Skip to main content
SpringerLink
Log in

Association analysis, genetic diversity and population structure of barley (Hordeum vulgare L.) under heat stress conditions using SSR and ISSR markers linked to primary and secondary metabolites

  • Original Article
  • Published:
Molecular Biology Reports Aims and scope Submit manuscript

Abstract

Background

Barley is one of the major cereal crops, which can provide a significant source of genes for stress tolerance due to its high diversity and adaptability. Metabolite traits are considered to be significant for adaptation of barley to heat stress.

Methods and results

In the present study, genetic relationships between 120 barley genotypes were determined with 50 simple sequence repeat (SSR) and 26 inter simple sequence repeat (ISSR) markers under heat stress and non-stress conditions. Moreover, genetic diversity of barley accessions was investigated using the studied markers covering 7 chromosomes of barley.

Results

In general, 153 and 85 polymorphic alleles were detected for SSR and ISSR and number of the observed polymorphic allele varied between 2–9 and 2–6, with an average of 3.26 and 3.26 alleles per locus, respectively. Markers of Bmag0223, GBMS180/180, HVM7, ISSR22, ISSR25, and ISSR48 were the most informative due to their high polymorphism information content value demonstrating that putative techniques utilized in this research can be powerful and valuable tools in breeding program of barley. Association analysis was performed between 9 important traits and SSR and ISSR markers using four statistical models. The results revealed that the model containing both population structure (Q) and general similarity in genetic background arising from shared kinship (K) factors reduced false positive associations between markers and phenotypes.

Conclusions

According to the results, some of markers related to more than one trait under normal conditions (ISSR31-2, HVM62, and GBMS180/180) and heat stress conditions (ISSR20-5, EBmac635, HVM14, and ISSR37-3) were determined, which can be considered to be the most interesting candidates for further studies and simultaneously will provide a useful target for the future breeding programs, such as marker-assisted selection (MAS).

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

Similar content being viewed by others

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. FAO (2015) FAO statistical database. http://faostat.fao.org/faostat/collections,subset=agriculture

  2. Kruszka K, Pacak A, Swida-Barteczka A, Nuc P, Alaba S, Wroblewska Z, Karlowski W, Jarmolowski A, Szweykowska-Kulinska Z (2014) Transcriptionally and post-transcriptionally regulated microRNAs in heat stress response in barley. J Exp Bot 65:6123–6135

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Xia Y, Li R, Ning Z, Bai G, Siddique KHM, Yan G, Baum M, Varshney RK, Guo P (2013) Single nucleotide polymorphisms in HSP17.8 and their association with agronomic traits in barley. PLoS ONE 8:56816

    Article  CAS  Google Scholar 

  4. Piasecka A, Sawikowska A, Kuczyńska A, Ogrodowicz P, Mikołajczak K, Krystkowiak K, Gudy K, Guzy-Wrobelska J, Krajewski P, Kachlicki P (2017) Drought-related secondary metabolites of barley (Hordeum vulgare L.) leaves and their metabolomic quantitative trait loci. Plant J 89:898–913

    Article  CAS  PubMed  Google Scholar 

  5. Luckert D, Toubia-Rahme H, Steffenson BJ, Choo T, Molnar SJ (2012) Novel Septoria speckled leaf blotch resistance loci in a barley doubled-haploid population. Phytopathology 102:683–691

    Article  CAS  PubMed  Google Scholar 

  6. Sayed M, Schumann H, Pillen K, Naz AA, Léon J (2012) AB-QTL analysis reveals new alleles associated to proline accumulation and leaf wilting under drought stress conditions in barley (Hordeum vulgare L.). BMC Genet 13:1–12

    Article  CAS  Google Scholar 

  7. Wang Q, Sun G, Ren X, Wang J, Du B, Li C, Sun D (2017) Detection of QTLs for seedling characteristics in barley (Hordeum vulgare L.) grown under hydroponic culture condition. BMC Genet 18:1–16

