Skip to content

Advertisement

BMC Research Notes

Research article
Open Access

Developmenrt of EST-SSR and genomic-SSR markers to assess genetic diversity in Jatropha Curcas L.

  • Mingfu Wen1, 2,
  • Haiyan Wang1,
  • Zhiqiang Xia1,
  • Meiling Zou1,
  • Cheng Lu1 and
  • Wenquan Wang1Email author
BMC Research Notes20103:42

https://doi.org/10.1186/1756-0500-3-42

©  Wang et al; licensee BioMed Central Ltd. 2010

Received: 28 January 2010

Accepted: 24 February 2010

Published: 24 February 2010

Abstract

Background

Jatropha curcas L. has attracted a great deal of attention worldwide, regarding its potential as a new biodiesel crop. However, the understanding of this crop remains very limited and little genomic research has been done. We used simple sequence repeat (SSR) markers that could be transferred from Manihot esculenta (cassava) to analyze the genetic relationships among 45 accessions of J. curcas from our germplasm collection.

Results

In total, 187 out of 419 expressed sequence tag (EST)-SSR and 54 out of 182 genomic (G)-SSR markers from cassava were polymorphic among the J. curcas accessions. The EST-SSR markers comprised 26.20% dinucleotide repeats, 57.75% trinucleotide repeats, 7.49% tetranucleotide repeats, and 8.56% pentanucleotide repeats, whereas the majority of the G-SSR markers were dinucleotide repeats (62.96%). The 187 EST-SSRs resided in genes that are involved mainly in biological and metabolic processes. Thirty-six EST-SSRs and 20 G-SSRs were chosen to analyze the genetic diversity among 45 J. curcas accessions. A total of 183 polymorphic alleles were detected. On the basis of the distribution of these polymorphic alleles, the 45 accessions were classified into six groups, in which the genotype showed a correlation with geographic origin. The estimated mean genetic diversity index was 0.5572, which suggests that our J. curcas germplasm collection has a high level of genetic diversity. This should facilitate subsequent studies on genetic mapping and molecular breeding.

Conclusion

We identified 241 novel EST-SSR and G-SSR markers in J. curcas, which should be useful for genetic mapping and quantitative trait loci analysis of important agronomic traits. By using these markers, we found that the intergroup gene diversity of J. curcas was greater than the intragroup diversity, and that the domestication of the species probably occurred partly in America and partly in Hainan, China.

Keywords

Simple Sequence Repeat MarkerQuantitative Trait Locus AnalysisTrinucleotide RepeatDinucleotide RepeatPolymorphic Allele

Background

Jatropha curcas L. is a perennial, monoecious shrub that belongs to the Euphorbiaceae family. The species is native to America but is distributed widely in the tropics [1, 2]. Wild or semicultivated types of J. curcas can grow well under all unfavorable climatic and soil conditions [3]. The seeds of J. curcas contain 40-45% oil [4–6] with a high percentage of monounsaturated oleic and polyunsaturated linoleic acid. The seed oils can be classified as semi-drying [7]. In recent years, the economic importance of J. curcas for the production of biodiesel fuel has been increasingly recognized [8].

To use J. curcas for producing biofuel, it is crucial to develop varieties with a high seed yield and a high oil content that are adapted well to varied conditions [9]. In recent years, J. curcas germplasm has been collected and analyzed in Brazil, India, Indonesia, and China [10, 11]. J. curcas has a heterozygous genome, so conventional breeding programs for its improvement might not be effective. Hence, it is likely that genomics-based breeding strategies need to be used. However, the genetic map of J. curcas is not well-developed and very limited information is available with respect to molecular markers. The traditional methods of developing simple sequence repeat (SSR) markers are usually time-consuming and labor-intensive. However, an alternative strategy has been developed that uses comparative genomics to identify SSR markers. This strategy has been used successfully in barley [12] and Brassica [13].

In the study reported herein, we selected 419 expressed sequence tag (EST)-SSR and 182 genomic (G)-SSR primer pairs that had been developed for Manihot esculenta (cassava), which also belongs to the Euphorbiaceae family, and investigated whether they could be transferred to J. curcas. Firstly, these primer pairs were tested using five accessions of J. curcas. The primer pairs that produced specific amplicons were then tested further. Finally, we used the transferable markers to analyze the genetic diversity of our collection of J. curcas accessions.

