Theoretical and Applied Genetics

, Volume 113, Issue 6, pp 1131–1146

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

  • A. Laperche
  • F. Devienne-Barret
  • O. Maury
  • J. Le Gouis
  • B. Ney
Original Paper


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.



Aerial dry matter


Doubled haploids




Leaf area


Linkage group


Lateral root length


Lateral root number




Aerial parts nitrogen content


Root nitrogen content


Amount of total nitrogen


Nitrogen-specific uptake rate


Photosynthetically active radiation


Primary root length


Quantitative trait locus


Root dry matter


Specific root length


Simple sequence repeats


Total dry matter


Total root length


Radiation use efficiency


  1. Agrama HAS, Zakaria AG, Said FB, Tuinstra M (1999) Identification of quantitative trait loci for nitrogen use efficiency in maize. Mol Breed 5:187–195CrossRefGoogle Scholar
  2. 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 168:81–87CrossRefGoogle Scholar
  3. Bänziger M, Betran FJ, Lafitte HR (1997) Efficiency of high-nitrogen selection environments for improving maize for low-nitrogen target environments. Crop Sci 37:1103–1109CrossRefGoogle Scholar
  4. 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 65–66Google Scholar
  5. Basten CJ, Weir BS, Zeng ZB (2002) QTL cartographer version 1.16Google Scholar
  6. Bertin P, Gallais A (2001) Genetic variation for nitrogen use efficiency in a set of recombinant inbred lines. II—QTL detection and coincidences. Maydica 46:53–68Google Scholar
  7. 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 110:932–940PubMedCrossRefGoogle Scholar
  8. 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 45:1427–1431CrossRefGoogle Scholar
  9. Bush MG, Evans LT (1988) Growth and development in tall and dwarf isogenic lines of spring wheat. Field Crops Res 18:243–270CrossRefGoogle Scholar
  10. 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 111:540–550PubMedCrossRefGoogle Scholar
  11. Churchill GA, Doerge RW (1994) Empirical threshold values for quantitative trait mapping. Genetics 138:963–971PubMedGoogle Scholar
  12. Dumas JBA (1831) Procédés de l’analyse organique. Ann Chim Phys 2:198–213Google Scholar
  13. 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 105:1038–1042PubMedCrossRefGoogle Scholar
  14. Gallais A, Hirel B (2004) An approach to the genetics of nitrogen use efficiency in maize. J Exp Bot 55:295–306PubMedCrossRefGoogle Scholar
  15. 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 47:705–713PubMedCrossRefGoogle Scholar
  16. 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 125:1258–1270PubMedCrossRefGoogle Scholar
  17. Jannink JL (2005) Selective phenotyping to accurately map quantitative trait loci. Crop Sci 45:901–908CrossRefGoogle Scholar
  18. 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 1:174–181PubMedCrossRefGoogle Scholar
  19. 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 43:111–116Google Scholar
  20. 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 47:233–243Google Scholar
  21. 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 112:797–807PubMedCrossRefGoogle Scholar
  22. 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 12:163–173CrossRefGoogle Scholar
  23. 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 110:1244–1252PubMedCrossRefGoogle Scholar
  24. 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 112:85–96PubMedCrossRefGoogle Scholar
  25. 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 163:711–722Google Scholar
  26. Loudet O, Chaillou S, Merigout P, Talbotec J, Daniel-Vedele F (2003b) Quantitative trait loci analysis of nitrogen use efficiency in Arabidopsis. Plant Physiol 131:345–358CrossRefGoogle Scholar
  27. McCaig TN, Morgan JA (1993) Root and shoot dry matter partitioning in near-isogenic wheat lines differing in height. Can J Plant Sci 73:679–689Google Scholar
  28. Mian MAR, Nafziger ED, Kolb FL, Teyker RH (1994) Root size and distribution of field-grown wheat genotypes. Crop Sci 34:810–812CrossRefGoogle Scholar
  29. 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 54:801–812PubMedCrossRefGoogle Scholar
  30. Miralles DJ, Slafer GA, Lynch V (1997) Rooting patterns in near-isogenic lines of spring wheat for dwarfism. Plant Soil 197:79–86CrossRefGoogle Scholar
  31. 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 107:1243–1251PubMedCrossRefGoogle Scholar
  32. 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 70:1840–1847CrossRefGoogle Scholar
  33. 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 106:659–667PubMedGoogle Scholar
  34. Price AH, Tomos AD, Virk DS (1997) Genetic dissection of root growth in rice (Oryza sativa L.) I: a hydroponic screen. Theor Appl Genet 95:132–142CrossRefGoogle Scholar
  35. 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 110:865–880PubMedCrossRefGoogle Scholar
  36. Quilot B, Genard M, Kervella J, Lescourret F (2002) Ecophysiological analysis of genotypic variation in peach fruit growth. J Exp Bot 53:1613–1625PubMedCrossRefGoogle Scholar
  37. 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 109:440–449PubMedGoogle Scholar
  38. Raugh BL, Basten C, Buckler ES (2002) Quantitative trait loci analysis of growth response to varying nitrogen sources in Arabidopsis thaliana. Theor Appl Genet 104:743–750CrossRefGoogle Scholar
  39. 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 131:664–675PubMedCrossRefGoogle Scholar
  40. 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 54:3–20Google Scholar
  41. 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 43:211–216Google Scholar
  42. SAS Institute Inc. (1999) SAS/STAT user’s guide, version 8. SAS Institute Inc., CaryGoogle Scholar
  43. 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 48:697–712CrossRefGoogle Scholar
  44. 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 255:35–54CrossRefGoogle Scholar
  45. Utz HF, Melchinger AE (1996) PLABQTL: a program for composite interval mapping of QTLs. J Quant Trait Loci 2:1Google Scholar
  46. 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 111:1260–1270PubMedCrossRefGoogle Scholar
  47. 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 155:407–420PubMedGoogle Scholar
  48. 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 261:77–84CrossRefGoogle Scholar
  49. Yin XY, Kropff MJ, Stam P (1999) The role of ecophysiological models in QTL analysis: the example of specific leaf area in barley. Heredity 82:415–421PubMedCrossRefGoogle Scholar
  50. 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 85:539–549PubMedCrossRefGoogle Scholar
  51. Zhu JM, Kaeppler SM, Lynch JP (2005) Mapping of QTL controlling root hair length in maize (Zea mays L.) under phosphorus deficiency. Plant Soil 270:299–310CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  • A. Laperche
    • 1
    • 2
  • F. Devienne-Barret
    • 1
  • O. Maury
    • 1
  • J. Le Gouis
    • 2
  • B. Ney
    • 1
  1. 1.UMR INRA-INA PG Environnement et Grandes CulturesThiverval GrignonFrance
  2. 2.INRA, Unité de Génétique et d’Amélioration des PlantesPéronne CedexFrance

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