Skip to main content
SpringerLink
Log in

Assessing the Levels of Polymorphism and Differentiation in Iris pumila L. Populations Using Three Types of PCR Markers

  • Published:
Cytology and Genetics Aims and scope Submit manuscript

Abstract

The genetic polymorphism of Iris pumila L., a rare ornamental species involved in hybridization, was studied by PCR analysis using three types of primers: those based on microsatellite sequences (ISSR), MGE sequences (IRAP and iPBS), and abiotic stress response genes (LP-PCR). High levels of intraspecific and intrapopulation genetic polymorphism were revealed, which were comparable to those in other species of the Iris genus. The main indicators of genetic polymorphism of five I. pumila populations from the territory of Ukraine were the proportion of polymorphic loci (P) of 26.5–68.5%, the Shannon index (S) of 0.105–0.285, and genetic diversity (He) of 0.069–0.190. The dependence of the variability level on the population size was direct in the ISSR analysis and inverse according to the other two markers. The direct relationship between genetic and geographic distances between populations was found only using ISSR markers. The highest level of genetic polymorphism was detected by LP-PCR markers, while the population identification of all individual plants was possible only using ISSR markers. The tested system of PCR markers can be used to monitor the state of the gene pool and investigate the genetic structure of populations and migration processes.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

REFERENCES

  1. Allendorf, F.W., Genetics and the conservation of natural populations: allozymes to genomes, Mol. Ecol., 2017, vol. 26, pp. 420–430. https://doi.org/10.1111/mec.13948

    Article  CAS  PubMed  Google Scholar 

  2. Antao, T. and Beaumont, M.A., Mcheza: a workbench to detect selection using dominant markers, Bioinformatics, 2011, vol. 27, no. 12, pp. 1717–1718. https://doi.org/10.1093/bioinformatics/btr253

    Article  CAS  PubMed  Google Scholar 

  3. Biswas, M.K., Xu, Q., and Deng, X., Utility of RAPD, ISSR, IRAP and REMAP markers for the genetic ana-lysis of Citrus spp., Sci. Hortic., 2010, vol. 124, no. 2, pp. 254–261. https://doi.org/10.1016/j.scienta.2009.12.013

    Article  CAS  Google Scholar 

  4. Bothwell, H., Bisbing, S., Therkildsen, N.O., et al., Identifying genetic signatures of selection in a non-model species, alpine gentian (Gentiana nivalis L.), using a landscape genetic approach, Conserv. Genet., 2013, vol. 14, pp. 467–481. https://doi.org/10.1007/s10592-012-0411-5

    Article  Google Scholar 

  5. Bublyk, O.M., Andreev, I.O., Kalendar, R.N., et at., Efficiency of different PCR-based marker systems for assessment of Iris pumila genetic diversity, Biologia, 2013, vol. 68, no. 4, pp. 613–620. https://doi.org/10.2478/s11756-013-0192-4

    Article  Google Scholar 

  6. Bublyk, O., Andreev, I., Parnikoza, I., and Kunakh, V., Population genetic structure of Iris pumila L. in Ukraine: effects of habitat fragmentation, Acta Biol. Cracov. Bot., 2020, vol. 62, no. 1, pp. 51–61. https://doi.org/10.24425/abcsb.2020.131665

    Article  Google Scholar 

  7. Chen, J., Huang, C., Lai, Y., et al., Postglacial range expansion and the role of ecological factors in driving adaptive evolution of Musa basjoo var. formosana, Sci. Rep., 2017, vol. 7, p. 5341. https://doi.org/10.1038/s41598-017-05256-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Chuanliang, D., Jian, Zh., Longdou, L., et al., Study on germplasmic resources of Lycoris longituba using RAPD and ISSR, Analele Universitatii “Alexandru Ioan Cuza,” Seria Genetica si Biologie Moleculara, 2006, vol. VII, pp. 111–120.

