Theoretical and Applied Genetics

, Volume 117, Issue 5, pp 653–669 | Cite as

Quantitative trait loci associated with adaptation to Mediterranean dryland conditions in barley

  • M. von Korff
  • S. Grando
  • A. Del Greco
  • D. This
  • M. BaumEmail author
  • S. Ceccarelli
Original Paper


The objective of the present study was to identify quantitative trait loci (QTL) influencing agronomic performance across rain fed Mediterranean environments in a recombinant inbred line (RIL) population derived from the barley cultivars ER/Apm and Tadmor. The population was tested in four locations (two in Syria and two in Lebanon) during four consecutive years. This allowed the analysis of marker main effects as well as of marker by location and marker by year within location interactions. The analysis demonstrated the significance of crossover interactions in environments with large differences between locations and between years within locations. Alleles from the parent with the higher yield potential, ER/Apm, were associated with improved performance at all markers exhibiting main effects for grain yield. The coincidence of main effect QTL for plant height and yield indicated that average yield was mainly determined by plant height, where Tadmor’s taller plants, being susceptible to lodging, yielded less. However, a number of crossover interactions were detected, in particular for yield, where the Tadmor allele improved yield in the locations with more severe drought stress. The marker with the highest number of cross-over interactions for yield and yield component traits mapped close to the flowering gene Ppd-H2 and a candidate gene for drought tolerance HVA1 on chromosome 1H. Effects of these candidate genes and QTL may be involved in adaptation to severe drought as frequently occurring in the driest regions in the Mediterranean countries. Identification of QTL and genes affecting field performance of barley under drought stress is a first step towards the understanding of the genetics behind drought tolerance.


Quantitative Trait Locus Quantitative Trait Locus Analysis Seed Dormancy Kernel Weight Grain Yield 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We would like to acknowledge the technical support of A. Sabbagh. The authors’ research was supported by grants to ICARDA from the German Federal Ministry of Economic Cooperation and Development (BMZ, Bonn, Germany) and the Generation Challenge Program. M.v.K. was supported by a fellowship from the Society for Technical Cooperation (Gesellschaft fuer Technische Zusammenarbeit, GTZ).

Supplementary material

122_2008_787_MOESM1_ESM.doc (216 kb)
Supplementary tables (DOC 215 kb)


