Skip to main content

Genome- wide structural and functional variant discovery of rice landraces using genotyping by sequencing

Molecular Biology Reports volume 47, pages 7391–7402 (2020)Cite this article

Abstract

Rice landraces are vital genetic resources for agronomic and quality traits but the undeniable collection of Kerala landraces remains poorly delineated. To effectively conserve, manage, and use these resources, understanding the genomic structure of germplasm is essential. Genotyping by sequencing (GBS) enables identification of an immense number of single nucleotide polymorphism (SNP) and insertion deletion (InDel) from 96 rice germplasm. In the present study, a total of 16.9 × 107 reads were generated, and among that 16.3 × 107 reads were mapped to the indica reference genome. Exploring GBS data unfolded a wide genomic variations including 82,59,639 SNPs and 1,07,140 Indels. Both neighbor-joining tree and principal coordinate analysis with InDel markers revealed the selected germplasm in this study as highly diverse in structure. We assembled unmapped reads which were further employed for gene ontology analysis. These unmapped sequences that are generally expelled from subsequent studies of GBS data analysis may exist as an unexplored resort for several novel significant biological findings. The discovery of SNPs from the haplotyping results of GS3 and GIF1 genes provided insight into marker- assisted selection based on grain size and yield and can be utilized for rice yield improvement. To our knowledge, this is the first report on structural variation analysis using the GBS platform in rice landraces collected from Kerala. Genomic information from this study endows with valuable resources for perceptive rice landrace structure and can also facilitate sequencing-based molecular breeding.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

43,34 €

Price includes VAT (Finland)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Data availability

GBS data can be shared based on mutual agreement and plant material can be shared for scientific purposes.

References

  1. Mishra PK, Sinha AK (2012) Rice diversity in bankura district of West bengal (India). Biosci Discov 3:284–287

    Google Scholar 

  2. Fukuoka S, Suu TD, Ebana K et al (2006) Diversity in phenotypic profiles in landrace populations of vietnamese rice: a case study of agronomic characters for conserving crop genetic diversity on farm. Genet Resour Crop Evol 53:753–761. https://doi.org/10.1007/s10722-004-4635-1

    Article  Google Scholar 

  3. Manickavelu A, Jighly A, Ban T (2014) Molecular evaluation of orphan Afghan common wheat (Triticum aestivum L.) landraces collected by Dr. Kihara using single nucleotide polymorphic markers. BMC Plant Biol. https://doi.org/10.1186/s12870-014-0320-5

    Article  PubMed Central  PubMed  Google Scholar 

  4. Sinha AK, Mishra PK (2013) Morphologybased multivariate analysis of phenotypic diversity of landraces of rice (Oryza sativa L.) of Bankura district of West Bengal. J Crop Weed 9(2):115–121

    Google Scholar 

  5. Das B, Sengupta S, Parida S et al (2013) Genetic diversity and population structure of rice landraces from Eastern and North Eastern States of India. BMC Genet 14:71. https://doi.org/10.1186/1471-2156-14-71

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  6. Eckardt NA (2000) Sequencing the rice genome. Plant Cell 12(11):2011–2017

    CAS  Article  PubMed  Google Scholar 

  7. Gale MD, Devos KM (1998) Comparative genetics in the grasses. Proc Natl Acad Sci USA 95:1971–1974. https://doi.org/10.1073/pnas.95.5.1971

    CAS  Article  Google Scholar 

  8. Davey JW, Hohenlohe PA, Etter PD et al (2011) Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nat Rev Genet 12:499–510

    CAS  Article  Google Scholar 

  9. McCouch SR, McNally KL, Wang W, Hamilton RS (2012) Genomics of gene banks: a case study in rice. Am J Bot 99:407–423. https://doi.org/10.3732/ajb.1100385

    Article  Google Scholar 

  10. Tang W, Wu T, Ye J et al (2016) SNP-based analysis of genetic diversity reveals important alleles associated with seed size in rice. BMC Plant Biol 16:93. https://doi.org/10.1186/s12870-016-0779-3