    Article  PubMed  PubMed Central  Google Scholar 

  8. Drine S, Guasmi F, Bacha H, Abdellaoui R, Ferchich A (2016) Polymorphism of microsatellite markers in barley varieties contrasting in response to drought stress. Braz J Bot 40:463–473

    Article  Google Scholar 

  9. Bolouri-moghadam MR, Safarnejad A, Kazemitabar KS (2011) Genetic diversity assessment in several barley (Hordeum vulgare L.) cultivars using microsatellite markers. Not Sci Biol 3:140–144

    Article  CAS  Google Scholar 

  10. Xia Y, Li R, Bai G, Siddique KHM, Varshney RK, Yan MG, Guo P (2017) Genetic variations of HvP5CS1 and their association with drought tolerance related traits in barley (Hordeum vulgare L.). Sci Rep 7:7870

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Wang J, Yang J, Neil DM, Zhou M (2010) Mapping of quantitative trait loci controlling barley flour pasting properties. Genetica 138:1191–1200

    Article  PubMed  Google Scholar 

  12. Visioni A, Tondelli A, Francia E, Pswarayi A, Malosetti M, Russell J, Thomas W, Waugh R, Pecchioni N, Romagosa I, Comadran J (2013) Genome-wide association mapping of frost tolerance in barley (Hordeum vulgare L.). BMC Genomics 14:1–13

    Article  CAS  Google Scholar 

  13. Dawood M, Moursi Y, Amro A, Baenziger P, Sallam A (2020) Investigated heat-induced changes in the grain yield and grains metabolites, with 16,966 single nucleotide polymorphisms on barley candidate genes by a collection of 60 Egyptian spring barley genotypes. Agronomy 10:17–30

    Article  CAS  Google Scholar 

  14. Arnon DI (1949) Copper enzymes in isolated chloroplasts, polyphenol oxidase in beta. Plant Physiol 24:1–5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Miller GL (1972) Use of dinitrosalicylic acid reagent for determination of reducing sugar. Anal Chem 31:426

    Article  Google Scholar 

  16. Kochert G (1978) Carbohydrate determination by the phenol sulfuric acid method. In: Helebust JA, Craigie JS (eds) Hand book of physiological methods. Cambridge University Press, Cambridge, pp 96–97

    Google Scholar 

  17. Bates LS, Walderen RD, Taere ID (1973) Rapid determination of free proline for water stress studies. Plant Soil 39:205–207

    Article  CAS  Google Scholar 

  18. Malick CP, Singh MB (1980) In plant enzymology and histo enzymologhy. Kalyani Publishers, New Dehli

    Google Scholar 

  19. Chanes B, Mahely AC (1996) Assay of catalase and peroxidase. In: Colowick SP, Kaplan ND (eds) Methods in enzymology. Academic Press, Cambridge, pp 764–791

    Google Scholar 

  20. Saghai Maroof MA, Biyashev R, Yang GP, Zhang Q, Allard RW (1994) Extraordinarily polymorphic mi-crosatellite DNA in barley: species diversity, chromosomal locations and population dynamics. PNAS 91:5466–5470

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Alfonso C, Igartua E, Ciudad F, Codesal P, Russell JR, Molina-Cano JL, MoralejoPéter Szűcs M, Gracia MP, Lasa M, Casas AM (2008) Heading date QTL in a spring × winter barley cross evaluated in Mediterranean environments. Mol Breed 21:455–471

    Article  Google Scholar 

  22. Yeh FC, Boyle TJB (1997) Population genetic analysis of co-dominant and dominant markers and quantitative traits. Belg J Bot 129:157

    Google Scholar 

  23. Kopahnke D, Nachtigall M, Ordon F, Stefenson BJ (2004) Evaluation and mapping of a leaf rust resistance gene derived from Hordeum vulgare subsp. Spontaneum. Czech J Genet Plant Breed 40:86–90

    Article  Google Scholar 

  24. Kanbar A, Katsuhiko K (2011) Efficiency of ISSR and RAPD dominant markers in assessing genetic diversity among Japanese and Syrian cultivars of barley (H. vulgare L.). J Agric Sci 7:4–10