  1. Bahieldin A, Mahfouz H, Eissa HF, Saleh OM, Ramadan AM, Ahmed IA, Dyer WE, El-Itriby HA, Madkour MA (2005) Field evaluation of transgenic wheat plants stably expressing the HVA1 gene for drought tolerance. Physiol Plant 123:421–427CrossRefGoogle Scholar
  2. Baskin CC, Baskin JM (1998) Seeds-ecology, biogeography, and evolution of dormancy and germination. Academic Press, San DiegoGoogle Scholar
  3. Baum M, Grando S, Backes G, Jahoor A, Sabbagh A, Ceccarelli S (2003) QTLs for agronomic traits in the Mediterranean environment identified in recombinant inbred lines of the cross ‘Arta’ × H. spontaneum 41–1. Theor Appl Genet 107:1215–1225PubMedCrossRefGoogle Scholar
  4. Benjamini J, Yekutieli B (2005) Quantitative trait loci analysis using the false discovery rate. Genetics 171:783–790PubMedCrossRefGoogle Scholar
  5. Blum A (2005) Drought resistance, water use efficiency and yield potential—are they compatible, dissonant or mutually exclusive? Aust J Agr Res 56:1159–1168CrossRefGoogle Scholar
  6. Boyd WJR, Li CD, Grime CE, Cakir CR, Potipibol S, Kaveeta L, Men S, Jalal Kamali MR, Barr AR, Moody DB, Lance RCM, Logue SJ, Raman H, Read BJ (2003) Conventional and molecular genetic analyses of factors contributing to the variation in the timing of heading among spring barley (H. vulgare L.) genotypes grown over a mild winter growing season. Aust J Agric Res 54:1277–1301CrossRefGoogle Scholar
  7. Ceccarelli S, Grando S (1991) Selection environment and environmental sensitivity in barley. Euphytica 57:157–167CrossRefGoogle Scholar
  8. Ceccarelli S, Acevedo E, Grando S (1991) Breeding for yield stability in unpredictable environments: single traits, interaction between traits, and architecture of genotypes. Euphytica 56:169–185CrossRefGoogle Scholar
  9. Ceccarelli S, Grando S, Baum M, Udupa SM (2004) Breeding for drought resistance in a changing climate. In: Rao SC, Ryan J (eds) Challenges and Strategies for dryland agriculture. CSSA Spec. Publ. 32. ASA and CSSA, Madison, WI, pp 167–190Google Scholar
  10. Diab AA, Teulat-Merah B, This D, Ozturk NZ, Benscher D, Sorrells ME (2004) Identification of drought-inducible genes and differentially expressed sequence tags in barley. Theor Appl Genet 109(7):1417–1425PubMedCrossRefGoogle Scholar
  11. Forster BP, Ellis RP, Thomas WTB, Newton AC, Tuberosa R, This D, El-Enein RA, Bahri MH, Ben Salem M (2000) The development and application of molecular markers for abiotic stress tolerance in barley. J Exp Bot 51(342):19–27PubMedCrossRefGoogle Scholar
  12. Forster BP, Ellis RP, Moir J, Talame V, Sanguineti MC, Tuberosa R, This D, Teulat-Merah B, Ahmed I, Mariy SAEE, Bahri H, El Ouahabi M, Zoumarou-Wallis N, El-Fellah M, Ben Salem M (2004) Genotype and phenotype associations with drought tolerance in barley tested in North Africa. Ann Appl Biol 144(2):157–168CrossRefGoogle Scholar
  13. Guo Y, Xiong L, Song CP, Gong D, Halfter U, Zhu JK (2002) A calcium sensor and its interacting protein kinase are global regulators of abscisic acid signaling in Arabidopsis. Dev Cell 3(2):233–244PubMedCrossRefGoogle Scholar
  14. Haley CS, Knott SA (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69:315–324PubMedGoogle Scholar
  15. Han F, Romagosa I, Ullrich SE, Jones BL, Hayes PM, Wesenberg DM (1997) Molecular marker-assisted selection for malting quality traits in barley. Mol Breed 2(6):427–437CrossRefGoogle Scholar
  16. Han F, Ullrich SE, Clancy JA, Romagosa I (1999) Inheritance and fine mapping of a major barley seed dormancy QTL. Plant Sci 143:113–118CrossRefGoogle Scholar
  17. Hazen SP, Safiullah Pathan M, Sanchez A, Baxter I, Dunn M, Estes B, Chang H-S, Zhu T, Kreps JA, Nguyen HT (2005) Expression profiling of rice segregating for drought tolerance QTLs using a rice genome array. Funct Integr Genomics 5:104–116PubMedCrossRefGoogle Scholar