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  11. Yan J, Yang X, Shah T et al (2010) High-throughput SNP genotyping with the Goldengate assay in maize. Mol Breed 25:441–451. https://doi.org/10.1007/s11032-009-9343-2

    CAS  Article  Google Scholar 

  12. Rockah-Shmuel L, Tóth-Petróczy Á, Sela A et al (2013) Correlated occurrence and bypass of frame-shifting insertion-deletions (InDels) to give functional proteins. PLoS Genet 9:e1003882. https://doi.org/10.1371/journal.pgen.1003882

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  13. Yonemaru JI, Choi SH, Sakai H et al (2015) Genome-wide indel markers shared by diverse Asian rice cultivars compared to japanese rice cultivar ‘Koshihikari’. Breed Sci 65:249–256. https://doi.org/10.1270/jsbbs.65.249

    Article  PubMed Central  PubMed  Google Scholar 

  14. Ndjiondjop MN, Semagn K, Sow M et al (2018) Assessment of genetic variation and population structure of diverse rice genotypes adapted to lowland and upland ecologies in Africa using SNPs. Front Plant Sci 9:446. https://doi.org/10.3389/fpls.2018.00446

    Article  PubMed Central  PubMed  Google Scholar 

  15. Sahu PK, Mondal S, Sharma D et al (2017) Indel marker based genetic differentiation and genetic diversity in traditional rice (Oryza sativa L) landraces of Chhattisgarh, India. PLoS ONE. https://doi.org/10.1371/journal.pone.0188864

    Article  PubMed Central  PubMed  Google Scholar 

  16. Islam MZ, Khalequzzaman M, Prince MFRK et al (2018) Diversity and population structure of red rice germplasm in Bangladesh. PLoS ONE. https://doi.org/10.1371/journal.pone.0196096

    Article  PubMed Central  PubMed  Google Scholar 

  17. Faber-Hammond JJ, Brown KH (2016) Pseudo-De novo assembly and analysis of unmapped genome sequence reads in wild zebrafish reveal novel gene content. Zebrafish 13:95–102. https://doi.org/10.1089/zeb.2015.1154

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  18. Berardini TZ, Mundodi S, Reiser L et al (2004) Functional annotation of the Arabidopsis genome using controlled vocabularies. Plant Physiol 135:745–755. https://doi.org/10.1104/pp.104.040071

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  19. Bayer MM, Rapazote-Flores P, Ganal M et al (2017) Development and evaluation of a Barley 50k iSelect SNP array. Front Plant Sci 8:1792. https://doi.org/10.3389/fpls.2017.01792

    Article  PubMed Central  PubMed  Google Scholar 

  20. Ballesta P, Maldonado C, Pérez-Rodríguez P, Mora F (2019) SNP and haplotype-based genomic selection of quantitative traits in eucalyptus globulus. Plants 8:331. https://doi.org/10.3390/plants8090331

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  21. Doyle JJ, Doyle JL (1990) Isolation of plant DNA from fresh tissue. Science Open, Germany, Focus (Madison)

    Google Scholar 

  22. Poland JA, Rife TW (2012) Genotyping-by-sequencing for plant breeding and genetics. Plant Genome 5:92–102

    CAS  Article  Google Scholar 

  23. Li H, Durbin R (2009) Fast and accurate short read alignment with burrows-wheeler transform. Bioinformatics 25:1754–1760. https://doi.org/10.1093/bioinformatics/btp324

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  24. Li H, Handsaker B, Wysoker A et al (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25:2078–2079. https://doi.org/10.1093/bioinformatics/btp352

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  25. Wang K, Li M, Hakonarson H (2010) ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 38:e164–e164. https://doi.org/10.1093/nar/gkq603

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  26. Rathinasabapathi P, Purushothaman N, Ramprasad VL, Parani M (2015) Whole genome sequencing and analysis of Swarna, a widely cultivated indica rice variety with low glycemic index. Sci Rep 5:11303. https://doi.org/10.1038/srep11303