Results

Identification of EST-SSRs and G-SSRs for use in J. curcas

Five accessions of J. curcas were used to test the transferability of 419 EST-SSR and 182 G-SSR primer pairs between cassava and J. curcus. Among these primers, 234 (55.85%) EST-SSR and 68 (37.36%) G-SSR primer pairs produced amplicons, but only 187 (44.63%) EST-SSRs and 54 (29.67%) G-SSRs were polymorphic among the five accessions (Fig.1). For each of the 241 EST-SSR and G-SSR markers in J. curcas, the name, sequence of the forward and reverse primers, the repeat type, annealing temperature, and expected size of the PCR products are listed in additional file 1.
Figure 1
Figure 1

PCR amplification of the loci JESR-118 (CESR0806) and JSSR-203 (SSRY100) in five accessions of Jatropha curcas.

Characteristics of the EST-SSR and G-SSR markers and relevant genes in J. curcas

Microsatellites or SSRs are regions in the genome of tandemly repeated DNA segments, with each segment comprising up to six bases. Analysis of the nucleotide sequences of the EST- and G-SSRs showed that, in J. curcas, the EST-SSRs corresponded to 57.75% trinucleotide repeats, 26.20% dinucleotide repeats, 8.56% pentanucleotide repeats, and 7.49% tetranucleotide repeats. In contrast, the G-SSRs were composed mainly of dinucleotide repeats (62.96%) (Table 1).
Table 1

Structural characteristics of the ES T-SSR and G-SSR markers in J. curcas

Repeat type

EST-SSR

G-SSR

 

No.

Polymorphic

No.

Polymorphic

  

Number and percentage

 

Number and percentage

Di

57

49

26.20

43

34

62.96

Tri

139

108

57.75

4

2

3.70

Tetra

20

16

8.56

-

-

-

Penta

18

14

7.49

-

-

-

Total

234

187

100

68

54

100

A BLAST search of the GenBank database was performed using the sequences of the 187 EST-SSRs. The functions of the ESTs were then clustered according to the general functional categories in the KOG (Eukaryotic Orthologous Groups of Proteins) database. A general functional classification of the EST-SSRs is shown in Fig. 2. Out of the 187 unique ESTs, 184 were annotated and they were classified into more than 55 categories. Many ESTs were found in several categories. The five main categories, which contained 137 of the ESTs, were biological processes, cellular processes, metabolic processes, cellular metabolic processes, and primary metabolic processes.
Figure 2
Figure 2

Functional classification of the ESTs amplified by the 187 EST-SSR primer pairs in J. curcas (GO-Terms: Gene Ontology Terms).

Assessment of genetic diversity in J. curcas

Thirty-six EST-SSRs and 20 G-SSRs were used to estimate the genetic diversity of 45 accessions of J. curcas. A total of 216 alleles were identified, and 183 (84.72%) of them were polymorphic. The sizes of the amplicons for the EST-SSRs and G-SSRs ranged from 120 to 600 bp. The 36 EST-SSRs generated 152 alleles, of which 128 (84%) were polymorphic, whereas the 20 G-SSRs produced 64 alleles, of which 55 (86%) were polymorphic. The number of alleles for each G-SSR ranged from one to six with a mean of 3.20; in contrast, the number of alleles for each EST-SSR ranged from one to nine with a mean of 4.22. Therefore, more alleles were obtained with the EST-SSRs than with the G-SSRs.

Genotyping data that were obtained for all 183 polymorphic alleles were used to estimate pairwise similarity comparisons among these accessions. The similarity coefficient values of the phenogram ranged from 0.55 to 0.92 with a mean of 0.76 (Fig. 3). The 45 accessions clustered into six groups: group I, which comprised seven accessions from Indonesia and four from South America; group II, which comprised three accessions from Grenada, one from South America, and three from Yunnan, China; Group III, in which all four accessions were from Hainan, China; Group IV, which comprised four accessions from Yunnan, China plus three American clones; Group V, which consisted of two accessions from Indonesia, two from Grenada, one from America, and one from Hainan, China; and Group VI, which comprised four clones from Hainan, China, three from Grenada, and one from Yunnan, China. The results suggested that, in general, the genotypes of the J. curcas accessions are correlated with their geographic origins, although some crosses had occurred.
Figure 3
Figure 3

An UPGMA tree of the 45 Jatropha accessions.

