, Volume 163, Issue 3, pp 523–531 | Cite as

Molecular markers to exploit genotype–environment interactions of relevance in organic growing systems

  • Gunter BackesEmail author
  • Hanne Østergård


One of the substantial differences between conventional and organic growing systems is the degree to which the farmer can control biotic and abiotic stresses; for organic growing systems varieties are needed with a broad adaptation to annually varying factors, while at the same time a good specific adaptation is necessary with respect to more constant climate and soil conditions. This combination of requirements implies that varieties for organic farming need to be better characterised with respect to genotype × environment interactions than varieties for conventional farming. Such interactions, which often are found for quantitatively expressed traits, are in general difficult to deal with in phenotypic selection. New approaches for QTL analyses (e.g. using physiological models) facilitate estimation of effects of genes on a trait (the phenotype) as a response to environmental influences. From such analyses, markers can be identified which may help to predict the trait expression of a plant genotype in relation to defined environmental factors. The application of markers to select for loci with specific interactions with the environment could, therefore, be especially important for plant breeders targeting organic farming systems.


Genotype–environment interaction Marker-assisted selection Organic farming Plant breeding QTL 



Valuable comments from reviewers and guest editors to previous version of the manuscript are acknowledged. Further, the EU-FP6 project BIOEXPLOIT is acknowledged for financial support and the COST860 SUSVAR network for providing organisational framework for discussions of MAS in breeding for organic farming systems.