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  27. Simpson JT, Wong K, Jackman SD et al (2009) ABySS: a parallel assembler for short read sequence data. Genome Res 19:1117–1123. https://doi.org/10.1101/gr.089532.108

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  28. Conesa A, Gotz S, Garcia-Gomez JM et al (2005) Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21:3674–3676. https://doi.org/10.1093/bioinformatics/bti610

    CAS  Article  Google Scholar 

  29. Yu J, Hu S, Wang J et al (2002) A draft sequence of the rice genome (Oryza sativa L. ssp indica). Science 296(5565):79–92

    CAS  Article  Google Scholar 

  30. Rao IS, Neeraja CN, Srikanth B et al (2018) Identification of rice landraces with promising yield and the associated genomic regions under low nitrogen. Sci Rep 8:9200. https://doi.org/10.1038/s41598-018-27484-0

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  31. Manickavelu A, Niwa S, Ayumi K et al (2014) Molecular evaluation of Afghan wheat landraces. Plant Genet Resour 12:S31–S35. https://doi.org/10.1017/S1479262114000203

    Article  Google Scholar 

  32. Rekha T, Kottackal Poulose M, Sreekumar VB, Madassery J (2011) Genetic diversity assessment of rarely cultivated traditional indica rice (Oryza sativa L.) Varieties. Biotechnol Res Int. https://doi.org/10.4061/2011/784719

    Article  PubMed Central  PubMed  Google Scholar 

  33. Kim C, Guo H, Kong W et al (2016) Application of genotyping by sequencing technology to a variety of crop breeding programs. Plant Sci 242:14–22. https://doi.org/10.1016/j.plantsci.2015.04.016

    CAS  Article  Google Scholar 

  34. Pértille F, Guerrero-Bosagna C, Da SVH et al (2016) High-throughput and cost-effective chicken genotyping using next-generation sequencing. Sci Rep 6:26929

    Article  PubMed  Google Scholar 

  35. Poland JA, Brown PJ, Sorrells ME, Jannink J-L (2012) Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach. PLoS ONE 7:e32253. https://doi.org/10.1371/journal.pone.0032253

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  36. Sonah H, Bastien M, Iquira E et al (2013) An improved genotyping by sequencing (GBS) approach offering increased versatility and efficiency of SNP discovery and genotyping. PLoS ONE 8:e54603. https://doi.org/10.1371/journal.pone.0054603

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  37. Wenzl P, Carling J, Kudrna D et al (2004) Diversity Arrays Technology (DArT) for whole-genome profiling of barley. Proc Natl Acad Sci USA 101:9915–9920. https://doi.org/10.1073/pnas.0401076101

    CAS  Article  PubMed  Google Scholar 

  38. Šmarda P, Bureš P, Horová L et al (2014) Ecological and evolutionary significance of genomic GC content diversity in monocots. Proc Natl Acad Sci USA 111:E4096–E4102. https://doi.org/10.1073/pnas.1321152111

    CAS  Article  PubMed  Google Scholar 

  39. Vinogradov AE (2003) DNA helix: the importance of being GC-rich. Nucleic Acids Res 31:1838–1844. https://doi.org/10.1093/nar/gkg296

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  40. Zhao WG, Chung JW, Lee GA et al (2011) Molecular genetic diversity and population structure of a selected core set in garlic and its relatives using novel SSR markers. Plant Breed 130:46–54. https://doi.org/10.1111/j.1439-0523.2010.01805.x

    CAS  Article  Google Scholar 

  41. Huang X, Feng Q, Qian Q et al (2009) High-throughput genotyping by whole-genome resequencing. Genome Res 19:1068–1076. https://doi.org/10.1101/gr.089516.108

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  42. Subbaiyan GK, Waters DLE, Katiyar SK et al (2012) Genome-wide DNA polymorphisms in elite indica rice inbreds discovered by whole-genome sequencing. Plant Biotechnol J 10:623–634. https://doi.org/10.1111/j.1467-7652.2011.00676.x