The parameters of genetic diversity between and within the five geographic groups (Table 2) were estimated using all 183 EST-SSR and G-SSR polymorphic alleles. The genetic diversity index ranged from 0.4099 to 0.5022 with an average of 0.5572, which suggested that the collection had a broad genetic background, although the level of genetic diversity was higher among the accessions from America and Yunnan, China. The total gene diversity (Ht = 0.3819 ± 0.0197) was higher than the gene diversity within groups (Hs = 0.3108 ± 0.0158), which indicated that the genetic diversity between groups is greater than that within groups. The coefficient of gene differentiation (Gst) was 0.1861, which indicated that the level of genetic differentiation among the five groups was relatively high. The gene flow index (Nm) was 2.18, which indicated that high levels of gene flow occurred during the domestication of J. curas.
Table 2

Parameters of intra- and intergroup genetic diversity among the five populations of J. curcas

Population

n

#loc_P

PLP (%)

na

ne

H

I

Indonesia

9

118

54.63

1.7541

1.5769

0.3140

0.4526

Grenada

9

100

46.30

1.6995

1.5046

0.2818

0.4099

South America

9

147

68.06

1.8907

1.5763

0.3326

0.4920

Yunnan, China

9

108

50.00

1.8361

1.6274

0.3473

0.5022

Hainan, China

9

148

68.52

1.8142

1.4575

0.2785

0.4221

Total

45

183

84.72

2.0000

1.6869

0.3819

0.5572

St. Dev

-

-

-

± 0.0000

± 0.3107

± 0.1403

± 0.1741

    

Ht

Hs

Gst

Nm

Mean

   

0.3819

0.3108

0.1861

2.1868

St. Dev

   

± 0.0197

± 0.0158

  

n = number of genotypes per sample;

#loc P = number of polymorphic loci;

PLP = percentage of polymorphic loci;

na = observed number of alleles;

ne = effective number of alleles [Kimura and Crow (1964)];

h = Nei's (1973) gene diversity;

I = average genetic diversity index;

Ht = total gene diversity;

Hs = intrapopulation gene diversity;

Gst = coefficient of gene differentiation;

Nm = estimate of gene flow from Gst; Nm = 0.5 (1-Gst)/Gst

The genetic similarity matrix that was obtained from Jaccard's similarity coefficient was subjected to principal coordinates analysis (PCA). The results indicated that all 45 accessions could be divided into three categories (Fig. 4). Category I contained the most accessions and they came from all five areas. Category II included eight accessions in total: three from Grenada, two from Indonesia, two from Hainan, and one from America. Category III contained four accessions from Hainan, two from Grenada, and one from Yunnan. Category I included many accessions from different areas, and therefore demonstrated the weak geographic differentiation in the population; in other words, the observed genetic diversity was due mainly to original evolution. The category II and category III clusters revealed that the phylogenetic characteristics of J. curcas from South America, where J. curcas originated, and from Hainan in China, where the species was domesticated less than 300 years ago, were very similar.
Figure 4
Figure 4

Two-dimensional PCA plot of the 45 J. curcas accessions.

Discussion

Development of molecular markers in J. curcas

It was crucial to develop a sufficient number of species-specific markers to enable the fingerprinting and genetic mapping of J. curcas; in rice and soybean, for example, more than 10,000 SSR and SNP markers are available [14, 15]. Random amplified polymorphic DNA (RAPD), inter-simple sequence repeat (ISSR) and amplified fragment length polymorphism (AFLP) markers have been used to evaluate genetic diversity and for fingerprinting in accessions of J. curcas and related species [11, 16–18]. Up to now, only 12 SSR markers had been developed for Jatropha (they are polymorphic in six species of Jatropha) [19], and no genetic map of Jatropha has been reported. The transferable SSRs identified in the present study should enable the genetic diversity, elite clones, and evolution of J. curcas to be assessed, and a linkage map to be constructed.