  1. Ahmadi N, Albar L, Pressoir G, Pinel A, Fargette D, Ghesquiere A (2001) Genetic basis and mapping of the resistance to rice yellow mottle virus. III. Analysis of QTL efficiency in introgressed progenies confirmed the hypothesis of complementary epistasis between two resistance QTLs. Theor Appl Genet 103:1084–1092. doi: 10.1007/s001220100642 CrossRefGoogle Scholar
  2. Brennan JP, Martin PJ (2007) Returns to investment in new breeding technologies. Euphytica 157:337–349. doi: 10.1007/s10681-007-9378-6 CrossRefGoogle Scholar
  3. Campbell BT, Baenziger PS, Eskridge KM, Budak H, Streck NA, Weiss A et al (2004) Using environmental covariates to explain genotype x environment and QTL × environment interactions for agronomic traits on chromosome 3A of wheat. Crop Sci 44:620–627Google Scholar
  4. Ceccarelli S, Grando S (2007) Decentralized-participatory plant breeding: an example of demand driven research. Euphytica 155:349–360. doi: 10.1007/s10681-006-9336-8 CrossRefGoogle Scholar
  5. Charcosset A, Moreau L (2004) Use of molecular markers for the development of new cultivars and the evaluation of genetic diversity. Euphytica 137:81–94. doi: 10.1023/B:EUPH.0000040505.65040.75 CrossRefGoogle Scholar
  6. Cho YI, Jiang WZ, Chin JH, Piao ZZ, Cho YG, McCouch SR et al (2007) Identification of QTLs associated with physiological nitrogen use efficiency in rice. Mol Cell 23:72–79Google Scholar
  7. Christiansen MJ, Feenstra B, Skovgaard IM, Andersen SB (2006) Genetic analysis of resistance to yellow rust in hexaploid wheat using a mixture model for multiple crosses. Theor Appl Genet 112:581–591. doi: 10.1007/s00122-005-0128-7 PubMedCrossRefGoogle Scholar
  8. Collard BCY, Jahufer MZZ, Brower JB, Pang ECK (2005) An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: the basic concepts. Euphytica 142:169–196. doi: 10.1007/s10681-005-1681-5 CrossRefGoogle Scholar
  9. Coventry S, Collins HM, Barr AR, Jefferies SP, Chalmers KJ, Logue SJ et al (2003) Use of putative QTLs and structural genes in marker-assisted selection for diastatic power in malting barley (Hordeum vulgare L.). Aust J Agric Res 54:1241–1250. doi: 10.1071/AR02193 CrossRefGoogle Scholar
  10. Crossa J, Vargas M, van Eeuwijk FA, Jiang C, Edmeades GO, Hoisington D (1999) Interpreting genotype x environment interaction in tropical maize using linked molecular markers and environmental covariables. Theor Appl Genet 99:611–625. doi: 10.1007/s001220051276 CrossRefGoogle Scholar
  11. Dawson JC, Murphy KM, Jones SS (2008) Decentralized selection and participatory approaches in plant breeding for low-input systems. Euphytica 160:143–154. doi: 10.1007/s10681-007-9533-0 CrossRefGoogle Scholar
  12. Dayteg C, Tuvesson S, Merker A, Jahoor A, Kolodinska-Brantestam A (2007) Automation of DNA marker analysis for molecular breeding in crops: practical experience of a plant breeding company. Plant Breed 126:410–415. doi: 10.1111/j.1439-0523.2007.01306.x CrossRefGoogle Scholar
  13. de Oliveira EJ, Alzate-Marin AL, Borém A, Fagundes SD, de Barros EG, Moreira MA (2005) Molecular marker-assisted selection for development of common bean lines resistant to angular leaf spot. Plant Breed 124:572–575. doi: 10.1111/j.1439-0523.2005.01155.x CrossRefGoogle Scholar
  14. Dingkuhn M, Luquet D, Quilot B, de Reffye P (2005) Environmental and genetic control of morphogenesis in crops: towards models simulating phenotypic plasticity. Aust J Agric Res 56:1289–1302. doi: 10.1071/AR05063 CrossRefGoogle Scholar
  15. Dirlewanger E, Graziano E, Joobeur T, Garriga-Calderé F, Cosson P, Howad W et al (2004) Comparative mapping and marker-assisted selection in Rosaceae fruit crops. Proc Natl Acad Sci USA 101:9891–9896. doi: 10.1073/pnas.0307937101 PubMedCrossRefGoogle Scholar
  16. Dudley JW (1993) Molecular markers in plant improvement - manipulation of genes affecting quantitative traits. Crop Sci 33:660–668Google Scholar
  17. Francia E, Tacconi G, Crosatti C, Barabaschi D, Bulgarelli D, Dall’Aglio E et al (2005) Marker-assisted selection in crop plants. Plant Cell Tissue Organ Cult 82:317–342. doi: 10.1007/s11240-005-2387-z CrossRefGoogle Scholar
