Journal of Genetics

, Volume 97, Issue 1, pp 189–203 | Cite as

Identification of SSR and retrotransposon-based molecular markers linked to morphological characters in oily sunflower (\(\textit{Helianthus annuus}\) L.) under natural and water-limited states

  • Soleimani Gezeljeh Ali
  • Reza Darvishzadeh
  • Asa Ebrahimi
  • Mohammad Reza Bihamta
Research Article


Sunflower is an important source of edible oil. Drought is known as an important factor limiting the growth and productivity of field crops in most parts of the world. Agricultural biotechnology mainly aims at developing crops with higher tolerance to the challenging environmental conditions, such as drought. This study examined a number of morphological characters, along with relative water content (RWC) in 100 inbred sunflower lines. A 10 \(\times \) 10 simple lattice design with two replications was employed to measure the mentioned parameters under natural and water-limited states during two successive years. In molecular trial, 30 simple sequence repeat (SSR) primer pairs, as well as 14 inter-retrotransposon amplified polymorphism (IRAP) and 14 retrotransposon-microsatellite amplified polymorphism (REMAP) primer combinations were used for DNA fingerprinting of the lines. Most of the examined characters had lower average values under water-limited than natural states. Maximum and minimum reductions were observed in the cases of yield and oil percentage, respectively. The broad-sense heritabilities for all the examined characters were 0.20–0.73 and 0.10–0.34 under natural and water-limited states, respectively. In the studied samples, 8.97% of the 435 possible locus pairs of the SSRs represented significant linkage disequilibrium (LD) levels. In the association analysis using SSR markers, 22 and 21 markers were identified (\(P \le 0.05\)) for the studied characters under natural and water-limited states, respectively. The corresponding values were 50 and 37 using retrotransposon-based molecular markers. Some detected markers were communal between the characters under water-limited and natural states. This was in line with the phenotypic correlations detected between the characters. Communal markers facilitate the simultaneous selection of several characters and can thus improve the efficacy of selection based on markers in the plant-breeding activities.


linkage disequilibrium microsatellite marker oily sunflower retrotransposon-based molecular marker water-stressed states 

Supplementary material

12041_2018_901_MOESM1_ESM.doc (199 kb)
Supplementary material 1 (doc 199 KB)