    Google Scholar 

  9. Clo, J., Gay, L., and Ronfort, J., How does selfing affect the genetic variance of quantitative traits? An updated meta-analysis on empirical results in angiosperm species, Evolution, 2019, vol. 73, no. 8, pp. 1578–1590. https://doi.org/10.1111/evo.13789

    Article  PubMed  Google Scholar 

  10. Dembicz, I., Szczeparska, L., Moysiyenko, I.I., and Wodkiewicz, M., High genetic diversity in fragmented Iris pumila L. populations in Ukrainian steppe enclaves, Basic Appl. Ecol., 2018, vol. 28, pp. 37–47. https://doi.org/10.1016/j.baae.2018.02.009

    Article  Google Scholar 

  11. D’Onofrio, C., De Lorenzis, G., Giordani, T., et al., Retrotransposon-based molecular markers for grapevine species and cultivars identification, Tree Genet. Genom., 2010, vol. 6, pp. 451–466. https://doi.org/10.1007/s11295-009-0263-4

    Article  Google Scholar 

  12. Doyle, J.J. and Doyle, J.L., A rapid DNA isolation procedure for small quantities of fresh leaf tissue, Phytochem. Bull., 1987, vol. 19, pp. 11–15.

    Google Scholar 

  13. Ellegren, H. and Galtier, N., Determinants of genetic diversity, Nat. Rev. Genet., Nature Publ. Group, 2016, vol. 17, no. 7, pp. 422–433. https://doi.org/10.1038/nrg.2016.58

    Article  CAS  Google Scholar 

  14. Felsenstein, J., PHYLIP—Phylogeny Inference Package (version 3.2), Cladistics, 1989, vol. 5, pp. 164–166.

    Google Scholar 

  15. Flanagan, S.P., Forester, B.R., Latch, E., et al., Guidelines for planning genomic assessment and monitoring of locally adaptive variation to inform species conservation, Evol. Appl., 2018, vol. 11, no. 7, pp. 1035–1052.https://doi.org/10.1111/eva.12569

  16. Frankham, R., Ballou, J.D., Ralls, K., et al., Genetic Management of Fragmented Animal and Plant Populations, Oxford, UK: Oxford Univ. Press, 2017. https://doi.org/10.1093/oso/9780198783398.001.0001

    Book  Google Scholar 

  17. Garrido-Cardenas, J.A., Mesa-Valle, C., and Manzano-Agugliaro, F., Trends in plant research using molecular markers, Planta, 2018, vol. 247, pp. 543–557. https://doi.org/10.1007/s00425-017-2829-y

    Article  CAS  PubMed  Google Scholar 

  18. Glémin, S., Francois, C.M., and Galtier, N., Genome evolution in outcrossing vs. selfing vs. asexual species, in Evolutionary Genomics, Anisimova, M., Ed., Methods Mol. Biol., New York, NY: Humana, 2019, vol. 1910. https://doi.org/10.1007/978-1-4939-9074-011

  19. Gupta, P.K. and Rustgi, S., Molecular markers from the transcribed/expressed region of the genome in higher plants, Funct. Integr. Genom., 2004, vol. 4, pp. 139–162. https://doi.org/10.1007/s10142-004-0107-0

    Article  CAS  Google Scholar 

  20. Hanson, J.O., Rhodes, J.R., Riginos, C., and Fuller, R.A., Environmental and geographic variables are effective surrogates for genetic variation in conservation planning, Proc. Natl. Acad. Sci. U. S. A., 2017, vol. 114, no. 48, pp. 12755–12760. https://doi.org/10.1073/pnas.1711009114

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Holderegger, R., Balkenhol, N., Bolliger, J., et al., Conservation genetics: linking science with practice, Mol. Ecol., 2019, vol. 28, pp. 3848–3856. https://doi.org/10.1111/mec.15202

    Article  PubMed  Google Scholar 

  22. Kalendar, R., Antonius, K., Smykal, P., and Schulman, A.H., iPBS: a universal method for DNA fingerprinting and retrotransposon isolation, Theor. Appl. Genet., 2010, vol. 121, no. 8, pp. 1419–1430. https://doi.org/10.1007/s00122-010-1398-2