  18. Groos C, Robert N, Bervas E, Charmet G (2003) Genetic analysis of grain protein-content, grain yield and thousand-kernel weight in bread wheat. Theor Appl Genet 106:1032–1040PubMedGoogle Scholar
  19. Kamoshita A, Wade LJ, Ali ML, Pathan MS, Zhang J, Sarkarung S et al (2002) Mapping QTLs for root morphology of a rice population adapted to rainfed lowland conditions. Theor Appl Genet 104:880–893. doi: 10.1007/s00122-001-0837-5 PubMedCrossRefGoogle Scholar
  20. Kicherer S, Backes G, Walther U, Jahoor A (2000) Localising QTLs for leaf rust resistance and agronomic traits in barley (Hordeum vulgare L.). Theor Appl Genet 100:881–888. doi: 10.1007/s001220051365 CrossRefGoogle Scholar
  21. Knoll J, Ejeta G (2008) Marker-assisted selection for early-season cold tolerance in sorghum: QTL validation across populations and environments. Theor Appl Genet 116:541–553. doi: 10.1007/s00122-007-0689-8 PubMedCrossRefGoogle Scholar
  22. Lammerts van Bueren ET, Struik PC, Tiemens-Hulscher M, Jacobsen E (2003) Concepts of intrinsic value and integrity of plants in organic plant breeding and propagation. Crop Sci 43:1922–1929Google Scholar
  23. Lammerts van Bueren ET, Goldringer I, Østergård H (2005) In Proceedings of COST SUSVAR/ECO-PB workshop on organic plant breeding strategies and the use of molecular markers. 17–19 January 2005. Louis Bolk Institute, Driebergen, The Netherlands, 103 pGoogle Scholar
  24. Lammerts van Bueren ET, Verhoog H, Tiemens-Hulscher M, Struik PC, Haring MA (2007) Organic agriculture requires process rather than product evaluation of novel breeding techniques. NJAS - Wageningen. J Life Sci 54:401–412Google Scholar
  25. Lijavetzky D, Martinez MC, Carrari F, Hopp HE (2000) QTL analysis and mapping of pre-harvest sprouting resistance in sorghum. Euphytica 112:125–135. doi: 10.1023/A:1003823829878 CrossRefGoogle Scholar
  26. Malosetti M, Voltas J, Romagosa I, Ullrich SE, van Eeuwijk FA (2004) Mixed models including environmental covariables for studying QTL by environment interaction. Euphytica 137:139–145. doi: 10.1023/B:EUPH.0000040511.46388.ef CrossRefGoogle Scholar
  27. Melchinger AE, Utz HF, Schön CC (1998) Quantitative trait locus (QTL) mapping using different testers and independent population samples in maize reveals low power of QTL detection and large bias in estimates of QTL effects. Genetics 149:383–403PubMedGoogle Scholar
  28. Moreau L, Charcosset A, Hospital F, Gallais A (1998) Marker-assisted selection efficiency in populations of finite size. Genetics 148:1353–1365PubMedGoogle Scholar
  29. Moreau L, Charcosset A, Gallais A (2004) Use of trial clustering to study QTL x environment effects for grain yield and related traits in maize. Theor Appl Genet 110:92–105. doi: 10.1007/s00122-004-1781-y PubMedCrossRefGoogle Scholar
  30. Murphy K, Lammer D, Lyon S, Carter B, Jones SS (2005) Breeding for organic and low-input farming systems: an evolutionary-participatory breeding method for inbred cereal grains. Renew Agric Food Syst 20:48–55. doi: 10.1079/RAF200486 CrossRefGoogle Scholar
  31. Olofsdotter M, Jensen LB, Courtois B (2002) Improving crop competitive ability using allelopathy - an example from rice. Plant Breed 121:1–9. doi: 10.1046/j.1439-0523.2002.00662.x CrossRefGoogle Scholar
  32. Paterson AH, Saranga Y, Menz M, Jiang CX, Wright RJ (2003) QTL analysis of genotype x environment interactions affecting cotton fiber quality. Theor Appl Genet 106:384–396PubMedGoogle Scholar
  33. Reymond M, Muller B, Leonardi A, Charcosset A, Tardieu F (2003) Combining quantitative trait loci analysis and an ecophysiological model to analyze the genetic variability of the responses of maize leaf growth to temperature and water deficit. Plant Physiol 131:664–675. doi: 10.1104/pp. 013839 PubMedCrossRefGoogle Scholar
