Theoretical and Applied Genetics

, Volume 110, Issue 1, pp 106–115 | Cite as

Detection and mapping of QTL for earliness components in a bread wheat recombinant inbred lines population

  • E. Hanocq
  • M. Niarquin
  • E. Heumez
  • M. Rousset
  • J. Le Gouis
Original Paper


Earliness, an adaptative trait and factor of variation for agronomic characters, is a major trait in plant breeding. Its constituent traits, photoperiod sensitivity (PS), vernalization requirement (VR) and intrinsic earliness (IE), are largely under independent genetic controls. Mapping of major genes and quantitative trait loci (QTL) controlling these components is in progress. Most of the studies focusing on earliness considered it as a whole or through one (or two) of its components. The purpose of this study was to detect and map QTL for the three traits together through an experimental design combining field trials and controlled growth conditions. QTL were mapped in a population of F7 recombinant inbred lines derived by single-seed descent from a cross between two French varieties, ‘Renan’ and ‘Récital’. A map was previously constructed, based on 194 lines and 254 markers, covering about 77% of the genome. Globally, 13 QTL with a LOD>2.5 were detected, of which four control PS, five control VR and four control IE. Two major photoperiod sensitive QTL, together explaining more than 31% of the phenotypic variation, were mapped on chromosomes 2B and 2D, at the same position as the two major genes Ppd-B1 and Ppd-D1. One major VR QTL explaining (depending on the year) 21.8–39.6% of the phenotypic variation was mapped on 5A. Among the other QTL, two QTL of PS and VR not referenced so far were detected on 5A and 6D, respectively. A VR QTL already detected on 2B in a connected population was confirmed.


Quantitative Trait Locus Major Quantitative Trait Locus Quantitative Trait Locus Detection Head Date Photoperiod Sensitivity 
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 thank J.-P. Noclercq and O. Jaminon for their excellent technical assistance in growing plants in controlled conditions as well as D. Bouthors and D. Brasseur for their help in planting plants in field. We thank G. Doussinault for his help in planning the experimental design. We also thank L. Moreau and V. Fontaine for their valuable comments and suggestions on the manuscript.


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Copyright information

© Springer-Verlag 2004

Authors and Affiliations

  • E. Hanocq
    • 1
  • M. Niarquin
    • 1
  • E. Heumez
    • 1
  • M. Rousset
    • 1
  • J. Le Gouis
    • 1
  1. 1.Institut National de la Recherche Agronomique (INRA)Unité de Génétique et Amélioration des PlantesPéronne CedexFrance

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