Skip to main content
Log in

Development of a set of SSR markers for characterization of Indian mustard germplasm and varieties

  • Original Article
  • Published:
Journal of Plant Biochemistry and Biotechnology Aims and scope Submit manuscript

Abstract

Simple sequence repeat (SSR) markers can serve as an important tool for characterization and genetic diversity evaluation in Indian mustard germplasm and varieties. For DNA fingerprinting of various Indian mustard varieties, it is necessary that a common set of SSRs be used by various laboratories so that the developed genetic profiles of various genotypes can be compared with one another. In this study, we evaluated the polymorphic potential of 350 SSR markers to derive a set of SSR markers for characterization of Indian mustard germplasm and varieties. Out of a total of 350 SSR markers evaluated, 310 (88.57%) SSRs produced polymorphic amplicons, while remaining 40 (11.43%) SSRs resulted into monomorphic products. The allele number varied from 2 to 7 with 3.22 average number of alleles per locus. Polymorphism information content (PIC) value ranged from 0.24 (Ol09A01) to 0.75 (nia-m141a) with an average PIC value of 0.40 per locus. A total of 95 (31%) SSR markers evaluated were having PIC values more than the average PIC value, which constitute the representative set of SSR markers. Unweighted pair group method with arithmetic averages (UPGMA)-dendrogram grouped all the 46 genotypes into two main clusters, while STRUCTURE analysis formed three subpopulations having admixture of alleles. This SSR marker set will facilitate systematic characterization and classification of various Indian mustard germplasm accessions and varieties.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig.3
Fig. 4

Similar content being viewed by others

Availability of data and material

All the data and material is available with the authors.

Abbreviations

SSR:

Simple sequence repeat

ISSR:

Inter simple sequence repeat

RAPD:

Random amplified polymorphic DNA

RFLP:

Restriction fragment length polymorphism

AFLP:

Amplified fragment length polymorphism

PIC:

Polymorphic information content

UPGMA:

Unweighted pair group method with arithmetic averages

References

  • Botstein D, White RL, Skolnick M, Davis RW (1980) Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am J Hum Genet 32:314–331

    CAS  PubMed  PubMed Central  Google Scholar 

  • Cheng F, Wu J, Wang X (2014) Genome triplication drove the diversification of Brassica plants. Hort Res 1:14024. https://doi.org/10.1038/hortres.2014.2

    Article  Google Scholar 

  • Dettori MT, Micali S, Giovinazzi J, Scalabrin S, Verde I, Cipriani G (2015) Mining microsatellites in the peach genome: development of new long-core SSR markers for genetic analyses in five Prunus species. Springerplus 4:337. https://doi.org/10.1186/s40064-015-1098-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Earldent A, Von Holdt Bridgett M (2012) STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Cons Genet Resour 4(359–361):224

    Google Scholar 

  • Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software Structure: a simulation study. Mol Ecol 14:2611–2620

    Article  CAS  Google Scholar 

  • Govt. of India (2018) Agricultural statistics at a glance. Directorate of Economics and Statistics, Department of Agriculture and Cooperation, Ministry of Agriculture, Govt. of India, New Delhi. http://cands.daenet.nic.in/PDF

  • Li P, Su T, Wang H, Zhao X, Wang W, Yu Y, Zhang D, Wen C, Yu S, Zhang F (2019) Development of a core set of KASP markers for assaying genetic diversity in Brassica rapa subsp. chinensis Makino. Plant Breed 1:16. https://doi.org/10.1111/pbr.12686

    Article  CAS  Google Scholar 

  • Liu K, Muse M (2005) PowerMarker: an integrated analysis environment for genetic marker analysis. Bioinfo 21:2128–2129

    Article  CAS  Google Scholar 

  • Nagaharu U (1935) Genomic analysis in Brassica with special reference to the experimental formation of B. napus and peculiar mode of fertilization. Jpn J Bot 7:389–452

    Google Scholar 

  • Nanjundan J, Singh K, Singh KH, Singh D (2014) Catalogue on rapeseed-mustard germplasm. Directorate of Rapeseed-Mustard Research, Bharatpur, Rajasthan, pp 180