    CAS  Google Scholar 

  25. Shehadi M, Lakkis GH, Hamze K, Abou Hamdan H, Kobaissi A (2014) Molecular and physiological characterization of local lebanese barley (Hordeum vulgare L.) genotypes. IJSTR 3:216–222

    Google Scholar 

  26. Gous P, Hickey L, Christopher JT, Franckowiak G, Glen P (2016) Discovery of QTL for stay-green and heat-stress in barley (Hordeum vulgare) grown under simulated abiotic stress conditions. Euphytica 207:305–317

    Article  CAS  Google Scholar 

  27. Brbaklic L, Trkulja D, Mikic S, Mirosavljevic M, Momcilovic DB, Prochazkova AV (2021) Genetic diversity and population structure of Serbian barley (Hordeum vulgare L.) collection during a 40-Year long breeding period. Agronomy 11:1–9

    Article  CAS  Google Scholar 

  28. Francia E, Barabaschi D, Tondelli A, Laidò G, Rizza F, Stanca AM, Busconi M, Fogher C, Stockinger EJ, Pecchioni N (2007) Fine mapping of a HvCBF gene cluster at the frost resistance locus Fr-H2 in barley. Theor Appl Genet 115:1083–1091

    Article  CAS  PubMed  Google Scholar 

  29. Kumar P, Banjarey P, Malik B, Tikle A, Verma R (2020) Population structure and diversity assessment of barley (Hordeum vulgare L.) introduction from ICARDA. J Genet 99:1–9

    Article  CAS  Google Scholar 

  30. Abou-Elwafa S (2016) Association mapping for drought tolerance in barley at the reproductive stage. Mol Biol Genet 339:51–59

    Google Scholar 

  31. Firmpong F, Windt C, Dusschoten DV, Naz A, Frei M, Fiorani F (2021) A wild allele of pyrroline-5-carboxylate synthase1 leads to proline accumulation in spikes and leaves of barley contributing to improved performance under reduced water availability. Front Plant Sci 12:448–633

    Google Scholar 

  32. Faure S, Higgins J, Turner A, Laurie DA (2007) The flowering locus T-like gene family in barley (Hordeum vulgare). GSA 176:599–609

    CAS  Google Scholar 

  33. Lakew B, Henry RJ, Ceccarelli S, Grando S, Eglinton J, Baum M (2012) Genetic analysis and phenotypic associations for drought tolerance in Hordeum spontaneum introgression lines using SSR and SNP markers. Euphytica 189:9–29

    Article  CAS  Google Scholar 

  34. Wang A, Yu Z, Ding Y (2009) Genetic diversity analysis of wild close relatives of barley from Tibet and the Middle East by ISSR and SSR markers. CR Biol 332:393–403

    Article  CAS  Google Scholar 

  35. Nei M (1973) Analysis of gene diversity in subdivided populations. Proc Natl Acad Sci USA 70:3321–3323

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Sun D, Ren W, Sun G (2011) Molecular diversity and association mapping of quantitative traits in Tibetan wild and worldwide originated barley (Hordeum vulgare L.) germplasm. Euphytica 178:31–43

    Article  Google Scholar 

  37. Quarrie SA, Steed A, Calestani C, Semikhodskii A, Lebreton C, Chinoy C, Steele N, Pljevljakusic D, Waterman E, Weyen J (2005) A high-density genetic map of hexaploid wheat (Triticum aestivum L.) from the cross Chinese spring X SQ1 and its use to compare QTLs for grain yield across a range of environments. Theor Appl Genet 110:865–880

    Article  CAS  PubMed  Google Scholar 

  38. Cai S, Yu G, Chen X, Huang Y, Jiang X, Zhang G, Jin X (2013) Grain protein content variation and its association analysis in barley. BMC Plant Biol 13:35–42

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Liu L, Genlou S, Xifeng R, Chengdao L, Dongfa S (2015) Identification of QTL underlying physiological and morphological traits of flag leaf in barley. BMC Genet 16:29–34