  1. Abdel-Tawab F. M., Eman M. F., Bahieldin A., Asmahan A. M., Mahfouz H. T., Hala F. E. et al. 2003 Marker-assisted selection for drought tolerance in Egyptian bread wheat (Triticum aestivum L.). Egypt J. Genet. Cytol. 32, 43–65.Google Scholar
  2. Abdollahi Mandoulakani B., Piri Y., Darvishzadeh R., Bernoosi I. and Jafari M. 2012 Retroelement insertional polymorphism and genetic diversity in Medicago sativa populations revealed by IRAP and REMAP markers. Plant Mol. Biol. Rep. 30, 286–296.CrossRefGoogle Scholar
  3. Abdi N., Darvishzadeh R., Jafari M., Pirzad A. and Haddadi P. 2012 Genetic analysis and QTL mapping of agro-morphological traits in sunflower (Helianthus annuus L.) under two contrasting water treatment conditions. Plant Omics J. 5, 149–158.Google Scholar
  4. Abdi A., Darvishzadeh R., Hatami Maleki H., Haddadi P. and Sarrafi A. 2013 Identification of quantitative trait loci for relative water content and chlorophyll concentration traits in recombinant inbred lines of sunflower (Helianthus annuus L.) under well-watered and water-stressed conditions. Zemdirbyste-Agriculture 100, 159–166.CrossRefGoogle Scholar
  5. Agrama H. A. and Tuinstra M. R. 2003 Phylogenetic diversity and relationship among sorghum accessions using SSRs and RAPDs. Afr. J. Biotechnol. 2, 334–340.CrossRefGoogle Scholar
  6. Allen E. L., Comstock R. E. and Rasmusson D. C. 1978 Optimal environments for yield testing. Crop Sci. 18, 747–751.CrossRefGoogle Scholar
  7. Basirnia A., Darvishzadeh R. and Abdollahi Mandoulakani B. 2016 Retrotransposon insertional polymorphism in sunflower (Helianthus annuus L.) lines revealed by IRAP and REMAP markers. Plant Biosyst. 150, 641–652.CrossRefGoogle Scholar
  8. Bazin J., Langlade N., Vincourt P., Arribat S., Balzergue S., El-Maarouf-Bouteau H. et al. 2011. Targeted mRNA oxidation regulates sunflower seed dormancy alleviation during dry after-ripening. Plant Cell 23, 2196–2208.CrossRefPubMedPubMedCentralGoogle Scholar
  9. Borojevic S. 1990 Principles and methods of plant breeding, Elsevier, New YorkGoogle Scholar
  10. Collard B. C. Y. and Mackill D. J. 2008 Marker-assisted selection: an approach for precision plant breeding in the twenty-first century. Philos. Trans. R. Soc. Lond. B 363, 557–572.CrossRefGoogle Scholar
  11. Collard B. C. Y., Jahufer M. Z. Z., Brouwer J. B. and Pang E. C. K. 2005 An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: The basic concepts. Euphytica 142, 169–196.CrossRefGoogle Scholar
  12. Darvishzadeh R. 2012 Association of SSR markers with partial resistance to Sclerotinia sclerotiorum isolates in sunflower (Helianthus annuus L.). Aust. J. Crop Sci. 6, 276–282.Google Scholar
  13. Evanno G., Regnaut S. and Goudet J. 2005 Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14, 2611–2620.CrossRefPubMedGoogle Scholar
  14. Fernandez P., Soria M., Blesa D., DiRienzo J., Moschen S., Rivarola M. et al. 2012 Development, characterization and experimental validation of a cultivated sunflower (Helianthus annuus L.) gene expression oligonucleotide microarray. PLoS One 7, e45899.CrossRefPubMedPubMedCentralGoogle Scholar
  15. Gupta P. K., Balyan H. S., Edwards K. J., Isaac P., Korzun V., Roder M. S. et al. 2002 Genetic mapping of 66 new microsatellite (SSR) loci in bread wheat. Theor. Appl. Genet. 105, 413–422.CrossRefPubMedGoogle Scholar
  16. Haddadi P., Yazdi-samadi B., Naghavi M. R., Kalantari A., Maury P. and Sarrafi A. 2011 QTL analysis of agronomic traits in recombinant inbred lines of sunflower under partial irrigation. Plant Biotechnol. Rep. 5, 135–146.CrossRefGoogle Scholar
  17. Haddadi P., Ebrahimi A., Langlade N. B., Yazdi-samadi B., Berger M., Calmon A. et al. 2012 Genetic dissection of tocopherol and phytosterol in recombinant inbred lines of sunflower through quantitative trait locus analysis and the candidate gene approach. Mol. Breed. 29, 717–729.CrossRefGoogle Scholar
  18. Haddadi P., Yazdi-samadi B., Langlade N. B., Naghavi M. R., Berger M., Kalantari A. et al. 2010 Genetic control of protein, oil and fatty acids content under partial drought stress and late sowing conditions in sunflower (Helianthus annuus L.). Afr. J. Biotechnol. 9, 6768–6782.Google Scholar