  • Nanjundan J, Singh KH, Thakur AK, Meena KN, Singh D (2015) Development of core collection and trait-specific reference sets in Indian mustard [Brassica juncea (L.) Czern & Coss.] germplasm. Abstract 64, 14th International Rapeseed Congress, held during July 5–9, 2015, at Saskatoon, Saskatchewan, Canada

  • Nanjundan J, Thakur AK, Singh KH, Mishra DC, Singh K, Verma V (2015) Assessment of genetic diversity in Indian mustard [Brassica juncea (L.) Czern & Coss.] for high temperature tolerance using SSR markers. Vegetos 28(4):122–130

    Article  Google Scholar 

  • Nguyen NN, Kwon YS, Park JR, Sc S (2018) Development of a core set of SSR markers for cultivar identification and seed purity T tests in Oriental Melon (Cucumis melo L. var makuwa). Hort Sci Technol 37(1):119–129. https://doi.org/10.12972/kjhst.20190011

    Article  CAS  Google Scholar 

  • Peakall R, Smouse PE (2012) GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics 28:2537–2539

    Article  CAS  Google Scholar 

  • Pratap P, Thakur AK, Meena PD, Meena HS, Sharma P, Singh D, Majumdar R (2015) Genetic diversity assessment in Indian mustard (Brassica juncea L.) for Alternaria blight tolerance using SSR markers. J Oilseed Brass 6(1):175–182

    Google Scholar 

  • Pritchard JK, Wen W (2003) Documentation for the structure software, version 2. Department of Human Genetics, University of Chicago, Chicago. http://pritch.bsd.uchicago.edu/software

  • Singh BK, Choudhary SB, Yadav S, Malhotra EV, Rani R, Ambawat S, Priyamedha PA, Kumar R, Kumar S, Sharma SK, Singh DK, Rai PK (2018) Genetic structure identification and assessment of interrelationships between Brassica and allied genera using newly developed genic-SSRs of Indian Mustard (Brassica juncea L.). Ind Crops Prod 113:111–120

    Article  Google Scholar 

  • Singh BK, Mishra DC, Yadav S, Ambawat S, Vaidya E, Tribhuvan KU, Kumar A, Kumar S, Kumar S, Chaturvedi KK, Rani R, Yadav P, Rai A, Rai PK, Singh VV, Singh D (2016) Identification, characterization, validation and cross-species amplification of genic-SSRs in Indian mustard (Brassica juncea). J Plant Biochem Biotechnol 25:410. https://doi.org/10.1007/s13562-016-0353-y

    Article  CAS  Google Scholar 

  • Tamanna A, Khan AU (2005) Mapping and analysis of simple sequence repeats in the Arabidopsis thaliana genome. Bioinformation 1:64–68

    Article  Google Scholar 

  • Tanksley SD, McCouch SR (1997) Seed banks and molecular maps: unlocking genetic potential from the wild. Sci 277(5329):1063–1066

    Article  CAS  Google Scholar 

  • Thakur AK, Singh BK, Verma V, Chauhan JS (2013) Direct organogenesis in Brassica juncea var. NRCDR-2 and analysis of genetic uniformity using RAPD markers. Natl Acad Sci Lett 36:403–409

    Article  CAS  Google Scholar 

  • Thakur AK, Singh KH, Lal S, Nanjundan J, Yasin JK, Singh D (2018) SSR marker variations in Brassica species provide insight into the origin and evolution of Brassica amphidiploids. Hereditas 155:6

    Article  Google Scholar 

  • Thakur AK, Singh KH, Singh L, Nanjundan J, Rana MK, Singh D (2015) Transferability of SSR markers of Brassica species to some popular varieties of Brassica juncea. Proc Natl Acad Sci India Sect B Biol Sci 85(4):1001–1010

    Article  CAS  Google Scholar 

  • Tyagi R, Sharma V, Sureja AK, Munshi AD, Arya L, Saha D, Verma M (2020) Genetic diversity and population structure detection in sponge gourd (Luffa cylindrica) using ISSR, SCoT and morphological markers. Physiol Mol Biol Plants 26(1):119–131. https://doi.org/10.1007/s12298-019-00723-y