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Varshney RK, Marcel TC, Ramsay L, Russell J, Roder MS, Stein R, Waugh R, Langridge P, Niks RE, Graner A (2007) A high density barley microsatellite consensus map with 775 SSR loci. Theor Appl Genet 114:1091–1103

    Article  CAS  PubMed  Google Scholar 

  41. Abedini R, GhaneGolmohammadi F, PishkamRad R, Pourabed E, Jafarnezhad A, Shobbar ZS, Shahbazi M (2017) Plant dehydrins: shedding light on structure and expression patterns of dehydrin gene family in barley. JPR 130:747–763

    Article  CAS  Google Scholar 

  42. Lewontin RC (1972) Testing the theory of natural selection. Nature 236:181–182

    Article  Google Scholar 

  43. 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 

  44. Liu K, Muse SV (2005) PowerMarker: an integrated analysis environment for genetic marker analysis. J Bioinform 21:2128–2129

    Article  CAS  Google Scholar 

  45. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Evanno G, Reganut E, Goudet J (2005) Detecting the number of clusters of individuals using the software structure: a simulation study. Mol Ecol 14:2611–2620

    Article  CAS  PubMed  Google Scholar 

  47. Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES (2007) TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23:2633–2635

    Article  CAS  PubMed  Google Scholar 

  48. Spataro G, Tiranti B, Arcaleni P, Bellucci E, Attene G, Papa R, Spagnoletti Zeuli P, Negri V (2011) Genetic diversity and structure of a worldwide collection of Phaseolus coccineus L. Theor Appl Genet 122:1281–1291

    Article  CAS  PubMed  Google Scholar 

  49. Elakhdar A, Abd El-sattar M, Amer K, Kumamaru T (2016) Genetic diversity and association analysis among Egyptian barley (Hordeum vulgare L.) genotypes with different adaptations to saline conditions analyzed by SSR markers. AJCS 10:637–645

    CAS  Google Scholar 

  50. Gougerdchi V, Dezhsetan S, Ebrahimi MA, Asghari A, Sadeghzadeh B (2017) Assessment of genetic diversity and association analysis for phonological traits related to drought escape in barley lines using microsatellite markers. Plant Breed Seed Sci 8:60–69

    Google Scholar 

  51. Hamam KA (2004) Improving crop varieties of spring barley for drought and heat tolerance with AB-QTL analysis. Phd Dissertation, Bonn, University

  52. Ghomi K, Rabiei B, Sabouri H, Sabouri A (2013) Mapping QTLs for traits related to salinity tolerance at seedling stage of rice (Oryza sativa L.): an agrigenomics study of an Iranian rice population. OMICS 17:242–251

    Article  CAS  PubMed  Google Scholar 

  53. Rahimi M, Majidi Hervan I, Valizadeh M, Darvish Kajori F, Ebrahimpour F (2015) Genetic diversity among wild and cultivated barley by ISSR marker. BEPLS 3:57–62

    Google Scholar 

  54. Al-Abdallat AM, Karadsheh A, Hadadd NI, Akash MW, Ceccarelli S, Baum M, Hasan M, Jighly A, Abu Elenein JM (2017) Assessment of genetic diversity and yield performance in Jordanian barley (Hordeum vulgare L.) landraces grown under rainfed conditions. BMC Plant Biol 17:191

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Zhang P, Liu X, Tong H, Lu Y, Li J (2014) Association mapping for important agronomic traits in core collection of rice (Oryza sativa L.) with SSR markers. PLoS ONE 9:e111508

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Jabbari M, Fakheri B, Aghnoum R, Darvishzadeh R, Nezhad N, Ataei R, Koochakpour Z, Razi M (2021) Association analysis of physiological traits in spring barley (Hordeum vulgare L.) under water-deficit conditions. Food Sci Nutr 9:1761–1779

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Pillen K, Zacharias A, Leon J (2003) Advanced backcross QTL analysis in barley (Hordeum vulgare L.). Theor Appl Genet 107:340–352