    Article  CAS  PubMed  Google Scholar 

  23. Kalendar, R., Amenov, A., and Daniyarov, A., Use of retrotransposon-derived genetic markers to analyse genomic variability in plants, Funct. Plant Biol., 2018, vol. 46, pp. 15–29. https://doi.org/10.1071/FP18098

    Article  CAS  PubMed  Google Scholar 

  24. Kawecki, T.J., Adaptation to marginal habitats, Ann. Rev. Ecol. Evol., 2008, vol. 39, pp. 321–342. https://doi.org/10.1146/annurev.ecolsys.38.091206.095622

    Article  Google Scholar 

  25. Kozyrenko, M.M., Artyukova, E.V., and Zhuravlev, Yu.N., Independent species status of Iris vorobievii N.S. Pavlova, Iris mandshurica Maxim., and Iris humilis Georgi (Iridaceae): evidence from the nuclear and chloroplast genomes, Russ. J. Genet., 2009, vol. 45, no. 11, pp. 1394–1402. https://doi.org/10.1134/S1022795409110143

    Article  CAS  Google Scholar 

  26. Liviero, L., Maestri, E., Gulli, M., et al., Ecogeographic adaptation and genetic variation in wild barley, application of molecular markers targeted to environmentally regulated genes, Genet. Res. Crop. Ev., 2002, vol. 49, no. 2, pp. 133–144. https://doi.org/10.1023/A:1014792509087

    Article  Google Scholar 

  27. Mahmud, R., Kabir, M.R., Hoque, E., and Akhond, A.Y., Assessment of some genetic attributes in wheat (Triticum aestivum L.) using gene-specific molecular markers, Agric. Nat. Res., 2018, vol. 52, pp. 39–44. https://doi.org/10.1016/j.anres.2018.05.003

    Article  Google Scholar 

  28. Mantel, N., The detection of disease clustering and a generalized regression approach, Cancer Res., 1967, vol. 27, no. 2, pp. 209–220.

    CAS  PubMed  Google Scholar 

  29. Pakhrou, O., Medraoui, L., Yatrib, C., et al., Assessment of genetic diversity and population structure of an endemic Moroccan tree (Argania spinosa L.) based in IRAP and ISSR markers and implications for conservation, Physiol. Mol. Biol. Plants, 2017, vol. 23, pp. 651–661. https://doi.org/10.1007/s12298-017-0446-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Pannell, J.R. and Voillemot, M., Evolution and ecology of plant mating systems, eLS, Chichester: Wiley, 2017. https://doi.org/10.1002/9780470015902.a0021909.pub2

    Book  Google Scholar 

  31. Parnikoza, I., Andreev, I., Bublyk, O., et al., The current state of steppe perennial plants populations: a case study on Iris pumila, Biologia, 2017, vol. 72, no. 1, pp. 24–35. https://doi.org/10.1515/biolog-2017-0002

    Article  Google Scholar 

  32. Peakall, R. and Smouse, P.E., GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research, Mol. Ecol. Notes, 2006, vol. 6, pp. 288–295. https://doi.org/10.1111/j.1471-8286.2005.01155.x

    Article  Google Scholar 

  33. Pritchard, J.K., Stephans, M., and Donnelly, P., Inference of population structure using multilocus genotype data, Genetics, 2000, vol. 155, pp. 945–959.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Rodriguez-Quilon, I., Santos-del-Blanco, L., Serra-Varela, M.J., et al., Capturing neutral and adaptive genetic diversity for conservation in a highly structured tree species, Ecol. Appl., 2016, vol. 26, pp. 2254–2266. https://doi.org/10.1002/eap.1361

    Article  PubMed  Google Scholar 

  35. Ruan, Y., Huang, B.-H., Lai, Sh.-J., et al., Population genetic structure, local adaptation, and conservation genetics of Kandelia obovata, Tree Genet. Genom., 2013, vol. 9, pp. 913–925. https://doi.org/10.1007/s11295-013-0605-0

    Article  Google Scholar 

  36. Safriel, N.U., Volis, S., and Kark, S., Core and peripheral populations and global climate change, Isr. J. Plant Sci., 1994, vol. 42, pp. 331–345. https://doi.org/10.1080/07929978.1994.10676584

