Identification and validation of SNP markers linked to seed toxicity in Jatropha curcas L


Edible/non-toxic varieties of Jatropha curcas L. are gaining increasing attention, providing both oil as biofuel feedstock or even as edible oil and the seed kernel meal as animal feed ingredient. They are a viable alternative to the limitation posed by the presence of phorbol esters in toxic varieties. Accurate genotyping of toxic/non-toxic accessions is critical to breeding management. The aim of this study was to identify SNP markers linked to seed toxicity in J. curcas. For SNP discovery, NGS technology was used to sequence the whole genomes of a toxic and non-toxic parent along with a bulk of 51 toxic and 30 non-toxic F2 plants. To ascertain the association between SNP markers and seed toxicity trait, candidate SNPs were genotyped on 672 individuals segregating for seed toxicity and two collections of J. curcas composed of 96 individuals each. In silico SNP discovery approaches led to the identification of 64 candidate SNPs discriminating non-toxic and toxic samples. These SNPs were mapped on Chromosome 8 within the Linkage Group 8 previously identified as a genomic region important for phorbol ester biosynthesis. The association study identified two new SNPs, SNP_J22 and SNP_J24 significantly linked to low toxicity with R2 values of 0.75 and 0.54, respectively. Our study released two valuable SNP markers for high-throughput, marker-assisted breeding of seed toxicity in J. curcas.


Jatropha curcas L. is being fostered as a sustainable source of bioenergy and food. The most valuable parts of the plant are the kernels containing high amounts of oil and protein suitable for creating a range of beneficial products1. However, the phorbol esters in the toxic varieties of Jatropha render the most important byproduct of the biofuel extraction, the seed kernel meal, unsuitable for consumption. The adverse effects of toxicity have been firmly established on microorganisms to higher animals using extracts from fruit, seed, oil, roots, latex, bark, and leaves2. This affects the benefits of the plant negatively.

The identification of non-toxic edible Jatropha varieties presents a more suitable source of oil and animal feed ingredients3. An interesting study compared the two varieties for growth pattern, pest incidence and seed productivity4. It has been confirmed that variation in edibility of Jatropha seeds is due to a single trait i.e. the presence of phorbol esters. The content of other anti-nutrients such as curcin in the seeds of toxic and edible J. curcas remain unchanged3,5. Another study showed negligible waste production from Mexican non-toxic variety with use of both biodiesel and de-oiled seed cake as a protein and carbohydrate source6. Commercial breeders also value the generation of non-toxic accessions bypassing the chemical detoxification of the toxic varieties. Nevertheless, the seed characteristic is the single most determinant of the target market. This necessitates molecular markers strongly distinguishing the two varieties to aid in breeding. Efforts in this direction include generation of SCAR markers specific for toxic and non-toxic plants7. Studies have identified seven polymorphic microsatellite markers using RAPD and AFLP techniques8. Another group identified microsatellite markers classifying non-toxic and toxic J. curcas correlating them to the phorbol ester (PE) levels9. The generation of the first genetic map using SNP markers and also the identification of a locus for phorbol ester biosynthesis has been extremely valuable10. In addition, a high-resolution linkage map has recently been made available aiding in the mapping of important agronomic traits11.

More recently, SNP based markers are dominating the molecular breeding field due to i) their abundance in plant genomes ii) high-throughput detection in NGS and genotyping platforms iii) lower false positives due to bi-allelic nature iv) availability of a range of computational pipelines for SNP calling v) clear read-off in haplotypes from inbred lines12,13,14. Their wide spread application in the breeding of model plants has been well documented15,16,17,18. They continue to be the marker of choice for candidate gene identification, trait discrimination and diversity analysis. In the past decade, with the availability of affordable and high-throughput technologies such as NGS, the same approach has been applied to whole genomes of non-model plants19,20,21.

The objective of the present study was to identify SNP markers linked to seed toxicity in J. curcas. A thorough experimental design for sequencing (toxic JPT-86 parent, non-toxic JPNT-2 parent and F2 Jatropha plants from crossing JPNT-2 X JPT-86) enabled the identification of putative SNP markers governing seed toxicity. The results reported in this study demonstrate the first use of OpenArray technology for rapid screening of candidate SNPs in J. curcas along with PyroMark assays for accurate SNP identification. The reliability of SNPs from the associations study have been verified on two independent datasets. These SNP markers, SNP_J22 and SNP_J24 as well as the genotyping assay will be beneficial to the breeding of J. curcas non-toxic varieties.