Transferability of EST-SSRs and G-SSRs from cassava

The development of EST-SSR markers was particularly attractive because they represent coding regions of the genome. It has been reported that EST-derived SSRs and G-SSRs show a considerable degree of transferability to related species [20–24]. In the present study, we observed a high level of transferability from cassava to J. curcas; the level of transferability was higher for EST-SSRs (44.63%) than for G-SSRs (29.67%). This result was consistent with that reported for wheat and related species [25]. In several studies, the level of transferability of G-SSRs was found to be only 20-30% [26–29]. The higher levels of transferability of EST-SSRs than of G-SSRs reflect the conserved nature of coding sequences as compared with non-coding genomic DNA, and the fact that the mutation frequency of EST sequences is lower than that of genomic DNA sequences. These results demonstrate the potential value of EST-SSR markers for the development of genetic maps, assessment of genetic diversity, and marker-assisted selection (MAS) breeding in J. curcas, all of which would benefit comparative mapping and analysis of the comparative functions of genes among the economic species in the Euphorbiaceae family. Our finding that the majority of transferable EST-SSRs were trinucleotide repeats agreed with previous studies [21, 30–32], and can be explained by the suppression of non-trimeric SSRs in coding regions due to the risk of frameshift mutations, which might occur with non-trimeric microsatellites [33–36].

Collection of germplasm and evaluation of genetic diversity

Several projects to collect germplasm have been carried out in Brazil [37], India [38], and China [39], but systematic work on the collection of germplasm and its evaluation is still in its infancy. The low genetic variability found among accessions of J. curcas from Africa and Asia [11, 16, 17] has demonstrated the need for new sources of genetic variation in J. curcas that could be used in breeding programs. Such sources of genetic variation have been identified in Latin America, especially in Guatemala [40]. In the present study, high levels of genetic diversity were revealed in the 45 Jatropha accessions analyzed with the 56 EST-SSR and G-SSR primer pairs that were utilized. The accessions from South America, Yunnan (China), and Indonesia showed higher levels of genetic variation than the other two geographic regions (Grenada and Hainan, China). In particular, the collections from Yunnan (China) could be used to enrich the genetic background of J. curcas for breeding. Gst and Nm showed that some differentiation occurred in each geographic group.

Conclusion

We developed a set of 241 SSR primer pairs for use in J. curcas. Of these markers, 187 EST-SSRs should be useful for genetic mapping and quantitative trait loci analysis of important agronomic traits. Fifty-six EST-SSRs and G-SSRs were used successfully to analyze genetic diversity in 45 accessions of J. curcas. The accessions analyzed showed a broad genetic background with an average genetic diversity index of 0.5572. The intergroup genetic diversity was larger than the intragroup diversity, and domestication had taken place in both America and Hainan, China.

Methods

Plant material

Five J. curcas accessions, which corresponded to JI-1(Indonesia), JA-1(America), JG-1 (Grenada), JYC-1(Yunnan, China), and JHC-1(Hainan, China), were used to test the suitability of the cassava SSR markers by PCR. To analyze the genetic diversity of J. curcas, 45 accessions were chosen, nine from each of the five above-mentioned regions (Table 3). All the materials were obtained from the germplasm collection of the Institute of Tropical Biosciences & Biotechnology, Chinese Academy of Tropical Agricultural Sciences, Haikou, China. This collection contains 154 J. curcas accessions, which cover most of the Chinese collections as well as accessions from eight other countries: Brazil, Columbia, Grenada, Thailand, Indonesia, Laos, Myanmar, and Nigeria. Total genomic DNA was isolated from young leaves by the modified CTAB method as described by Doyle and Doyle [41]. The genomic DNA was diluted to a concentration of 25 ng/μl for PCR amplification.
Table 3