    Article  Google Scholar 

  37. Schluter, P.M. and Harris, S.A., Analysis of multilocus fingerprinting data sets containing missing data, Mol. Ecol. Notes, 2006, vol. 6, no. 2, pp. 569–572. https://doi.org/10.1111/j.1471-8286.2006.01225.x

    Article  CAS  Google Scholar 

  38. Shirmohammadli, S., Sabouri, H., Ahangar, L., et al., Genetic diversity and association analysis of rice genotypes for grain physical quality using iPBS, IRAP, and ISSR markers, J. Genet. Resour., 2018, vol. 4, pp. 122–129. https://doi.org/10.22080/jgr.2019.15415.1115

    Article  Google Scholar 

  39. Soumaya, Rh.-Ch., Sarra, Ch., Maha, M., et al., Gene-targeted markers to assess genetic diversity and population structure within Tunisian Phoenix dactylifera L. cultivars, Silvae Genet., 2020, vol. 69, no. 1. https://doi.org/10.2478/sg-2020-0005

  40. Tessier, C., David, J., This, P., et al., Optimization of the choice of molecular markers for varietal identification in Vitis vinifera L., Theor. Appl. Genet., 1999, vol. 98, pp. 171–177. https://doi.org/10.1007/s001220051054

    Article  CAS  Google Scholar 

  41. Wang, K., Kang, J., Zhou, H., et al., Genetic diversity of Iris lactea var. chinensis germplasm detected by inter-simple sequence repeat (ISSR), Afr. J. Biotechnol., 2009, vol. 8, no. 19, pp. 4856–4863.

    CAS  Google Scholar 

  42. Wroblewska, A. and Brzosko, E., The genetic structure of the steppe plant Iris aphylla L. at the northern limit of its geographical range, Bot. J. Linn. Soc., 2006, vol. 152, no. 2, pp. 245–255. https://doi.org/10.1111/j.1095-8339.2006. 00568.x

  43. Yang, A.-H., Wei, N., Fritsch, P.W., and Yao, X.-H., AFLP genome scanning reveals divergent selection in natural populations of Liriodendron chinense (Magnoliaceae) along a latitudinal transect, Front. Plant Sci., 2016. https://doi.org/10.3389/fpls.2016.00698

  44. Zhang, Y., Zhang, X., Chen, X., et al., Genetic diversity and structure of tea plant in Qinba area in China by three types of molecular markers, Hereditas, 2018, vol. 155, no. 22. https://doi.org/10.1186/s41065-018-0058-4

  45. Zietkiewicz, E., Rafalski, A., and Labuda, D., Genome fingerprinting by simple sequence repeat (SSR)-anchored polymerase chain reaction amplification, Genomics, 1994, vol. 20, no. 2, pp. 176–183. https://doi.org/10.1006/geno.1994. 1151

Download references

Funding

This work was carried out with the financial support of the Targeted Comprehensive Interdisciplinary Research Program of the National Academy of Sciences of Ukraine “Fundamentals of Molecular and Cellular Biotechnology” for 2010–2014.

Author information

Authors and Affiliations

  1. Institute of Molecular Biology and Genetics, National Academy of Sciences of Ukraine, 03143, Kyiv, Ukraine

    O. Bublyk, I. Parnikoza & V. Kunakh

  2. National Antarctic Scientific Center, Ministry of Education and Science of Ukraine, 01601, Kyiv, Ukraine

    I. Parnikoza

Authors
  1. O. Bublyk
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. I. Parnikoza
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. V. Kunakh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to O. Bublyk, I. Parnikoza or V. Kunakh.

Ethics declarations

The authors declare that they have no conflict of interests. This article does not contain any studies involving animals or human participants performed by any of the authors.

Additional information

Translated by K. Lazarev

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bublyk, O., Parnikoza, I. & Kunakh, V. Assessing the Levels of Polymorphism and Differentiation in Iris pumila L. Populations Using Three Types of PCR Markers. Cytol. Genet. 55, 36–46 (2021). https://doi.org/10.3103/S0095452721010047

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0095452721010047

Keywords:

Search

Navigation