  19. Halton T. A. 2001 Plant genotyping by analysis of microsatellite. In Plant genotyping: the DNA fingerprinting of plants (ed. R. J. Henry), pp. 15–29. CABI Publication, New York.CrossRefGoogle Scholar
  20. Hanamaratti N. G. and Salimath P. M. 2012 Association of flowering delay under stress and drought tolerance in upland rice (Oryza sativa L.). Int. J. Forest. Crop Improv. 3, 99–104.Google Scholar
  21. Harb A., Krishnan A., Ambavaram M. M. R. and Pereira A. 2010 Molecular and physiological analysis of drought stress in Arabidopsis reveals early responses leading to acclimation in plant growth. Plant Physiol. 154, 1254–1271.CrossRefPubMedPubMedCentralGoogle Scholar
  22. Herve D., Fabre F., Berrios E. F., Leroux N., Chaarani G. A., Planchon C. et al. 2001 QTL analysis of photosynthesis and water status traits in sunflower (Helianthus annuus L.) under greenhouse conditions. J. Exp. Bot. 52, 1857–1864.CrossRefPubMedGoogle Scholar
  23. Hu J., Seiler G. and Kole C. 2010 Genetics, genomics and breeding of crop plants: sunflower, pp. 79–109. Science Publishers, Enfield.CrossRefGoogle Scholar
  24. Jannatdoust M., Darvishzadeh R., Ziaeifard R., Azizi H. and Gholinezhad E. 2015 Association mapping for grain quality related traits in confectionery sunflower (Helianthus annuus L.) using retrotransposon markers under normal and drought stress conditions. Crop Biotech. 9, 15–28.Google Scholar
  25. Jannatdoust M., Darvishzadeh R., Azizi H., Ebrahimi M. A., Ziaefard R. and Gholinezhad E. 2017 Identification of retrotransposon markers associated with agromorphological traits in confectionary sunflower (Helianthus annuus L.) under normal and drought stress conditions. J. Crop Breed. 8, 183–197.Google Scholar
  26. Kaloo G. and Bergh B. O. (ed.) 1993 Genetic improvement of vegetable crops, pp. 187–190. Pergamon Press, Oxford and New York.Google Scholar
  27. Kiani S. P., Maury P., Nouri L., Ykhlef N., Grieu P. and Sarrafi A. 2009 QTL analysis of yield-related traits in sunflower under different water treatments. Plant Breed. 128, 363–373.CrossRefGoogle Scholar
  28. Kiani S. P., Maury P., Sarrafi A. and Grieu P. 2008 QTL analysis of chlorophyll fluorescence parameters in sunflower (Helianthus annuus L.) under well-watered and water-stressed conditions. Plant Sci. 175, 565–573.CrossRefGoogle Scholar
  29. Kiani S. P., Talia P., Maury P., Grieu P., Heinz R., Perrault A. et al. 2007a Genetic analysis of plant water status and osmotic adjustment in recombinant inbred lines of sunflower under two water treatments. Plant Sci. 172, 773–787.CrossRefGoogle Scholar
  30. Kiani S. P., Grieu P., Maury P., Hewezi T., Gentzbittel L. and Sarrafi A. 2007b Genetic variability for physiological traits under drought conditions and differential expression of water stress-associated genes in sunflower (Helianthus annuus L.). Theor. Appl. Genet. 114, 193–207.CrossRefGoogle Scholar
  31. Knapp S. J. 1998 Marker-assisted selection as a strategy for increasing the probability of selecting superior genotypes. Crop Sci. 38, 1164–1174.CrossRefGoogle Scholar
  32. Kole C. 2003 Genome mapping and molecular breeding in plants. Springer-Verlag, Berlin, Heidelberg.Google Scholar
  33. Lande R. and Thompson R. 1990 Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics 124, 743–756.PubMedPubMedCentralGoogle Scholar
  34. Langridge P. E., Lagudah S., Holton T. A., Appels R., Sharp P. J. and Chalmers K. J. 2001 Trends in genetic and genome analyses in wheat: A review. Aust. J. Agric. Res. 52, 1043–1077.CrossRefGoogle Scholar
  35. Mackay L. and Powell W. 2007 Methods for linkage disequilibrium mapping in crops. Trends Plant Sci. 12, 57–63.CrossRefPubMedGoogle Scholar
  36. Najafzadeh R., Darvishzadeh R., Musa-Khalifani Kh. and Abrinbana M. 2016 Identification of retrotransposon-based (IRAP) loci associated with resistance to Sclerotinia stem rot disease (Sclerotinia spp.) in sunflower. J. Agric. Biotechnol. 8, 97–118.Google Scholar
