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A simplified conceptual model of carbon/nitrogen functioning for QTL analysis of winter wheat adaptation to nitrogen deficiency

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Abstract

Breeding new varieties adapted to low-input agricultural practices is of particular interest in light of current economical and environmental concerns. Improving nitrogen (N) uptake and N utilization efficiency (NUE) are two ways of producing varieties tolerant to low N input. To offer new possibilities to breeders, it is necessary to acquire more knowledge about these two processes. Knowing C and N metabolisms are linked and knowing N uptake is partly explained by root characteristics, we carried out a QTL analysis for traits associated with N uptake and NUE by using both a conceptual model of C/N plant functioning and a root architecture description. A total of 120 lines were selected according to their genotype among 241 doubled haploids derived from two varieties, one N stress tolerant and the other N stress sensitive. They were grown in hydroponic rhizotrons under N-limited nutritional conditions. Initial conditions varied among genotypes; therefore, total root length on day 1 was used to correct traits. Heritabilities ranged from 13 to 84%. Thirty-two QTL were located: 6 associated with root architecture (on chromosomes 4B, 5A, 5D and 7B), 6 associated with model efficiencies (1B, 2B, 6A, 6B, 7A, 7B and 7D) and 20 associated with state variables (1A, 1B, 2B, 4B, 5A, 5B and 6B). The effects of the dwarfing gene Rht-B1 on root traits are discussed, as well as the features of a conceptual plant functioning model, as a useful tool to assess pertinent traits for QTL detection. It is suggested that further studies that couple QTL with a functioning model and a root architecture description could serve in the search for ideotypes.

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Abbreviations

ADM:

Aerial dry matter

DH:

Doubled haploids

h 2 :

Heritability

LA:

Leaf area

LG:

Linkage group

LRL:

Lateral root length

LRN:

Lateral root number

N:

Nitrogen

NcAP :

Aerial parts nitrogen content

NcR :

Root nitrogen content

NTOT:

Amount of total nitrogen

NUR:

Nitrogen-specific uptake rate

PAR:

Photosynthetically active radiation

PRL:

Primary root length

QTL:

Quantitative trait locus

RDM:

Root dry matter

SRL:

Specific root length

SSR:

Simple sequence repeats

TDM:

Total dry matter

TRL:

Total root length

RUE:

Radiation use efficiency

References

  • Agrama HAS, Zakaria AG, Said FB, Tuinstra M (1999) Identification of quantitative trait loci for nitrogen use efficiency in maize. Mol Breed 5:187–195

    Article  Google Scholar 

  • Bahrman N, Gouy A, Devienne-Barret F, Hirel B, Vedele F, Le Gouis J (2005) Differential change in root protein patterns of two wheat varieties under high and low nitrogen nutrition levels. Plant Sci 168:81–87

    Article  CAS  Google Scholar 

  • Bänziger M, Betran FJ, Lafitte HR (1997) Efficiency of high-nitrogen selection environments for improving maize for low-nitrogen target environments. Crop Sci 37:1103–1109

    Article  Google Scholar 

  • Basten CJ, Weir BS, Zeng ZB (1994) Zmap—a QTL cartographer. In: Proceedings of the 5th world congress on genetics applied to livestock production, vol 22, pp 65–66

  • Basten CJ, Weir BS, Zeng ZB (2002) QTL cartographer version 1.16

  • Bertin P, Gallais A (2001) Genetic variation for nitrogen use efficiency in a set of recombinant inbred lines. II—QTL detection and coincidences. Maydica 46:53–68

    Google Scholar 

  • Boisson M, Mondon K, Torney V, Nicot N, Lainé AL, Bahrman N, Gouy A, Daniel-Vedele F, Hirel B, Sourdille P, Dardevet M, Ravel C, Le Gouis J (2005) Partial sequences of nitrogen metabolism genes in hexaploid wheat. Theor Appl Genet 110:932–940

    Article  PubMed  CAS  Google Scholar 

  • Brancourt-Hulmel M, Heumez E, Pluchard P, Béghin D, Depatureaux C, Giraud A, Le Gouis J (2005) Indirect versus direct selection of winter wheat for low input or high input levels. Crop Sci 45:1427–1431