    Article  CAS  PubMed  Google Scholar 

  • Vieira ML, Santini L, Diniz AL, Munhoz Cde F (2016) Microsatellite markers: what they mean and why they are so useful. Genet Mol Biol 39:312–328. https://doi.org/10.1590/1678-4685-GMB-2016-0027

    Article  PubMed  PubMed Central  Google Scholar 

  • Vinu V, Singh N, Vasudev S, Yadav DK, Kumar S, Naresh S, Bhat SR, Prabhu KV (2013) Assessment of genetic diversity in Brassica juncea (Brassicaceae) genotypes using phenotypic differences and SSR markers. Rev Biol Trop 61(4):1919–1934

    CAS  PubMed  Google Scholar 

  • Xu J, Qian X, Wang X, Li R, Cheng X, Yang Y, Fu J, Zhang S, King GJ, Wu J, Liu K (2010) Construction of an integrated genetic linkage map for the A genome of Brassica napus using SSR markers derived from sequenced BACs in B. rapa. BMC Genomics 11:594

    Article  Google Scholar 

  • Yadava DK, Parida SK, Dwivedi SK, Varshney A, Ghazi IA, Sujata V, Mohapatra T (2009) Cross- transferability and polymorphic potential of genomic STMS markers of Brassica species. J Plant Biochem Biotechnol 18(1):29–36

    Article  CAS  Google Scholar 

  • Yan L, Ogutu C, Huang L, Wang X, Zhou H, Lv Y, Long Y, Dong Y, Han Y (2019) Genetic diversity and population structure of coffee germplasm collections in China revealed by ISSR markers. Plant Mol Biol Rep 37:204–213. https://doi.org/10.1007/s11105-019-01148-3

    Article  Google Scholar 

  • Yang et al (2016) The genome sequence of allopolyploid Brassica juncea and analysis of differential homoeolog gene expression influencing selection. Nat Genet 48:1225. https://doi.org/10.1038/ng.3657

    Article  CAS  PubMed  Google Scholar 

  • Yang J, Zhang J, Han R, Zhang F, Mao A, Luo J, Dong B, Liu H, Tang H, Zhang J, Wen C (2019) Target SSR-Seq: A novel SSR genotyping technology associated with perfect SSRs in genetic analysis of cucumber varieties. Front Plant Sci 10:531. https://doi.org/10.3389/fpls.2019.00531

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Authors are thankful to the Science & Engineering Research Board (SERB), New Delhi for providing funding for this work.

Funding

Funds for this research work was provided under the ‘Start-up Research Grant No. SB/YS/LS-86/2014’ received by AKT from Science & Engineering Research Board (SERB), New Delhi.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ajay Kumar Thakur.

Ethics declarations

Conflict of interest

The authors do not have any conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

13562_2021_737_MOESM1_ESM.tif

Fig. S4 Principal co-ordinate analysis (PCoA) of 46 Indian mustard accessions base on selected 95 microsatellite loci (TIF 150 kb)

13562_2021_737_MOESM2_ESM.tif

Fig. S1 UPGMA dendrogram depicting the genetic inter-relationship among different accessions of Indian mustard based on the allelic data of selected set of 95 SSR markers (TIF 148 kb)

13562_2021_737_MOESM3_ESM.tif

Fig. S2 Population genetic structure and relationships among 46 Indian mustard accessions based on the allelic data of selected set of 95 SSR markers. Values of delK with its modal value detecting a true K of three groups (K = 3) (TIF 106 kb)

13562_2021_737_MOESM4_ESM.tif

Fig.S3 The three subgroups inferred from the STRUCTURE analysis using the allelic data of selected 95 microsatellite loci (TIF 216 kb)

Supplementary file5 (DOCX 36 kb)

Supplementary file6 (DOCX 37 kb)

Supplementary file7 (DOCX 22 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh, L., Nanjundan, J., Singh, K.H. et al. Development of a set of SSR markers for characterization of Indian mustard germplasm and varieties. J. Plant Biochem. Biotechnol. 31, 581–591 (2022). https://doi.org/10.1007/s13562-021-00737-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13562-021-00737-2

Keywords

Navigation