  37. Pourtaghi A., Darvish F., Habibi D., Nourmohammadi G. and Daneshian J. 2011 Effect of irrigation water deficit on antioxidant activity and yield of some sunflower hybrids. Aust. J. Crop Sci. 5, 197–204.Google Scholar
  38. Pearce S. R., Harrison G., Li D., Heslop-Harrison J. S., Kumar A. and Flavell A. J. 1996 The Tyl-copia group of retrotransposons in Vicia species: copy number, sequence heterogeneity and chromosomal localisation. Mol. Gen. Genet. 205, 305–315.Google Scholar
  39. Pritchard J. K., Stephanes M., Rosenberg N. A. and Donnelly P. 2000 Association mapping in structured populations. Am. J. Hum. Genet. 67, 170–181.CrossRefPubMedPubMedCentralGoogle Scholar
  40. Pua E. C. and Davey M. R. 2007 Transgenic crops VI series: biotechnology in agriculture and forestry. Springer, Heidelberg.CrossRefGoogle Scholar
  41. Remington D. L., Thornsberry J. M., Matsuoka Y., Wilson L. M., Whitt S. R., Doebley J. et al. 2001 Structure of linkage disequilibrium and phenotypic associations in the maize genome. Proc. Natl. Acad. Sci. USA 98, 11479–11484.CrossRefPubMedPubMedCentralGoogle Scholar
  42. Rengel D., Arribat S., Maury P., Martin-Magniette M. L., Hourlier T., Laporte M. et al. 2012 A gene-phenotype network based on genetic variability for drought responses reveals key physiological processes in controlled and natural environments. PLoS One 7, e45249.CrossRefPubMedPubMedCentralGoogle Scholar
  43. Robitzch Sierra V. 2013 Genetic connectivity of the reef building coral Pocillopora sp. in the Red Sea, pp. 58. MSc. Thesis, Bremen University, Germany.Google Scholar
  44. Roder M. S., Victor K., Wendehake Z. K., Plaschke J., Tixier M. H., Leroy P. et al. 1998 A microsatellite map of wheat. Genetics 149, 2007–2023.PubMedPubMedCentralGoogle Scholar
  45. Rosenberg N. A., Pritchard J. K., Weber J. L., Cann H. M., Kidd K. K., Zhivotovsky L. A. et al. 2002 The genetic structure of human populations. Science 298, 2381–2385.CrossRefPubMedGoogle Scholar
  46. Sahranavard A. F., Darvishzadeh R., Ghadimzadeh M., Azizi H. and Aboulghasemi Z. 2015 Identification of SSR loci related to some important agromorphological traits in different oily sunflower (Helianthus annuus L.) lines using association mapping. Crop Biotech. 10, 73–87.Google Scholar
  47. Sanmiguel P., Tikhonov A., Jin Y. K., Motchoulskaia N., Zakharov D., Melake-Berhan A. et al. 1996 Nested retrotransposons in the intergenic regions of the maize genome. Science 274, 765–768.CrossRefPubMedGoogle Scholar
  48. Shirasu K., Schulman A. H., Lahaye T. and Schulze-Lefert P. 2000 A contiguous 66 kb barley DNA sequence provides evidence for reversible genome expansion. Genome Res. 10, 908–915.CrossRefPubMedPubMedCentralGoogle Scholar
  49. Snowdon R. J. and Fried W. 2004 Molecular markers in Brassica oilseed breeding, current status and future possibilities. Plant Breed. 123, 1–8.CrossRefGoogle Scholar
  50. Tang S., Yu J. K., Slabaugh M. B., Shintani D. K. and Knapp S. J. 2002 Simple sequence repeat map of the sunflower genome. Theor. Appl. Genet. 105, 1124–1136.CrossRefPubMedGoogle Scholar
  51. Thirumarimurugan M., Sivakumar V. M., Merly Xavier A., Prabhakaran D. and Kannadasan T. 2012 Preparation of biodiesel from sunflower oil by transesterification. Int. J. Biosci. Biochem. Bioinforma. 2, 441–444.Google Scholar
  52. Vukich M., Schulman A. H., Giordani T., Natali L., Kalendar R. and Cavallini A. 2009 Genetic variability in sunflower (Helianthus annuus L.) and in the Helianthus genus as assessed by retrotransposon-based molecular markers. Theor. Appl. Genet. 119, 1027–1038CrossRefPubMedGoogle Scholar
  53. Xu S. 2013 Principles of statistical genomics. Springer, New York.CrossRefGoogle Scholar

Copyright information

© Indian Academy of Sciences 2018

Authors and Affiliations

  • Soleimani Gezeljeh Ali
    • 1
  • Reza Darvishzadeh
    • 2
  • Asa Ebrahimi
    • 1
  • Mohammad Reza Bihamta
    • 3
  1. 1.Faculty of Agriculture and Natural Resources, Science and Research BranchIslamic Azad UniversityTehranIran
  2. 2.Department of Plant Breeding and BiotechnologyUrmia UniversityUrmiaIran
  3. 3.Faculty of AgricultureTehran UniversityKarajIran

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