    Article  Google Scholar 

  • Bush MG, Evans LT (1988) Growth and development in tall and dwarf isogenic lines of spring wheat. Field Crops Res 18:243–270

    Article  Google Scholar 

  • Charmet G, Robert N, Branlard G, Linossier L, Martre P, Triboï E (2005) Genetic analysis of dry matter and nitrogen accumulation and protein composition in wheat kernels. Theor Appl Genet 111:540–550

    Article  PubMed  CAS  Google Scholar 

  • Churchill GA, Doerge RW (1994) Empirical threshold values for quantitative trait mapping. Genetics 138:963–971

    PubMed  CAS  Google Scholar 

  • Dumas JBA (1831) Procédés de l’analyse organique. Ann Chim Phys 2:198–213

    Google Scholar 

  • Ellis MH, Spielmeyer W, Gale GJ, Rebetzke GR, Richards RA (2002) “Perfect” markers for the Rht-B1b and Rht-D1b dwarfing genes in wheat. Theor Appl Genet 105:1038–1042

    Article  PubMed  CAS  Google Scholar 

  • Gallais A, Hirel B (2004) An approach to the genetics of nitrogen use efficiency in maize. J Exp Bot 55:295–306

    Article  PubMed  CAS  Google Scholar 

  • Guillaumie S, Charme G, Linossier L, Torney V, Robert N, Ravel C (2004) Colocation between a gene encoding the bZip factor SPA and a eQTL for a high-molecular-weight glutenin subunit in wheat (Triticum aestivum). Genome 47:705–713

    Article  PubMed  CAS  Google Scholar 

  • Hirel B, Bertin P, Quilleré I, Bourdoncle W, Attagnant C, Dellay C, Gouy A, Retailliau C, Falque M, Gallais A (2001) Towards a better understanding of the genetic and physiological basis for nitrogen use efficiency in maize. Plant Physiol 125:1258–1270

    Article  PubMed  CAS  Google Scholar 

  • Jannink JL (2005) Selective phenotyping to accurately map quantitative trait loci. Crop Sci 45:901–908

    Article  CAS  Google Scholar 

  • Lander ES, Green P, Abrahamson J, Barlow A, Daly M, Lincoln SE, Newburg L (1987) MAPMAKER: an interactive computer package for constructing primary genetic maps of experimental and natural populations. Genomics 1:174–181

    Article  PubMed  CAS  Google Scholar 

  • Landi P, Albrecht B, Giuliani MM, Sanguineti MC (1998) Seedling characteristics in hydroponic culture and field performance of maize genotypes with different resistance to root lodging. Maydica 43:111–116

    Google Scholar 

  • Landi P, Sanguineti MC, Darrah LL, Giuliani MM, Salvi S, Conti S, Tuberosa R (2002) Detection of QTLs for vertical root pulling resistance in maize and overlap with QTLs for root traits in hydroponics and for grain yield under different water regimes. Maydica 47:233–243

    Google Scholar 

  • Laperche A, Brancourt-Hulmel M, Heumez E, Gardet O, Le Gouis J (2006) Estimation of winter wheat genetic parameters according to nitrogen stress with the use of probe genotypes. Theor Appl Genet 112:797–807

    Article  PubMed  CAS  Google Scholar 

  • Le Gouis J, Béghin D, Heumez E, Pluchard P (2000) Genetic differences for nitrogen uptake and nitrogen utilisation efficiencies in winter wheat. Eur J Agron 12:163–173

    Article  CAS  Google Scholar 

  • Li Z, Mu P, Li C, Shang H, Li Z, Gao Y, Wang X (2005) QTL mapping of root traits in a doubled haploid population from a cross between upland and lowland japonica rice in three environments. Theor Appl Genet 110:1244–1252

    Article  PubMed  CAS  Google Scholar 

  • Lian X, Xing Y, Yan H, Xu C, Li X, Zhang Q (2005) QTLs for low nitrogen tolerance at seedling stage identified using a recombinant inbred line population derived from an elite rice hybrid. Theor Appl Genet 112:85–96

    Article  PubMed  CAS  Google Scholar 

  • Loudet O, Chaillou S, Krapp A, Daniel-Vedele F (2003a) Quantitative trait loci analysis of water and anion contents in interaction with nitrogen availability in Arabidopsis thaliana. Genetics 163:711–722