Figure 1 shows the phorbol ester content (averaged over duplicates) in 249 plants from the F2 population segregating for seed toxicity and used for association study. The estimation of phorbol ester content by HPLC allowed for categorization of the plants as toxic and non-toxic. A level of 0.1 mg/g has been routinely considered to be the upper limit of phorbol ester levels beyond which test animals start rejecting diets containing toxic jatropha seeds4. The method is also not sensitive below the mentioned level of phorbol esters.

Figure 1

Scatter plot of log(PE)/phorbol ester (PE) content vs toxicity status in F2 population segregating for seed toxicity and generated by crossing a high yielding, early maturing, non-toxic edible accession from Mexico (JPNT-2) with a toxic, non-edible accession from India (JPT-86).

SNP discovery and mapping

SNP analysis from whole genome sequencing of parental DNA revealed a total of 6,248 homozygous SNPs between the two parental lines of J. curcas. Comparative analysis of the SNP data between toxic and non-toxic segregating bulks for the 6,248 homozygous positions yielded a total of 64 SNPs. These SNPs that clearly differentiated the two bulks were further considered for experimental validation. The SNP markers mapped on 41 Kazusa scaffolds identifying putative genomic candidate sequences linked to the gene(s) influencing toxicity in J. curcas (Supplementary Material S2). The 64 candidate SNPs mapped onto 11 scaffolds of the genome provided by Wu et al. (2015), specifically between 10 to 15 Mbp on the upper arm of chromosome 8 (Fig. 2). In this study, Kazusa genome (JAT_r4.5) was used as reference genome and the SNP analysis revealed that the toxic parental line sequenced here contains 6,057 SNPs (97%) matching the reference (Kazusa) allele and only 191 SNPs (3%) had the alternative allele.

Figure 2

Physical positions of 64 toxicity related SNPs on Wu et al. scaffolds (2015).

Association study

The collection of 672 plants comprising toxic and non-toxic plants were genotyped with a panel of 64 SNPs using OpenArray technology. TaqMan primers and probes sequences are available in Supplementary Material S3. Allelic discrimination plots obtained from screening the 64 candidate SNPs on OpenArray platform is shown in Fig. 3. The three distinct clusters are representative of the samples presenting three different genotypes. The OpenArray genotyping plates showed a genotyping call average success rate of 93%. The association between three SNP genotypes and phorbol ester content was tested by fitting one SNP at a time in a linear regression model. Regression analysis allowed the identification of two SNPs, SNP_J22 and SNP_J24 significantly linked to low toxicity with R2 values of 0.75 and 0.54, respectively (Fig. 4). The physical position of these highly significant SNPs, SNP_J22 and SNP_J24 are on the same scaffold Jcr4S00944 of the Kazusa genome (JAT_r4.5) at positions 3,558 bp and 25,943 bp respectively. The same SNPs were mapped to scaffold297 of Wu et al.’s genome at precise locations 184,697 bp for SNP_J22 and 207,326 bp for SNP_J24.

Figure 3

Allelic discrimination plots of 64 candidate SNPs genotyped on J. curcas samples using OpenArray technology.

Figure 4

Effect of SNP_J22 and SNP_J24 allelic status on phorbol ester content. The homozygous T/T genotype for SNP_J22 and A/A genotype for SNP_J24 is determining the lowest phorbol ester contents in evaluated samples.

In addition, to F2 population, a set of toxic and non-toxic accessions (Supplementary Material S1) was used to further ascertain the association between toxicity trait and markers identified in this study. Pyrosequencing Allele Quantification assays were designed to detect SNP_J22 and SNP_J24 on large segregating populations for seed toxicity. Sequences of primers for pyrosequencing assays are available in Supplementary Material S4. Representative pyrograms indicating the polymorphism in the SNP_J22 and SNP_J24 markers are presented in Fig. 5. Signals from the analyzed DNA sequence are represented as peaks in the pyrogram, corresponding to the number of identical nucleotides incorporated (Fig. 5). The association between the two SNPs and low toxicity character was also evaluated and confirmed using chi-square statistic (Table 1).

Figure 5

Pyrograms showing polymorphisms in the SNP_J22 and SNP_J24 markers obtained with PyroMark Allelic Quantification Assays.

Table 1 Allele frequencies of SNP_J22 and SNP_J24 from pyrosequencing allelic quantification tested in two validation datasets.