    CAS  Article  Google Scholar 

  43. Wakeley J (1996) The excess of transitions among nucleotide substitutions: new methods of estimating transition bias underscore its significance. Trends Ecol Evol 11:158–162. https://doi.org/10.1016/0169-5347(96)10009-4

    CAS  Article  Google Scholar 

  44. Zeng YX, Wen ZH, Ma LY et al (2013) Development of 1047 insertion-deletion markers for rice genetic studies and breeding. Genet Mol Res 12:5226–5235. https://doi.org/10.4238/2013.October.30.7

    CAS  Article  Google Scholar 

  45. Nizar AM, John JK, Asokan Nair R, Dutta M (2013) Rice landraces of Kerala state of India: a documentation. Int J Biodivers Conserv 5:250–263. https://doi.org/10.5897/IJBC12.138

    Article  Google Scholar 

  46. Nachimuthu VV, Raveendran M, Duraialaguraja S et al (2015) Analysis of population structure and genetic diversity in rice germplasm using SSR Markers: an initiative towards association mapping of agronomic traits in Oryza Sativa. Rice 8:1–25. https://doi.org/10.1186/s12284-015-0062-5

    Article  Google Scholar 

  47. Coneva V, Simopoulos C, Casaretto JA et al (2014) Metabolic and co-expression network-based analyses associated with nitrate response in rice. BMC Genomics 15(1):1056

    Article  PubMed  Google Scholar 

  48. Ducos E, Vergès V, de Bernonville TD et al (2017) Remarkable evolutionary conservation of antiobesity ADIPOSE/WDTC1 homologs in animals and plants. Genetics 207:153–162. https://doi.org/10.1534/genetics.116.198382

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  49. Zhang YD, Zhu Z, Zhao QY et al (2016) Haplotypes of qGL3 and their roles in grain size regulation with GS3 alleles in rice. Genet Mol Res. https://doi.org/10.4238/gmr.15017587

    Article  Google Scholar 

  50. Wang E, Xu X, Zhang L et al (2010) Duplication and independent selection of cell-wall invertase genes GIF1 and OsCIN1 during rice evolution and domestication. BMC Evol Biol 10:108. https://doi.org/10.1186/1471-2148-10-108

    CAS  Article  PubMed Central  PubMed  Google Scholar 

Download references

Funding

Supported by Department of Science and Technology–Science and Engineering Research Board, Government of India (Grant No. ECR/2016/001934).

Author information

Affiliations

  1. Department of Genomic Science, Central University of Kerala, Kasaragod, Kerala, 671316, India

    Smitha Kunhiraman Vasumathy, Maya Peringottillam, S. Hari Krishna Kumar & Manickavelu Alagu

  2. South Asia hub, ICRISAT Campus, International Rice Research Institute, Secundarabad, Telangana, India

    Krishna T. Sundaram

Authors
  1. Smitha Kunhiraman Vasumathy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maya Peringottillam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Krishna T. Sundaram
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. S. Hari Krishna Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Manickavelu Alagu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: MA, SKV; Methodology: KTS, HKKS, SKV; Formal analysis and investigation: SKV, HKKS; Writing—original draft preparation: SKV; Writing—review and editing: MA; Funding acquisition: MA; Resources: MP; Supervision: MA.

Corresponding author

Correspondence to Manickavelu Alagu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

The authors declare that the study compliance with Ethical standards.

Consent for publication

All authors agreed to submit the manuscript.

Additional information

Publisher's Note

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

Electronic supplementary material

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Vasumathy, S.K., Peringottillam, M., Sundaram, K.T. et al. Genome- wide structural and functional variant discovery of rice landraces using genotyping by sequencing. Mol Biol Rep 47, 7391–7402 (2020). https://doi.org/10.1007/s11033-020-05794-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11033-020-05794-9

Keywords

  • Rice landraces
  • Genotyping by sequencing
  • SNP
  • InDel
  • Unmapped sequences
  • Gene ontology