    CAS  Google Scholar 

  • Loudet O, Chaillou S, Merigout P, Talbotec J, Daniel-Vedele F (2003b) Quantitative trait loci analysis of nitrogen use efficiency in Arabidopsis. Plant Physiol 131:345–358

    Article  CAS  Google Scholar 

  • McCaig TN, Morgan JA (1993) Root and shoot dry matter partitioning in near-isogenic wheat lines differing in height. Can J Plant Sci 73:679–689

    Google Scholar 

  • Mian MAR, Nafziger ED, Kolb FL, Teyker RH (1994) Root size and distribution of field-grown wheat genotypes. Crop Sci 34:810–812

    Article  Google Scholar 

  • Mickelson S, See D, Meyer FD, Garner JP, Foster CR, Blake TK, Fischer AM (2003) Mapping of QTL associated with nitrogen storage and remobilization in barley (Hordeum vulgare L.) leaves. J Exp Bot 54:801–812

    Article  PubMed  CAS  Google Scholar 

  • Miralles DJ, Slafer GA, Lynch V (1997) Rooting patterns in near-isogenic lines of spring wheat for dwarfism. Plant Soil 197:79–86

    Article  CAS  Google Scholar 

  • Olmos S, Distelfeld A, Chicaiza O, Schlatter AR, Fahima T, Echenique V, Dubcovsky J (2003) Precise mapping of a locus affecting grain protein content in durum wheat. Theor Appl Genet 107:1243–1251

    Article  PubMed  CAS  Google Scholar 

  • Pagès L (1992) Mini-rhizotrons transparents pour l’étude du système racinaire de jeunes plantes. Application à la caractérisation du développement racinaire de jeunes chênes (Quercus robus). Can J Bot 70:1840–1847

    Article  Google Scholar 

  • Prasad M, Kumar N, Kulwal PL, Röder MS, Balyan HS, Dhaliwal HS, Gupta PK (2003) QTL analysis for grain protein content using SSR markers and validation studies using NILs in bread wheat. Theor Appl Genet 106:659–667

    PubMed  CAS  Google Scholar 

  • Price AH, Tomos AD, Virk DS (1997) Genetic dissection of root growth in rice (Oryza sativa L.) I: a hydroponic screen. Theor Appl Genet 95:132–142

    Article  Google Scholar 

  • Quarrie SA, Steed A, Calestani C, Semikhodskii A, Lebreton C, Chinoy C, Steele N, Pljevljakusic D, Waterman E, Weyen J, Schondelmaier J, Habash DZ, Farmer P, Saker L, Clarkson DT, Abugalieva A, Yessimbekova M, Turuspekov Y, Abugalieva S, Tuberosa R, Sanguineti MC, Hollington PA, Aragués R, Royo A, Dodig D (2005) A high-density genetic map of hexaploid wheat (L.) from the cross Chinese Spring × SQ1 and its use to compare QTLs for grain yield across a range of environments. Theor Appl Genet 110:865–880

    Article  PubMed  CAS  Google Scholar 

  • Quilot B, Genard M, Kervella J, Lescourret F (2002) Ecophysiological analysis of genotypic variation in peach fruit growth. J Exp Bot 53:1613–1625

    Article  PubMed  CAS  Google Scholar 

  • Quilot B, Génard M, Kervella J, Lescourret F (2004) Analysis of genotypic variation in fruit flesh total sugar content via an ecophysiological model applied to peach. Theor Appl Genet 109:440–449

    PubMed  CAS  Google Scholar 

  • Raugh BL, Basten C, Buckler ES (2002) Quantitative trait loci analysis of growth response to varying nitrogen sources in Arabidopsis thaliana. Theor Appl Genet 104:743–750

    Article  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

    Article  PubMed  CAS  Google Scholar 

  • Rolland B, Bouchard C, Loyce C, Meynard JM, Guyomard H, Lonnet P, Doussinault G (2004) Des itinéraires techniques à bas niveaux d’intrants pour des variétés rustiques de blé tendre: une alternative pour concilier économie et environnement. Le Sélectionneur Français 54:3–20