Jatropha curcas is an extraordinary industrial crop with compelling uses. Mismanagement of its valued byproducts is regarded unsustainable. Further, plantations of non-toxic J. curcas potentially increase the revenue by 25% compared to toxic varieties (Jatropower AG, Switzerland). Phorbol ester remains a crucial trait for research and in the assignment of cultivars to specific target markets. Therefore, methodologies allowing identification and improvement of non-toxic J. curcas is pivotal to its breeding and domestication.

The availability of existing genomic data in Jatropha22,23 along with a comprehensive experimental design and a relatively simple genome made the genome wide discovery of SNP markers related to toxicity straightforward. Comparative studies between the two genomes identified the cultivar sequenced by the Kazusa DNA Research Institute to be similar to a toxic line. This information is also beneficial to the breeders working on genetic improvement of this particular cultivar.

SNP discovery from whole genome sequencing revealed 64 candidate SNPs in Linkage group 8, a locus previously identified for phorbol ester biosynthesis and toxicity related genes10. The sequences of the 64 SNPs linked to toxicity are provided in Supplementary Material S2 for further analyses. Recently, an updated assembly with linkage map was made available11. The localization of all 64 SNPs in the upper arm of chromosome 8 suggests the identification of a genomic region that regulates toxicity. However, due to the limited recombination events expected in a F2 population and number of plants tested, the candidate genomic region still spans several kilo base pairs. Furthermore, correlating the SNPs with predicted genes can shed light on putative biosynthetic pathways influencing the toxicity trait.

The basis of association analysis is screening of large number of SNPs on large number of samples and thus, the use of OpenArray technology in this study is pertinent. This technology offers several advantages as demonstrated in this study: (i) low cost of € 0.07 per sample, (ii) accurate genotyping with an average call rate of 93% and (iii) rapid genotyping allowing generation of 36,864 data points in a day. SNP_J22 and SNP_J24 obtained from the association study are a fundamental step in the introduction and cultivation of non-toxic J. curcas accessions. This study is the first demonstration of SNP screening using OpenArray platform in J. curcas. The SNPs present a high correlation with the low phorbol ester content. The reproducibility of results using this tool has also been confirmed in prior studies24,25. The success of an association study is realized through translation of identified SNPs for routine genotyping in breeding. This process involves testing thousands of samples for an extremely critical trait like toxicity, with high confidence using as few characteristic markers. Pyrosequencing has earlier been shown as an appropriate method for SNP analysis where allele frequency is 50% and when the nucleotide variants are clearly known26.

Our study confirms the accuracy and reliability of allelic quantification accomplished with PyroMark. The assay design is simple and also allows the sequencing of bases around the SNP. This gives more information about the PCR product analysed and its authentication27. It allows allelic quantification at a cost of € 12 per assay. The economic feasibility is apt considering the widespread cultivation of J. curcas in countries with limited fiscal capacity. Further, the results of this study were collated from validation on samples representing diverse cultivated and wild germplasms. Recent studies have shown the wide genetic variability specially in the germplasms lacking phorbol esters among accessions from Mexico and Central America28. These germplasms could be segregating for multiple traits including toxicity. Therefore, an accurate SNP detection step subsume characterization of accessions and fixation of lines. In our study, Pyrosequencing technology clearly allowed separation of the true homozygotes from heterozygotes. The above reasons detailed enhance the applicability of Pyrosequencing in breeding. To our knowledge, this is the first research adopting allelic quantification by Pyrosequencing in J. curcas.

SNP markers along with the modern genomics approaches and NGS technologies accelerate the improvements in molecular-genomics-assisted plant breeding. As shown in the present study, these SNPs can be combined with other known SNPs10 related to toxicity with an aim to (i) create a SNP panel sanctioning the discrimination of non-toxic varieties in a mixed pool (ii) check contamination prior to usage as animal feed and (iii) fingerprint samples for identity and diversity analysis. A handful of SNP markers can significantly improve the accuracy and efficiency of trait selection in germplasm management19,29.

Material and Methods

Plant materials

An F2 population segregating for seed toxicity trait was generated by crossing a high yielding, early maturing, non-toxic edible accession originating from Mexico (JPNT-2) with a toxic, non-edible accession from India (JPT-86). In addition to the F2 population, a set of toxic and non-toxic accessions from various countries were used to further ascertain the association between toxicity trait and markers identified in this study. A list of accessions used in this study with their country of origin and toxicity status has been provided in Supplementary Material S1. All plant materials were raised at an experimental farm near Coimbatore, Tamil Nadu state, India (Latitude: 10.764972°; Longitude: 79.737439°) at a plant to plant and row to row spacing of 2.0 m each.