    Article  CAS  PubMed  Google Scholar 

  58. Sayed MA, El-sadek AN, Bakry BA, Ali MB, Leon J, Salem EM (2017) QTL analysis in barley across environments in egypt. Egypt J Agron 39:53–70

    Article  Google Scholar 

  59. Shahraki H, Fakheri BA (2016) QTLs mapping of morpho-physiological traits of flag leaf in steptoe × morex doubled haploid lines of barley in normal and salinity stress conditions. Int J Farm Allied Sci 5:356–362

    Google Scholar 

  60. Zhongyi Li, Li D, Du X, Wang H, Larroque O, Jenkins A, Jobling SA, Morell MK (2011) The barley amo1 locus is tightly linked to the starch synthase IIIa gene and negatively regulates expression of granule-bound starch synthetic genes. J Exp Bot 62:1–15

    CAS  Google Scholar 

  61. Ahmad A, Arshad M, Saqlan Naqvi SM, Rasheed A, Gul Kazi A, Mujeeb-Kazi A (2015) Comparative assessment of synthetic-derived and conventional bread wheat advanced lines under osmotic stress and implications for molecular analysis. Plant Mol Biol 9:70–85

    Google Scholar 

  62. Manninen OM, Jalli M, Kalendar R, Schulman A, Afanasenko O, Robinson J (2006) Mapping of major spot-type and net-type netblotch resistance genes in the Ethiopian barley line CI 9819. Genome 49:1564–1571

    Article  CAS  PubMed  Google Scholar 

  63. Bertholdsson N-O, Holefors A, Macaulay M, Crespo-Herrera LA (2014) QTL for chlorophyll fluorescence of barley plants grown at low oxygen concentration in hydroponics to simulate waterlogging. Euphytica 201:357–365

    Article  CAS  Google Scholar 

  64. Xue DW, Zhou MX, Zhang XG, Chen S, Wei K, Zeng F, Mao Y, Wu F, Zhang GP (2010) Identification of QTLs for yield and yield components of barley under different growth conditions. J Zhejiang Univ Sci B 11:169–176

Download references

Acknowledgements

The authors thank the University of Guilan and Gonbad-e-Kavous University for their financial supports and agricultural research Center of Gonbad-e-Kavous for providing barley seeds used in this research.

Funding

No funding was received to assist with the preparation of this manuscript.

Author information

Authors and Affiliations

  1. Department of Agronomy and Plant Breeding, Faculty of Agricultural Sciences, University of Guilan, Persian Gulf Highway, P.O. Box: 41635-1314, Rasht, Guilan, Iran

    Khadijeh Ghomi & Babak Rabiei

  2. Department of Plant Production, Faculty of Agriculture and Natural Resources, Gonbad University, Shahid Fallahi Street, Gonbad-e Kāvūs, Golestan, Iran

    Hossein Sabouri & Ebrahim Gholamalipour Alamdari

Authors
  1. Khadijeh Ghomi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Babak Rabiei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hossein Sabouri
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ebrahim Gholamalipour Alamdari
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [KG, BR, HS and EGA]. The first draft of the manuscript was written by [KG] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript (It is noteworthy, KG is as principal author. the article that are based primarily on the Phd thesis).

Corresponding author

Correspondence to Babak Rabiei.

Ethics declarations

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Ethical approval

This manuscript is an excerpt from the doctoral thesis that the relevant proposal was approved in the meeting of the Department of Agriculture, University of Guilan.

Consent to participate

Informed consent was obtained from all individual participants included in the study.

Consent for publication

All authors agreed with the content and gave explicit consent to submit before submission of the publication.

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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ghomi, K., Rabiei, B., Sabouri, H. et al. Association analysis, genetic diversity and population structure of barley (Hordeum vulgare L.) under heat stress conditions using SSR and ISSR markers linked to primary and secondary metabolites. Mol Biol Rep 48, 6673–6694 (2021). https://doi.org/10.1007/s11033-021-06652-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11033-021-06652-y

Keywords

Search

Navigation