  34. Reymond M, Muller B, Tardieu F (2004) Dealing with the genotype x environment interaction via a modelling approach: a comparison of QTLs of maize leaf length or width with QTLs of model parameters. J Exp Bot 55:2461–2472. doi: 10.1093/jxb/erh200 PubMedCrossRefGoogle Scholar
  35. Romagosa I, Ullrich SE, Han F, Hayes PM (1996) Use of the additive main effects and multiplicative interaction model in QTL mapping for adaptation in barley. Theor Appl Genet 93:30–37. doi: 10.1007/BF00225723 CrossRefGoogle Scholar
  36. Schmierer DA, Kandemir N, Kudrna DA, Jones BL, Ullrich SE, Kleinhofs A (2004) Molecular marker-assisted selection for enhanced yield in malting barley. Mol Breed 14:463–473. doi: 10.1007/s11032-004-0903-1 CrossRefGoogle Scholar
  37. Simmonds NW (1991) Selection for local adaptation in a plant-breeding program. Theor Appl Genet 82:363–367. doi: 10.1007/BF02190624 CrossRefGoogle Scholar
  38. Slafer GA (2003) Genetic basis of yield as viewed from a crop physiologist’s perspective. Ann Appl Biol 142:117–128. doi: 10.1111/j.1744-7348.2003.tb00237.x CrossRefGoogle Scholar
  39. Stuber CW, Lincoln SE, Wolff DW, Helentjaris T, Lander ES (1992) Identification of genetic-factors contributing to heterosis in a hybrid from 2 elite maize inbred lines using molecular markers. Genetics 132:823–839PubMedGoogle Scholar
  40. Struik PC, Cassmann KG, Koorneef M (2007) A dialogue on interdisciplinary collaboration to bridge the gap between plant genomics and crop sciences. In: Spiertz JHJ, Struik PC, Van Laar HH (eds) Scale and complexity in plant systems research: gene-plant-crop relations. Springer, Dordrecht, The Netherlands, pp 319–328CrossRefGoogle Scholar
  41. Tardieu F (2003) Virtual plants: modelling as a tool for the genomics of tolerance to water deficit. Trends Plant Sci 8:9–14. doi: 10.1016/S1360-1385(02)00008-0 PubMedCrossRefGoogle Scholar
  42. Tuvesson S, Dayteg C, Hagberg P, Manninen O, Tanhuanpaa P, Tenhola-Roininen T et al (2007) Molecular markers and doubled haploids in European plant breeding programmes. Euphytica 158:305–312. doi: 10.1007/s10681-006-9239-8 CrossRefGoogle Scholar
  43. Ungerer MC, Halldorsdottir SS, Purugganan MA, Mackay TFC (2003) Genotype–environment interactions at quantitative trait loci affecting inflorescence development in Arabidopsis thaliana. Genetics 165:353–365PubMedGoogle Scholar
  44. van Eeuwijk FA, Malosetti M, Yin XY, Struik PC, Stam P (2005) Statistical models for genotype by environment data: from conventional ANOVA models to eco-physiological QTL models. Aust J Agric Res 56:883–894. doi: 10.1071/AR05153 CrossRefGoogle Scholar
  45. Verhoog H (2005) Organic values and the use of marker technology in organic plant breeding. In: Lammerts van Bueren ET, Goldringer I, Østergård H (eds) COST SUSVAR/ECO-PB Workshop on organic plant breeding strategies and the use of molecular markers. 17–19 January 2005. Louis Bolk Institute, Driebergen, The Netherlands, pp 7–12Google Scholar
  46. Werner K, Friedt W, Ordon F (2005) Strategies for pyramiding resistance genes against the barley yellow mosaic virus complex (BaMMV, BaYMV, BaYMV-2). Mol Breed 16:45–55. doi: 10.1007/s11032-005-3445-2 CrossRefGoogle Scholar
  47. Wolfe MS, Baresel JP, Desclaux D, Goldringer I, Hoad S, Kovács G et al (2008) Developments in breeding cereals for organic agriculture. Euphytica (this issue). doi: 10.1007/s10681-008-9690-9
  48. Yadav RS, Bidinger FA, Hash CT, Yadav YP, Yadav OP, Bhatnagar SK et al (2003) Mapping and characterisation of QTL × E interactions for traits determining grain and stover yield in pearl millet. Theor Appl Genet 106:512–520PubMedGoogle Scholar
  49. Yin XY, Struik PC, van Eeuwijk FA, Stam P, Tang JJ (2005) QTL analysis and QTL-based prediction of flowering phenology in recombinant inbred lines of barley. J Exp Bot 56:967–976. doi: 10.1093/jxb/eri090 PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  1. 1.Faculty of Life Sciences, Agricultural DepartmentUniversity of CopenhagenFrederiksberg CDenmark
  2. 2.Risø National Laboratory for Sustainable EnergyTechnical University of DenmarkRoskildeDenmark

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