Seeds from the F2 population of plants were used for extraction and quantification of phorbol ester content by high performance liquid chromatography (HPLC). Each plant was analyzed in duplicate. The results are expressed as equivalent to a standard phorbol-12-myristate13-acetate in milligrams per gram (mg/g). HPLC conditions for estimation of phorbol ester content was carried out in accordance with previously described research30,31. An average phorbol ester cut off of 0.1 mg/g was used to categorize toxic plants from non-toxic plants4.

DNA isolation

Leaves from the parents and F2 progeny was used to extract DNA. This was performed on the BioSprint 96 platform (Qiagen) using the BioSprint 96 DNA Plant Kit (Qiagen) following the method described in our previous study32. DNA of 51 toxic samples, 30 non-toxic individual samples, 1 toxic parent and 1 non-toxic parent were used downstream for sequencing.

Library preparation for whole genome sequencing

Sequencing libraries were prepared by using TruSeq DNA HT Kit (cat# FC-121-2003, Illumina). The standard protocol recommended by the company was followed for library preparation. The quality and molarity of the libraries was evaluated by TapeStation (Agilent Technologies). Bulks were composed of 51 and 30 normalized DNA samples for the toxic and non-toxic bulks, respectively. Parental DNA samples were barcoded individually. All the samples were sequenced on the HiSeq2500 system (Illumina) using paired-read technology of 100 bp.

SNP Identification and mapping analysis

All the sequenced reads were checked for their quality using FastQC v0.11.433. The sequenced reads were then aligned to the reference genome from Kazusa DNA Research Institute (Version 4.5; using bowtie2 v2.2.1-034 and the alignment files were further processed with SAMtools v1.435. PCR duplicates were marked using Picard tools v2.636. Variant calling was done using bcftools call v1.437. SNP identification was done considering the following four groups: toxic parental; non-toxic parental; toxic bulk and non-toxic bulk. Only homozygous SNPs present with different allelic status between the parents were selected. For bulk segregant analysis, among the selected SNPs only those segregating with the parental non-toxic allele frequency >0.95 in the non-toxic bulk (expected = 1.00) and <0.50 in the toxic bulk (expected = 0.33) were selected. For the SNPs segregating between the bulks, which are the candidate SNPs linked to the gene(s) of interest, sequences harboring the SNPs were mapped against Kasuza scaffolds (Version 4.5; and the newly developed draft genome by Wu et al.11 to identify their genomic locations using BLAST38. The physical location of toxicity related SNPs on Wu et al.’s genome was visualized using a customized R script.

Association study

For association study, a collection of 672 F2 plants comprising toxic and non-toxic plants were genotyped with discovered SNPs using the Quant Studio 12 K Flex Real Time PCR System and OpenArray platform (Life Technologies). Sample preparation and loading methods were performed following the method described previously39. Selected SNPs showing best discrimination were further genotyped using PyroMark Q48 Advance Reagents on the PyroMark Q48 (Qiagen) on a collection of 602 F2 plants and 96 plants of diverse germplasm. PCR and sequencing primers were developed using the PyroMark Assay Design Software (Qiagen). The following thermal cycling conditions were used: 15 min at 95 °C, followed by 45 cycles of 30 s at 94 °C, 30 s at 60 °C, 30 s at 72 °C and 10 min at 72 °C. Sequence analysis was performed using the PyroMark Q48 Autoprep software (Version 2.4.2) in the Allele Quantification mode. The association between three SNP genotypes and toxicity character was tested by fitting one SNP at a time in a linear regression model. The model fit between each SNP genotype and phorbol ester content was then estimated using the program Statistica v.13.0 (Statsoft, Dell).


  1. 1.

    Montes, J. M. & Melchinger, A. E. Domestication and breeding of Jatropha curcas L. Trends Plant Sci. 21, 1045–1057 (2016).

    CAS  Article  Google Scholar 

  2. 2.

    Devappa, R. K., Makkar, H. P. S. & Becker, K. Jatropha toxicity–a review. J Toxicol Environ Health B Crit Rev 13, 476–507 (2010).

    CAS  Article  Google Scholar 

  3. 3.

    King, A. J. et al. Potential of Jatropha curcas as a source of renewable oil and animal feed. J. Exp. Bot. 60, 2897–2905 (2009).

