial domestication process of J. curcas seems to be happening, which can be seen in the reduced number of polymorphic loci and genetic diversity of accessions from Ariquemes (RO), Arac¸atuba (SP), Sa˜o Paulo (SP) e Sorriso (MT). The low genetic diversity could reflect selections from local small farmers to obtain a more uniform crop in terms of productivity. Although this domestication process had been initiated in some locations, the polymorphism of Brazilian accessions (91%) is higher than in accessions of others countries in African and Asian and Central America (61%) [4]. Genetic diversity is also higher in Brazilian accessions (0.2841) than Indian accessions (0.2118) [11] and there is high genetic differentiation in Brazilian accessions (UST = 0.709). In conclusion, the ISSR markers proved to be highly informative and efficient in the selection of more diverse accessions of J. curcas. The information on rare fragments, polymorphic loci, genetic diversity (Nei and Shannon) and genetic relationships among accessions obtained with the phenogram and MDS analysis indicates that accessions from Natal (RN) would be an appropriate material for use as a diversity bank for improvement of J. curcas in Brazil. In addition, this study provides information about geographically relationships and genetic structuring of Brazilian accessions and it shows that the J. curcas Brazilian accessions can have good value as source of genetic diversity for improvement programs of J. curcas in world. Acknowledgments We would like to thank the farmers Nagashi Tominaga and Divino Nunes for donating J. curcas seeds grown in Janau´ba (MG) and Rio Verde (GO) and Dr. Antoˆnio Andrade of the Instituto de Pesquisas Jardim Botaˆnico do Rio de Janeiro for his generous assistance with seed germination. We also thank Prof. Wellington Matos from the Universidade do Grande Rio for help with the map in Fig. 1. We are especially grateful to Prof. Martha Sorenson from the Universidade Federal do Rio de Janeiro for language editing. We are grateful to Conselho Nacional de Desenvolvimento Cientı´fico e Tecnolo´gico (CNPq) for financial support and fellowships.
References 1. Fairless D (2007) The little shrub that could—maybe. Nature 449:652–655 2. Achten WMJ, Verchot L, Franken YJ, Mathijs E, Singh VP, Aerts R, Muys B (2008) Jatropha bio-diesel production and use. Biomass Bioenergy 1–13. doi: 10.1016/j.biombioe.2008.03.003 3. Becker K, Makkar HPS (2008) Jatropha curcas: a potencial source for tomorrow0 s oil and biodiesel. Lipid Technol 20: 104–109. doi:10.1002/lite.200800023
123
Mol Biol Rep 4. Basha SD, Francis G, Makkar HPS, Becker K, Sujatha M (2009) A comparative study of biochemical traits and molecular markers for assessment of genetic relationships between Jatropha curcas L. germplasm from different countries. Plant Sci 176:812–823. doi:10.1016/j.plantsci.2009.03.008 5. Heller J (1996) Physic nut. Jatropha curcas L. Promoting the conservation and use of underutilized and neglected crops. Insitute of Plant Genetics and Crop Plant Research, Gatersleben, Germany and International Plant Genetic Resources Institute, Rome, Italy, 1996. http://www.bio-nica.info/biblioteca/Heller 1996Jatropha.pdf. Accessed 14 Mar 2008 6. Henning RK (2009) The Jatropha system. An integrated approach of rural development. http://www.jatropha.de/documents/The JatrophaBook-2009.pdf. Accessed 16 Dec 2009 7. Kumar A, Sharma S (2008) An evaluation of multipurpose oil seed crop for industrial uses (Jatropha curcas L.): a review. Ind Crops Prod 28:1–10. doi: 10.1016/j.indcrop.2008.01.001 8. Makkar HPS, Aderibigbe AO, Becker K (1998) Comparative evaluation of non-toxic and toxic varieties of Jatropha curcas for chemical composition, digestibility, protein degradability and toxic factors. Food Chem 62:207–215 9. Jongschaap REE, Corre´ WJ, Bindraban PS, Brandenburg WA (2007) Claims and facts on Jatropha curcas L. Global Jatropha curcas evaluation, breeding and propagation programme. http:// www.ifad.org/events/jatropha/breeding/claims.pdf. Accessed 14 Mar 2008 10. Arruda FP, Beltra˜o NEM, Andrade AP, Pereira WE, Severino LS (2004) Cultivo de pinha˜o manso (Jatropha curcas L.) como alternativa para o semi-a´rido nordestino. Revista Brasileira de Oleaginosas e Fibrosas 8:789–799 11. Ranade SA, Srivastava AP, Rana TS, Srivastava J, Tul R (2008) Easy assessment of diversity in Jatropha curcas L. plants using two simple-primer amplification (SPAR) methods. Biomass Bioenergy 32:533–540. doi:10.1016/j.biombioe.2007.11.006 12. Gupta S, Srivastava M, Mishra GP, Naik PK, Chauhan RS, Tiwari SK, Kumar M, Singh R (2008) Analogy of ISSR and RAPD markers for comparative analysis of genetic diversity among different Jatropha curcas genotypes. Afr J Biotechnol 7: 4230–4243 13. Ram SG, Parthiban KT, Kumar RS, Thiruvengadam V, Paramathma M (2008) Genetic diversity among Jatropha species as revealed by RAPD markers. Genet Resour Crop Evol. doi: 10.1007/s10722-007-9285-7 14. Pamidiamarri DVNS, Singh S, Mastan SG, Patel J, Reddy MP (2009) Molecular characterization and identification of markers for toxic and non-toxic varieties of Jatropha curcas L. using RAPD, AFLP and SSR markers. Mol Biol Rep 36:1357–1364. doi:10.1007/s11033-008-9320-6 15. Ikbal, Boora KS, Dhillon RS (2010) Evaluation of genetic diversity of Jatropha curcas L. using RAPD markers. Indian J Biotechnol 9:50–57 16. Basha SD, Sujatha M (2007) Inter and intra-population variability of Jatropha curcas (L.) characterized by RAPD and ISSR markers and development of population-specific SCAR markers. Euphytica 156:375–386. doi:10.1007/s10681-007-9387-5 17. Kumar R S, Parthiban K T, Rao M G (2008) Molecular characterization of Jatropha genetic resources through inter-simple sequence repeat (ISSR) markers. Mol Biol Rep. doi:10.1007/ s11033-008-9404-3 18. Tatikonda L, Wani SP, Kannan S, Beerelli N, Sreedevi TK, Hoisington DA, Devi P, Varshney RK (2009) AFLP-based molecular characterization of an elite germplasm collection of
123
19.