The J. curcas accessions and their origins

Code

District of collection

Code

District of collection

Code

District of collection

JI-1

Indonesia

JA-7

South America

JYC-4

Yunnan, China

JI-2

Indonesia

JA-8

South America

JYC-5

Yunnan, China

JI-3

Indonesia

JA-9

South America

JYC-6

Yunnan, China

JI-4

Indonesia

JG-1

Grenada

JYC-7

Yunnan, China

JI-5

Indonesia

JG-2

Grenada

JYC-8

Yunnan, China

JI-6

Indonesia

JG-3

Grenada

JYC-9

Yunnan, China

JI-7

Indonesia

JG-4

Grenada

JHC-1

Hainan, China

JI-8

Indonesia

JG-5

Grenada

JHC-2

Hainan, China

JI-9

Indonesia

JG-6

Grenada

JHC-3

Hainan, China

JA-1

South America

JG-7

Grenada

JHC-4

Hainan, China

JA-2

South America

JG-8

Grenada

JHC-5

Hainan, China

JA-3

South America

JG-9

Grenada

JHC-6

Hainan, China

JA-4

South America

JYC-1

Yunnan, China

JHC-7

Hainan, China

JA-5

South America

JYC-2

Yunnan, China

JHC-8

Hainan, China

JA-6

South America

JYC-3

Yunnan, China

JHC-9

Hainan, China

Design of EST-SSR and G-SSR primers and validation in J. curcas

A total of 419 EST-SSR primers that had been developed for cassava in our laboratory [42] and 182 G-SSR primers [43] were synthesized and used. Amplification by PCR was performed in a 20-μl reaction mixture that contained 50 ng template DNA, 1× PCR buffer (20 mM Tris pH 9.0, 100 mM KCl, 3.0 mM MgCl2), 400 μM of each of the four dNTPs, 0.4 μM of each of the forward and reverse primers, and one unit of Taq DNA polymerase. The following PCR conditions were used: 94°C for 1 min, followed by 35 cycles of 94°C for 1 min, 45-57°C for 1 min, 72°C for 1 min, and 10 min at 72°C for the final extension. PCR products were separated on a 2% agarose gel, and visualized using SYBR Green http://www.Genecopoeia.com.

Putative functional annotation of EST-SSRs

To assess the putative function of the EST-SSRs developed here, a BLASTX search of the GenBank nonredundant database http://www.ncbi.nlm.nih.gov/BLAST was performed using the 187 ESTs that contained polymorphic microsatellites. The threshold for a significant BLAST hit was set at a bit score greater than 80 bits. Functional categories were assigned on the basis of the BLAST searches using a specially formatted database of the Eukaryotic Orthologous Groups of proteins (KOG).

Analysis of genetic diversity

The polymorphic alleles obtained with each primer pair were scored for their presence (1) or absence (0). From the data matrix, a dendrogram was constructed using the unweighted pair group method using arithmetic averages (UPGMA), the similarity coefficient, and the software NTSYS-pc2.1 [44]. The binary data were also subjected to PCA to investigate the structure of our collection. The genetic diversity parameters of each geographic group, which included the percentage of polymorphic loci (PLP), observed number of alleles per locus (na), effective number of alleles per locus (ne), Nei's gene diversity (h), Shannon's information index (I), Gst, and Nm, were calculated by POPGENE 32 [45].

Declarations

Acknowledgements

This work was supported by the Cooperative Research Foundation of China and Brazil (2008KR0395) and the Basic Scientific Research Foundation of the Institute of Tropical Bioscience and Biotechnology, Chinese Academy of Tropical Agricultural Science (ITBBZX0843). The authors wish to thank M Fregene of the International Center for Tropical Agriculture (CIAT) for assistance with the development of the cassava G-SSR primers and Ruqiang Xu of the Virginia Commonwealth University for excellent suggestions and discussions about the paper. Finally, the authors wish to thank Cathel Kerr, Lucy Colegrove, and Chris Wright of Genedits http://www.genedits.com for editing.

Authors’ Affiliations

(1)
Institute of Tropical Biosciences & Biotechnology, Chinese Academy of Tropical Agricultural Science, Haikou, PR China
(2)
College of Agronomy, Hainan University, Danzhou, PR China