  18. Hori K, Sato K, Takeda K (2007) Detection of seed dormancy QTL in multiple mapping populations derived from crosses involving novel barley germplasm. Theor Appl Genet. doi: 10.1007/s00122-007-0620-3
  19. Horsley RD, Schmierer D, Maier C, Kudrna D, Urrea CA, Steffenson BJ, Schwarz PB, Franckowiak JD, Green MJ, Zhang B, Kleinhofs A (2006) Identification of QTL associated with Fusarium head blight resistance in barley accession CIho4196. Crop Sci 46:145–156CrossRefGoogle Scholar
  20. Igartua E, Casas AM, Ciudad F, Montoya JL, Romagosa I (1999) RFLP markers associated with major genes controlling heading date evaluated in a barley germplasm pool. Heredity 83(5):551–559PubMedCrossRefGoogle Scholar
  21. Kawaguchi R, Thomas G, Bray EA, Bailey-Serres J (2004) Differential mRNA translation contributes to gene regulation under non-stress and dehydration stress conditions in Arabidopsis thaliana. Plant J 38:823–839PubMedCrossRefGoogle Scholar
  22. Lambers H, Chapin FS, Pons TL (1998) Plant physiological ecology. Springer, New YorkGoogle Scholar
  23. Moraleja M, Swanston JS, Munoz P, Prada PD, Elía M, Russel JR, Ramsay L, Cistué L, Codesal P, Casas AM, Romagosa I, Powell W, Molina-Cano JL (2004) Use of new EST markers to elucidate the genetic differences in grain protein content between European and North American two-rowed malting barleys. Theor Appl Genet 110:116–125CrossRefGoogle Scholar
  24. Mustilli AC, Merlot S, Vavasseur A, Fenzi F, Giraudat J (2002) Arabidopsis OST1 protein kinase mediates the regulation of stomatal aperture by abscisic acid and acts upstream of reactive oxygen species production. Plant Cell 13:2513–2523Google Scholar
  25. Oraby HF, Ransom CB, Kravchenko AN, Sticklen MB (2005) Barley HVA1 gene confers salt tolerance in R3 transgenic oat. Crop Sci 45:2218–2227CrossRefGoogle Scholar
  26. Prada D, Ullrich SE, Molina-Cano JL, Cistué L, Clancy JA, Romagosa I (2004) Genetic control of dormancy in a Triumph/Morex cross in barley. Theor Appl Genet 109(4):62–70PubMedCrossRefGoogle Scholar
  27. Reinheimer JL, Barr AR, Eglinton JK (2004) QTL mapping of chromosomal regions conferring reproductive frost tolerance in barley (Hordeum vulgare L.). Theor Appl Genet 109(6):1267–1274PubMedCrossRefGoogle Scholar
  28. Riccardi F, Gazeau P, Jacquemot MP, Vincent D, Zivy M (2004) Deciphering genetic variation of proteome response to water deficit in maize leaves. Plant Physiol Biochem 42:1003–1011PubMedCrossRefGoogle Scholar
  29. SAS Institute (2003) The SAS system for Windows, release 9.1. SAS Institute, Cary, NC, USAGoogle Scholar
  30. Sayed H, Backes G, Kayyal H, Yahyaoui A, Ceccarelli S, Grando S, Jahoor A, Baum M (2004) New molecular markers linked to qualitative and quantitative powdery mildew and scald resistance genes in barley for dry areas. Euphytica 135:225–228CrossRefGoogle Scholar
  31. Schmierer DA, Kandemir N, Kudrna DA, Jones BL, Ullrich SE, Kleinhofs A (2005) Molecular marker-assisted selection for enhanced yield in malting barley. Mol Breed 14(4):463–473CrossRefGoogle Scholar
  32. Shen Q, Chen CN, Brands A, Pan SM, Tuan-Hua DH (2001) The stress- and abscisic acid-induced barley gene HVA22: developmental regulation and homologues in diverse organisms. Plant Mol Biol 45(3):327–340PubMedCrossRefGoogle Scholar
  33. Shinozaki K, Yamaguchi-Shinozaki K (2007) Gene networks involved in drought stress response and tolerance. J Exp Bot 58(2):221–227PubMedCrossRefGoogle Scholar
  34. Shinozaki K, Yamaguchi-Shinozaki K, Seki M (2003) Regulatory networks of gene expression in drought and cold stress responses. Curr Opin Plant Biol 6:410–417PubMedCrossRefGoogle Scholar