20.
21.
22.
23. 24.
25.
26.
27.
28. 29.
30.
31.
32.
33.
34.
35.
Jatropha curcas L., a biofuel plant. Plant Sci 176:505–513. doi: 10.1016/j.plantsci.2009.01.006 Sun Q, Li L, Li Y, Wu G, Ge X (2008) SSR and AFLP markers reveal low genetic diversity in the biofuel plant Jatropha curcas in China. Crop Sci 48:1865–1871. doi:10.2135/cropsci208.02.0074 Varshney RK, Chabane K, Hendre PS, Aggarwal RK, Graner A (2007) Comparative assessment of EST-SSR, EST-SNP and AFLP markers for evaluation of genetic diversity and conservation of genetic resources using wild, cultivated and elite barleys. Plant Sci 173:638–649. doi:10.1016/j.plantsci.2007.08.010 Zietkiewicz E, Rafalski A, Labuda D (1994) Genome fingerprinting by simple sequence repeat (SSR)-anchored polymerase chain reaction amplification. Genomics 20:176–183 Roldan-Ruiz I, Dendauw J, Vanbockstaele E, Depicker A, De Loose M (2000) AFLP markers reveal high polymorphic rates in ryegrasses (Lolium spp.). Mol Breed 6:125–134 Doyle JJ, Doyle JL (1987) A rapid DNA isolation method for small quantities of fresh tissues. Phytochem Bull 19:11–15 Prevost A, Wilkinson MJ (1999) A new system of comparing PCR primers applied to ISSR fingerprinting of potato cultivars. Theor Appl Genetic 98:107–112 Bonin A, Ehrich D, Manel S (2007) Statistical analysis of amplified fragment length polymorphism data: a toolbox for molecular ecologists and evolutionists. Mol Ecol 16:3737–3758. doi:10.1111/j.1365-294X.2007.03435.x Mohammadi SA, Prasanna BM (2003) Analysis of genetic diversity in crop plants—salient statistical tools and considerations. Crop Sci 43:1235–1248 Yeh FC, Yang R, Boyle T (1999) PopGene: Microsoft Windowbased freeware for population genetic analysis, version 3.2, University of Alberta, Edmonton Rohlf FJ (1993) NTSYS-pc (Numerical Taxonomy and Multivariate Analysis System), version 2.11. Exeter Software, Setauket Excoffier L, Smouse P, Quattro J (1992) Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics 131:479–491 Excoffier L, Laval LG, Schneider S (2005) Arlequin ver. 3.1: an integrated software package for population genetics data analysis. Evol Bioinform Online 1:47–50 Patra B, Acharya L, Mukherjee AK, Panda MK, Panda MC (2008) Molecular characterization of ten cultivars of Canna lilies (Canna Linn.) using PCR based molecular markers (RAPDs and ISSRs). Int J Integr Biol 2:129–137 Thimmappaiah, Santosh WG, Shobha D, Melwyn GS (2009) Assessment of genetic diversity in cashew germplasm using RAPD and ISSR markers. Sci Hortic 120:411–417. doi:10.1016/ j.scienta.2008.11.022 Gomes S, Martins-Lopes P, Lopes J, Guedes-Pinto H (2009) Assessing genetic diversity in Olea europaea L. using ISSR and SSR markers. Plant Mol Biol Rep 27:365–373. doi:10.1007/ s11105-009-0106-3 Muthusamy S, Kanagarajan S, Ponnusamy S (2008) Efficiency of RAPD and ISSR markers system in accessing genetic variation of rice bean (Vigna umbellata) landraces. Electron J Biotechnol. doi:10.2225/vol11-issue3-fulltext-8 Fernande´z ME, Figueiras AM, Benito C (2002) The use of ISSR and RAPD markers for detecting DNA polymorphism, genotype identification and genetic diversity among barley cultivars with known origin. Theor Appl Genet 104:845–851. doi:10.1007/s00 122-001-0848-2