References

  1. Cano-Asseleih LM: Chemical investigation of Jatropha Curcas L. seeds. Ph.D. Thesis. 1986, University of London, UKGoogle Scholar
  2. Cano-Asseleih LM, Plumbly RA, Hylands PJ: Purification and partial characterization of the hemagglutination from seeds of Jatropha Curcas L. Jour Food Biochem. 1989, 13: 1-20. 10.1111/j.1745-4514.1989.tb00381.x.View ArticleGoogle Scholar
  3. Katwal RPS, Soni PL: Biofuels: an opportunity for socioeconomic development and cleaner environment. Indian Forester. 2003, 129: 939-949.Google Scholar
  4. Biofuels and Industrial Products from Jatropha curcas, Wink M, Koschmieder C, Sauerwein M, Sporer F: Phorbol esters of J. curcas-biological activities and potential applications. DBV Graz. Edited by: Gubitz GM, Mittelbach M, Trabi M. 1997, Biofuels and Industrial Products from Jatropha curcas, 160-166.Google Scholar
  5. Biofuels and Industrial Products from V HV, akkar HPS, Becker K: Potential of J. curcas seed meal as a protein supplement to livestock feed; constraints to its utilization and possible strategies to overcome constraints. DBV Graz. Edited by: Giibitz GM, Mittelbach M, Trabi M. 1997, Biofuels and Industrial Products from V HV, 190-205.Google Scholar
  6. Openshaw K: A review of Jatropha Curcas L: an oil plant of unfulfilled promise. Biomass Bioenergy. 2000, 19: 1-15. 10.1016/S0961-9534(00)00019-2.View ArticleGoogle Scholar
  7. Akintayo ET: Characteristics and composition of Parkia biglobbossa and Jatropha Curcas L. oils and cakes. Bio-resour Technol. 2004, 92: 307-310.View ArticleGoogle Scholar
  8. Fairless D: Biofuel: The little shrub that could maybe. Nature. 2007, 449: 652-655. 10.1038/449652a.PubMedView ArticleGoogle Scholar
  9. Divakara BN, Upadhyaya HD, Wani SP, Laxmipathi Gowda CL: Biology and genetic improvement of Jatropha curcas L.: A review. Applied Energy. 2010, 87: 732-742. 10.1016/j.apenergy.2009.07.013.View ArticleGoogle Scholar
  10. Ou WJ, Wang WQ, Li KM: Molecular Genetic Diversity Analysis of 120 Accessions Jatropha curcas L. Germplasm. Chinese Journal of Tropical Crops. 2009, 30: 287-292.Google Scholar
  11. Tatikonda Leela, Wani Suhas, Kannan Seetha: AFLP-based molecular characterization of an elite germplasm collection of Jatropha Curcas L. a biofuel plant. Plant Science. 2009, 176: 505-513. 10.1016/j.plantsci.2009.01.006.PubMedView ArticleGoogle Scholar
  12. Rajeev KV, Ralf S, Andreas B: Interspecific transferability and comparative mapping of barley EST-SSR markers in wheat, rye and rice. Plant Science. 2005, 168: 195-202. 10.1016/j.plantsci.2004.08.001.View ArticleGoogle Scholar
  13. Suwabe K, Tsukazaki H, Iketani H, Hatakeyama K, Kondo M, Fujimura M, Nunome T, Fukuoka H, Hirai M, Matsumoto S: Simple Sequence Repeat-Based Comparative Genomics Between Brassica rapa and Arabidopsis thaliana: The Genetic Origin of Clubroot Resistance. Genetics. 2006, 173: 309-319. 10.1534/genetics.104.038968.PubMed CentralPubMedView ArticleGoogle Scholar
  14. Wang YH, Xue YB, Li JY: Towards molecular breeding and improvement of rice in China. TRENDS in Plant Science. 2005, 10: 610-614. 10.1016/j.tplants.2005.10.008.PubMedView ArticleGoogle Scholar
  15. Xu YB, Susan RM, Zhang Q: How can we use genomics to improve cereals with rice as a reference genome. Plant Molecular Biology. 2005, 59: 7-26. 10.1007/s11103-004-4681-2.PubMedView ArticleGoogle Scholar