    CAS  Article  Google Scholar 

  4. 4.

    Francis, G., Oliver, J. & Sujatha, M. Non-toxic jatropha plants as a potential multipurpose multi-use oilseed crop. Industrial Crops and Products 42, 397–401 (2013).

    Article  Google Scholar 

  5. 5.

    He, W. et al. Analysis of seed phorbol-ester and curcin content together with genetic diversity in multiple provenances of Jatropha curcas L. from Madagascar and Mexico. Plant Physiol. Biochem. 49, 1183–1190 (2011).

    CAS  Article  Google Scholar 

  6. 6.

    Sánchez-Arreola, E. et al. Biodiesel production and de-oiled seed cake nutritional values of a Mexican edible Jatropha curcas. Renewable Energy 76, 143–147 (2015).

    Article  Google Scholar 

  7. 7.

    Mastan, S. G., Sudheer, P. D. V. N., Rahman, H., Reddy, M. P. & Chikara, J. Development of SCAR marker specific to non-toxic Jatropha curcas L. and designing a novel multiplexing PCR along with nrDNA ITS primers to circumvent the false negative detection. Mol. Biotechnol. 50, 57–61 (2012).

    CAS  Article  Google Scholar 

  8. 8.

    Sudheer Pamidimarri, D. V. N., Singh, S., Mastan, S. G., Patel, J. & Reddy, M. P. 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 (2009).

    CAS  Article  Google Scholar 

  9. 9.

    Tanya, P., Dachapak, S., Tar, M. M. & Srinives, P. New microsatellite markers classifying nontoxic and toxic Jatropha curcas. J. Genet. 90, e76–78 (2011).

    PubMed  Google Scholar 

  10. 10.

    King, A. J. et al. Linkage mapping in the oilseed crop Jatropha curcas L. reveals a locus controlling the biosynthesis of phorbol esters which cause seed toxicity. Plant Biotechnol. J. 11, 986–996 (2013).

    ADS  CAS  Article  Google Scholar 

  11. 11.

    Wu, P. et al. Integrated genome sequence and linkage map of physic nut (Jatropha curcas L.), a biodiesel plant. Plant J. 81, 810–821 (2015).

    CAS  Article  Google Scholar 

  12. 12.

    Rafalski, A. Applications of single nucleotide polymorphisms in crop genetics. Curr. Opin. Plant Biol. 5, 94–100 (2002).

    CAS  Article  Google Scholar 

  13. 13.

    Mammadov, J., Aggarwal, R., Buyyarapu, R. & Kumpatla, S. SNP markers and their impact on plant breeding. Int J Plant Genomics 2012, 728398 (2012).

    Article  Google Scholar 

  14. 14.

    Manivannan, A. et al. Next-generation sequencing approaches in genome-wide discovery of single nucleotide polymorphism markers associated with pungency and disease resistance in pepper. Biomed Res Int 2018, 5646213 (2018).

    Article  Google Scholar 

  15. 15.

    Ching, A. et al. SNP frequency, haplotype structure and linkage disequilibrium in elite maize inbred lines. BMC Genet. 3, 19 (2002).

    Article  Google Scholar 

  16. 16.

    Close, T. J. et al. Development and implementation of high-throughput SNP genotyping in barley. BMC Genomics 10, 582 (2009).

    Article  Google Scholar 

  17. 17.

    Atwell, S. et al. Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature 465, 627–631 (2010).

    ADS  CAS  Article  Google Scholar 

  18. 18.

    Xu, X. et al. Resequencing 50 accessions of cultivated and wild rice yields markers for identifying agronomically important genes. Nat. Biotechnol. 30, 105–111 (2011).

    CAS  Article  Google Scholar 

  19. 19.

    Wang, B. et al. Developing single nucleotide polymorphism (SNP) markers from transcriptome sequences for identification of longan (Dimocarpus longan) germplasm. Hortic Res 2, 14065 (2015).

    Article  Google Scholar 

  20. 20.

    Ye, Y. et al. Identification and validation of SNP Markers lLinked to dwarf traits using SLAF-Seq technology in Lagerstroemia. PLoS ONE 11, e0158970 (2016).

    Article  Google Scholar 

  21. 21.

    Liao, Z., Wan, Q., Shang, X. & Su, J. Large-scale SNP screenings identify markers linked with GCRV resistant traits through transcriptomes of individuals and cell lines in Ctenopharyngodon idella. Sci Rep 7, 1184 (2017).