    Google Scholar 

  • Sanguineti MC, Giuliani MM, Govi G, Tuberosa R, Landi P (1998) Root and shoot traits of maize inbred lines grown in the field and in hydroponic culture and their relationships with root lodging. Maydica 43:211–216

    Google Scholar 

  • SAS Institute Inc. (1999) SAS/STAT user’s guide, version 8. SAS Institute Inc., Cary

  • Tuberosa R, Sanguineti MC, Landi P, Giuliani MM, Salvi S, Conti S (2002) Identification of QTLs for root characteristics in maize grown in hydroponics and analysis of their overlap with QTLs for grain yield in the field at two water regimes. Plant Mol Breed 48:697–712

    Article  CAS  Google Scholar 

  • Tuberosa R, Salvi S, Sanguineti MC, Maccaferri M, Giuliani S, Landi P (2003) Searching for quantitative trait loci controlling root traits in maize: a critical appraisal. Plant Soil 255:35–54

    Article  CAS  Google Scholar 

  • Utz HF, Melchinger AE (1996) PLABQTL: a program for composite interval mapping of QTLs. J Quant Trait Loci 2:1

    Google Scholar 

  • Vales MI, Schön CC, Capettini F, Chen XM, Corey AE, Mather DE, Mundt CC, Richardson KL, Sandoval-Islas JS, Utz HF, Hayes PM (2005) Effect of population size on the estimation of QTL: a test using resistance to barley stripe rust. Theor Appl Genet 111:1260–1270

    Article  PubMed  CAS  Google Scholar 

  • Vision TJ, Brown DG, Shmoys DB, Durrett RT, Tanksley SD (2000) Selective mapping: a strategy for optimizing the construction of high-density linkage maps. Genetics 155:407–420

    PubMed  CAS  Google Scholar 

  • Wang LD, Liao H, Yan XL, Zhuang BC, Dong YS (2004) Genetic variability for root hair traits as related to phosphorus status in soybean. Plant Soil 261:77–84

    Article  CAS  Google Scholar 

  • Yin XY, Kropff MJ, Stam P (1999) The role of ecophysiological models in QTL analysis: the example of specific leaf area in barley. Heredity 82:415–421

    Article  PubMed  Google Scholar 

  • Yin X, Chasalow SD, Dourleijn CJ, Stam P, Kropff MJ (2000) Coupling estimated effects of QTLs for physiological traits to a crop growth model: predicting yield variation among recombinant inbred lines in barley. Heredity 85:539–549

    Article  PubMed  CAS  Google Scholar 

  • Zhu JM, Kaeppler SM, Lynch JP (2005) Mapping of QTL controlling root hair length in maize (Zea mays L.) under phosphorus deficiency. Plant Soil 270:299–310

    Article  CAS  Google Scholar 

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Acknowledgment

This work was backed by the Picardie region, Arvalis-Institut du Végétal and the “Génoplante” French Genomics project. The authors wish to thank A. Fortineau, J. Rodrigues, V. Bontems, S. Lauransot, V. Burban and H. Soyer for their helpful technical assistance, as well as Suzette Tanis-Plant for her thorough reading of the English manuscript as well as her overall editorial advice.

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Authors and Affiliations

  1. UMR INRA-INA PG Environnement et Grandes Cultures, 78850, Thiverval Grignon, France

    A. Laperche, F. Devienne-Barret, O. Maury & B. Ney

  2. INRA, Unité de Génétique et d’Amélioration des Plantes, BP 50136, 80203, Péronne Cedex, France

    A. Laperche & J. Le Gouis

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  1. A. Laperche
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  2. F. Devienne-Barret
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  3. O. Maury
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  4. J. Le Gouis
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  5. B. Ney
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Correspondence to A. Laperche.

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Communicated by P. Langridge.

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Laperche, A., Devienne-Barret, F., Maury, O. et al. A simplified conceptual model of carbon/nitrogen functioning for QTL analysis of winter wheat adaptation to nitrogen deficiency. Theor Appl Genet 113, 1131–1146 (2006). https://doi.org/10.1007/s00122-006-0373-4

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  • DOI: https://doi.org/10.1007/s00122-006-0373-4

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