  16. Basha SD, George F, Makkar HPS, Becker K, Sujatha M: A comparative study of biochemical traits and molecular markers for assessment of genetic relationships between Jatropha Curcas L. germplasm from different countries. Plant Science. 2009, 176: 812-823. 10.1016/j.plantsci.2009.03.008.View ArticleGoogle Scholar
  17. Basha SD, Sujatha M: Inter and intra-population variability of Jatropha curcas(L.) characterized by RAPD and ISSR markers and development of population-specific SCAR markers. Euphytica. 2007, 156: 375-386. 10.1007/s10681-007-9387-5.View ArticleGoogle Scholar
  18. Ganesh Ram S, Parthiban KT, Senthil Kumar R, Thiruvengadam V, Paramathma M: Genetic diversity among Jatropha species as revealed by RAPD markers. Genet Resour Crop Evol. 2008, 55: 803-809. 10.1007/s10722-007-9285-7.View ArticleGoogle Scholar
  19. Sudheer Pamidimarri DVN, Sweta S, Shaik GM, Jalpa P, Muppala PR: Molecular characterization and identification of markers for toxic and non-toxic varieties of Jatropha curcas L. using RAPD, AFLP and SSR markers. Molecular Biology Reports. 2008, 36: 1357-1364. 10.1007/s11033-008-9320-6.PubMedView ArticleGoogle Scholar
  20. Cordeiro GM, Casu R, McIntyre CL, Manners JM, Henry RJ: Microsatellite markers from sugarcane (Saccharum spp.) ESTs cross transferable to erianthus and sorghum. Plant Science. 2001, 160: 1115-1123. 10.1016/S0168-9452(01)00365-X.PubMedView ArticleGoogle Scholar
  21. Thiel T, Michalek W, Varshney RK, Graner A: Exploiting EST databases for the development of cDNA derived microsatellite markers in barley (Hordeum vulgare L.). Theor Appl Genet. 2003, 106: 411-422.PubMedGoogle Scholar
  22. Gupta PK, Rustgi S, Sharma S, Singh R, Kumar N, Balyan HS: Transferable EST-SSR markers for the study of polymorphism and genetic diversity in bread wheat. Mol Gen Genomics. 2003, 270: 315-323. 10.1007/s00438-003-0921-4.View ArticleGoogle Scholar
  23. Bory S, Silva DD, Risterucci AM: Development of microsatellite markers in cultivated vanilla: Polymorphism and transferability to other vanilla species. Scientia Horticulturae. 2008, 115: 420-425. 10.1016/j.scienta.2007.10.020.View ArticleGoogle Scholar
  24. Wunsch A: Cross-transferable polymorphic SSR loci in Prunus species. Scientia Horticulturae. 2009, 120: 348-352. 10.1016/j.scienta.2008.11.012.View ArticleGoogle Scholar
  25. Zhang LY, Bernard M, Leroy P: Hight transferability of bread wheat EST-derived SSRs to other cereals. Theor Appl Genet. 2005, 111: 677-687. 10.1007/s00122-005-2041-5.PubMedView ArticleGoogle Scholar
  26. Roder MS, Korzun V, Wendehake K, Plaschke J, Tixier MH, Leroy P, Ganal MW: A microsatellite map of wheat. Genetics. 1998, 149: 2007-2023.PubMed CentralPubMedGoogle Scholar
  27. Varshney RK, Kumar A, Balyan HS, Roy JK, Prasad M, Gupta PK: Characterization of microsatellites and development of chromosome specific STMS markers in bread wheat. Plant Mol Biol Rep. 2000, 18: 1-12. 10.1007/BF02825285.View ArticleGoogle Scholar
  28. Sourdille P, Tavaud M, Charmet G, Bernard M: Transferability of wheat microsatellites to diploid Triticeae species carrying the A, B and D genomes. Theor Appl Genet. 2001, 103: 346-352. 10.1007/s00122-001-0542-4.View ArticleGoogle Scholar
  29. Tahan O, Geng YP, Zeng LY: Assessment of genetic diversity and population structure of Chinese wild almond, Amygdalus nana, using EST- and genomic SSRs. Biochemical Systematics and Ecology. 2009, 37: 146-153. 10.1016/j.bse.2009.02.006.View ArticleGoogle Scholar
  30. Li LZ, Wang JJ, Guo Y: Development of SSR markers from ESTs of gramineous species and their chromosome location on wheat. Progress in Natural Science. 2008, 18: 1485-1490. 10.1016/j.pnsc.2008.05.012.View ArticleGoogle Scholar