  35. Singh M, Malhotra RS, Ceccarelli S, Sarker A, Grando S, Erskine W (2003) Spatial variability models to improve dryland field trials. Exp Agric 39:1–10CrossRefGoogle Scholar
  36. Talamé V, Sanguineti MC, Chiapparino E, Bahri H, Ben Salem M, Forster BP, Ellis RP, Rhouma S, Zoumarou W, Waugh R, Tuberosa R (2004) Identification of Hordeum spontaneum QTL alleles improving field performance of barley grown under rainfed conditions. Ann Appl Biol 144(3):309–319CrossRefGoogle Scholar
  37. Teulat B, This D, Khairallah M, Borries C, Ragot C, Sourdille P, Leroy P, Monneveux P, Charrier A (1998) Several QTLs involved in osmotic adjustment trait variation in barley (Hordeum vulgare L.). Theor Appl Genet 96:688–698CrossRefGoogle Scholar
  38. Teulat B, Borries C, This D (2001a) New QTLs identified for plant water status, water-soluble carbohydrates, osmotic adjustment in a barley population grown in a growth-chamber under two water regimes. Theor Appl Genet 103:161–170CrossRefGoogle Scholar
  39. Teulat B, Merah O, Souyris I, This D (2001b) QTLs for agronomic traits from Mediterranean barley progeny grown in several environments. Theor Appl Genet 103:774–787CrossRefGoogle Scholar
  40. Teulat B, Merah O, Sirault X, Borries C, Waugh R, This D (2002) QTL for grain carbon isotope discrimination in field-grown barley. Theor Appl Genet 106:118–126PubMedGoogle Scholar
  41. This D, Borries C, Souryis I, Teulat B (2000) QTL study of chlorophyll content as a genetic parameter of drought tolerance in barley. Barley Genet Newsl 30:20Google Scholar
  42. Tinker NA, Mather DE (1995) MQTL: Software for simplified composite interval mapping of QTL in multiple environments. J Agric Genom 1:1–24Google Scholar
  43. Tournaire-Roux C, Sutka M, Javot H, Gout E, Gerbeau P, Luu DT, Bligny R, Maurel C (2003) Cytosolic pH regulates root water transport during anoxic stress through gating of aquaporins. Nature 425:393–397PubMedCrossRefGoogle Scholar
  44. Tuberosa R, Salvi S (2006) Genomics-based approaches to improve drought tolerance of crops. Trends Plant Sci 11(8):405–412PubMedCrossRefGoogle Scholar
  45. Ullrich SE, Hayes PM, Dyer WE, Blake TK, Clancy JA (1993) Quantitative trait locus analysis of seed dormancy in ‘Steptoe’ barley. In: Proceedings of the pre-harvest sprouting in cereals 1992, St Paul, pp 136–145Google Scholar
  46. van Ginkel M, Calhoun DS, Gebeyehu G, Miranda A, Tian-You C, Pargas Lara R, Trethowan RM, Sayre K, Crossa J, Rajaram S (1998) Plant traits related to yield of wheat in early, late, or continuous drought conditions. Euphytica 100:109–121CrossRefGoogle Scholar
  47. Weltzien E (1988) Evaluation of barley (Hordeum vulgare L) landrace populations originating from different growing regions in the near east. Plant Breed 101:95–106CrossRefGoogle Scholar
  48. Xu D, Duan X, Wang B, Hong B, Ho T-HD, Wu R (1996) Expression of a late embryogenesis related protein gene, HVA1, from barley confers tolerance to water deficit and salt stress in transgenic rice. Plant Physiol 110:249–257PubMedGoogle Scholar
  49. Yan W, Hunt LA, Sheng Q, Szlavnics Z (2000) Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Sci 40:597–605Google Scholar
  50. Zhang F, Chen G, Huang Q, Orion O, Krugman T, Fahima T, Korol AB, Nevo E, Gutterman Y (2005) Genetic basis of barley caryopsis dormancy and seedling desiccation tolerance at the germination stage. Theor Appl Genet 110:445–453PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • M. von Korff
    • 1
  • S. Grando
    • 1
  • A. Del Greco
    • 3
  • D. This
    • 2
  • M. Baum
    • 1
    Email author
  • S. Ceccarelli
    • 1
  1. 1.International Center for Agricultural Research in the Dry AreasAleppoSyria
  2. 2.Montpellier SupAgroMontpellier cedexFrance
  3. 3.Bioversity InternationalMaccarese, RomeItaly

Personalised recommendations