Skip to main content

Advertisement

SpringerLink
Log in

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

  • Published:
Euphytica Aims and scope Submit manuscript

Abstract

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.

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.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • 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

    Article  CAS  Google Scholar 

  • Brennan JP, Martin PJ (2007) Returns to investment in new breeding technologies. Euphytica 157:337–349. doi:10.1007/s10681-007-9378-6

    Article  Google Scholar 

  • 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 44:620–627

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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 23:72–79

    CAS  Google Scholar 

  • 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

    Article  PubMed  CAS  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  PubMed  CAS  Google Scholar 

  • Dudley JW (1993) Molecular markers in plant improvement - manipulation of genes affecting quantitative traits. Crop Sci 33:660–668

    CAS  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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 106:1032–1040

    PubMed  CAS  Google Scholar 

  • 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

    Article  PubMed  CAS  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Article  PubMed  Google Scholar 

  • 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 43:1922–1929

    Google Scholar 

  • 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 p

  • 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 54:401–412

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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 149:383–403

    PubMed  CAS  Google Scholar 

  • Moreau L, Charcosset A, Hospital F, Gallais A (1998) Marker-assisted selection efficiency in populations of finite size. Genetics 148:1353–1365

    PubMed  CAS  Google Scholar 

  • 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

    Article  PubMed  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 106:384–396

    PubMed  CAS  Google Scholar 

  • 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

    Article  PubMed  CAS  Google Scholar 

  • 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

    Article  PubMed  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • Simmonds NW (1991) Selection for local adaptation in a plant-breeding program. Theor Appl Genet 82:363–367. doi:10.1007/BF02190624

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 132:823–839

    PubMed  CAS  Google Scholar 

  • 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 319–328

    Chapter  Google Scholar 

  • 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

    Article  PubMed  CAS  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • Ungerer MC, Halldorsdottir SS, Purugganan MA, Mackay TFC (2003) Genotype–environment interactions at quantitative trait loci affecting inflorescence development in Arabidopsis thaliana. Genetics 165:353–365

    PubMed  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 7–12

  • 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

    Article  CAS  Google Scholar 

  • 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

  • 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 106:512–520

    PubMed  CAS  Google Scholar 

  • 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

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgements

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.

Author information

Authors and Affiliations

  1. Faculty of Life Sciences, Agricultural Department, University of Copenhagen, Thorvaldsensvej 40, 1871, Frederiksberg C, Denmark

    Gunter Backes

  2. Risø National Laboratory for Sustainable Energy, Technical University of Denmark, Frederiksborgvej 399, 4000, Roskilde, Denmark

    Hanne Østergård

Authors
  1. Gunter Backes
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hanne Østergård
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gunter Backes.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Backes, G., Østergård, H. Molecular markers to exploit genotype–environment interactions of relevance in organic growing systems. Euphytica 163, 523–531 (2008). https://doi.org/10.1007/s10681-008-9729-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10681-008-9729-y

Keywords

Search

Navigation