  31. Gao LF, Tang JF, Li HW: Analysis of microsatellites in major crops assessed by computational and experimental approaches. Mol Breed. 2003, 12: 245-261. 10.1023/A:1026346121217.View ArticleGoogle Scholar
  32. Chen HM, Li LZ, Wei XY: Development, chromosome location and genetic mapping of EST-SSR markers in wheat. Chin Sci Bull. 2005, 50: 2328-2336. 10.1360/982005-379.View ArticleGoogle Scholar
  33. Metzgar D, Bytof J, Wills C: Selection against frameshift mutations limits microsatellite expansion in coding DNA. Genome Res. 2000, 10: 72-80.PubMed CentralPubMedGoogle Scholar
  34. Wang D, Liao XL, Cheng L: Development of novel EST-SSR markers in common carp by data mining from public EST sequences. Aquaculture. 2007, 271: 558-574. 10.1016/j.aquaculture.2007.06.001.View ArticleGoogle Scholar
  35. Chen CX, Zhou P, Choi YA, Huang S, Gmitter FG: Mining and characterizing microsatellites from citrus ESTs. Theor Appl Genet. 2006, 112: 1248-1257. 10.1007/s00122-006-0226-1.PubMedView ArticleGoogle Scholar
  36. Kantety RV, Rota ML, Matthews DE, Sorrells ME: Data mining for simple sequence repeats in expressed sequence tags from barley, maize, rice, sorghum and wheat. Plant Mol Biol. 2002, 48: 501-510. 10.1023/A:1014875206165.PubMedView ArticleGoogle Scholar
  37. Vieira RF: Conservation of medicinal and aromatic plants in Brazil. Perspectives on new crops and new uses. Edited by: Janick J. 1999, ASHS Press, Alexandria, VA, 152-159.Google Scholar
  38. Sunil N, Varaprasad KS, Sivaraj N, Suresh Kumar T, Abraham B, Prasad RBN: Assessing Jatropha curcas L. germplasm in-situ-A case study. Biomass and Bioenergy. 2008, 32: 198-202. 10.1016/j.biombioe.2007.09.003.View ArticleGoogle Scholar
  39. Sun QB, Li LF, Li Y, Wu GJ, Ge XJ: SSR and AFLP Markers Reveal Low Genetic Diversity in the Biofuel Plant Jatropha curcas in China. Crop Sci. 2008, 48: 1865-1871. 10.2135/cropsci2008.02.0074.View ArticleGoogle Scholar
  40. Linden Vander CG, Wouters DC, Mihalka V, Kochieva EZ, Smulders MJM, Vosman B: Efficient targeting of plant disease resistance loci using NBS profiling. Theor Appl Genet. 2004, 109: 384-393. 10.1007/s00122-004-1642-8.PubMedView ArticleGoogle Scholar
  41. Doyle JJ, Doyle JL: A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem Bull. 1987, 19: 11-15.Google Scholar
  42. Zou ML, Ling P, Zhang Y, Wei ZS, Xia ZQ, Wang WQ: Mining EST derived SSR markers and use for genetic diversity evaluation in cassava (Manihot esculenta Crantz).Google Scholar
  43. Mba REC, Stephenson P, Edwards K, Melzer S, Nkumbira J, Gullberg U, Apel K, Gale M, Tohme J, Fregene M: Simple sequence repeat (SSR) markers survey of the cassava (Manihot esculenta Crantz) genome: towards an SSR-based molecular genetic map of cassava. Theor Appl Genet. 2001, 102: 21-31. 10.1007/s001220051614.View ArticleGoogle Scholar
  44. Rohlf FJ, NTSYS-pc: numerical taxonomy system ver.2.1. 2002, Exeter Publishing Ltd., Setauket, New YorkGoogle Scholar
  45. Yeh FC, Yang R, Boyle TJ, Ye Z, Xiyan JM: PopGene32, Microsoft Windows-based Freeware for Population Genetic Analysis, Version 1.32. 2000, Molecular Biology and Biotechnology Centre, University of Alberta, Edmonton, Alberta, CanadaGoogle Scholar

Copyright

© Wang et al; licensee BioMed Central Ltd. 2010

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Advertisement

BMC Research Notes

ISSN: 1756-0500

Contact us