    ADS  Article  Google Scholar 

  22. 22.

    Sato, S. et al. Sequence analysis of the genome of an oil-bearing tree, Jatropha curcas L. DNA Res. 18, 65–76 (2011).

    CAS  Article  Google Scholar 

  23. 23.

    Hirakawa, H. et al. Upgraded genomic information of Jatropha curcas L. Plant. Biotechnology 29, 123–130 (2012).

    CAS  Google Scholar 

  24. 24.

    Patel, S. N. et al. TaqMan® OpenArray® high-throughput transcriptional analysis of human embryonic and induced pluripotent stem cells. Methods Mol. Biol. 997, 191–201 (2013).

    CAS  Article  Google Scholar 

  25. 25.

    Martins, F. T. A., Ramos, P. Z., Svidnicki, M. C. C. M., Castilho, A. M. & Sartorato, E. L. Optimization of simultaneous screening of the main mutations involved in non-syndromic deafness using the TaqMan® OpenArrayTM Genotyping platform. BMC Med. Genet. 14, 112 (2013).

    Article  Google Scholar 

  26. 26.

    Sivertsson, A., Platz, A., Hansson, J. & Lundeberg, J. Pyrosequencing as an alternative to single-strand conformation polymorphism analysis for detection of N-ras mutations in human melanoma metastases. Clin. Chem. 48, 2164–2170 (2002).

    CAS  PubMed  Google Scholar 

  27. 27.

    Pruvost, M., Reissmann, M., Benecke, N. & Ludwig, A. From genes to phenotypes - evaluation of two methods for the SNP analysis in archaeological remains: pyrosequencing and competitive allele specific PCR (KASPar). Ann. Anat. 194, 74–81 (2012).

    CAS  Article  Google Scholar 

  28. 28.

    Montes Osorio, L. R. et al. High level of molecular and phenotypic biodiversity in Jatropha curcas from Central America compared to Africa, Asia and South America. BMC Plant Biol. 14, 77 (2014).

    Article  Google Scholar 

  29. 29.

    Jatropha, Challenges for a New Energy Crop: Volume 2: Genetic Improvement and Biotechnology. (Springer-Verlag, 2013).

  30. 30.

    Martinez-Herrera, J., Siddhuraju, P., Francis, G., Dávila-Ortíz, G. & Becker, K. Chemical composition, toxic/antimetabolic constituents, and effects of different treatments on their levels, in four provenances of Jatropha curcas L. from. Mexico. Food Chem 80–89 (2006).

  31. 31.

    Makkar, H. P. S., Aderibigbe, A. O. & Becker, K. Comparative evaluation of non-toxic and toxic varieties of Jatropha curcas for chemical composition, digestibility, protein degradability and toxic factors. Food Chemistry 62, 207–215 (1998).

    CAS  Article  Google Scholar 

  32. 32.

    Stevanato, P. et al. Identification and validation of a SNP marker linked to the gene HsBvm-1 for nematode resistance in sugar beet. Plant Mol Biol Rep 33, 474–479 (2015).

    CAS  Article  Google Scholar 

  33. 33.

    Babraham Bioinformatics - FastQC A quality control tool for high throughput sequence data. Available at, (Accessed: 5th May 2019).

  34. 34.

    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    CAS  Article  Google Scholar 

  35. 35.

    Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  Google Scholar 

  36. 36.

    Picard Tools - By Broad Institute. Available at,, (Accessed: 5th May 2019)

  37. 37.

    Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987–2993 (2011).

    CAS  Article  Google Scholar 

  38. 38.

    Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).

    CAS  Article  Google Scholar 

  39. 39.

    Broccanello, C. et al. A SNP mutation affects rhizomania-virus content of sugar beets grown on resistance-breaking soils. Euphytica 214, 14 (2018).

    Article  Google Scholar 

Download references


This research was supported by grants from Jatropower AG, Switzerland.

Author information




D.T., S.R., C.B. and C.C. collected the data and wrote the manuscript. S.S. assisted with bioinformatics and revision of the text. G.F., M.S., J.O. and P.S. performed data analysis and provided critical revision of the manuscript. All authors approved the final version for publication.

Corresponding author

Correspondence to Piergiorgio Stevanato.

Ethics declarations

Competing Interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Trebbi, D., Ravi, S., Broccanello, C. et al. Identification and validation of SNP markers linked to seed toxicity in Jatropha curcas L. Sci Rep 9, 10220 (2019).

Download citation

Further reading


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.