Theoretical and Applied Genetics

, Volume 126, Issue 5, pp 1337–1356 | Cite as

Combined linkage and association mapping of flowering time in Sunflower (Helianthus annuus L.)

  • Elena CadicEmail author
  • Marie Coque
  • Felicity Vear
  • Bruno Grezes-Besset
  • Jerôme Pauquet
  • Joël Piquemal
  • Yannick Lippi
  • Philippe Blanchard
  • Michel Romestant
  • Nicolas Pouilly
  • David Rengel
  • Jerôme Gouzy
  • Nicolas Langlade
  • Brigitte Mangin
  • Patrick VincourtEmail author
Original Paper


Association mapping and linkage mapping were used to identify quantitative trait loci (QTL) and/or causative mutations involved in the control of flowering time in cultivated sunflower Helianthus annuus. A panel of 384 inbred lines was phenotyped through testcrosses with two tester inbred lines across 15 location × year combinations. A recombinant inbred line (RIL) population comprising 273 lines was phenotyped both per se and through testcrosses with one or two testers in 16 location × year combinations. In the association mapping approach, kinship estimation using 5,923 single nucleotide polymorphisms was found to be the best covariate to correct for effects of panel structure. Linkage disequilibrium decay ranged from 0.08 to 0.26 cM for a threshold of 0.20, after correcting for structure effects, depending on the linkage group (LG) and the ancestry of inbred lines. A possible hitchhiking effect is hypothesized for LG10 and LG08. A total of 11 regions across 10 LGs were found to be associated with flowering time, and QTLs were mapped on 11 LGs in the RIL population. Whereas eight regions were demonstrated to be common between the two approaches, the linkage disequilibrium approach did not detect a documented QTL that was confirmed using the linkage mapping approach.


Quantitative Trait Locus Single Nucleotide Polymorphism Flowering Time Recombinant Inbred Line Association Mapping 
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 would like to thank M.C. Boniface and D. Varès (INRA Toulouse), H. Bony, G. Joubert, F. Serre, S. Roche and J. Philippon (INRA Clermont-Ferrand), Th. André (SOLTIS), S. Châtre (RAGT), P. George and M. Barthes (BIOGEMMA) and colleagues from SYNGENTA Seeds for their involvement in sunflower trial management. This work benefited from the GENOPLANTE program “HP1” (2001–2004), the “SUNYFUEL” project, financially supported by the French National Research Agency (2008–2011), and the “OLEOSOL” project (2009–2012) with the financial support from the Midi Pyrénées Region, the European Fund for Regional Development (EFRD), and the French Fund for Competitiveness Clusters (FUI).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Elena Cadic
    • 1
    • 2
    • 3
    Email author
  • Marie Coque
    • 3
    • 5
  • Felicity Vear
    • 4
  • Bruno Grezes-Besset
    • 3
  • Jerôme Pauquet
    • 3
  • Joël Piquemal
    • 5
  • Yannick Lippi
    • 1
    • 2
  • Philippe Blanchard
    • 6
  • Michel Romestant
    • 7
  • Nicolas Pouilly
    • 1
    • 2
  • David Rengel
    • 1
    • 2
  • Jerôme Gouzy
    • 1
    • 2
  • Nicolas Langlade
    • 1
    • 2
  • Brigitte Mangin
    • 8
  • Patrick Vincourt
    • 1
    • 2
    Email author
  1. 1.Laboratoire des Interactions Plantes-Microorganismes (LIPM)INRA, UMR441Castanet-TolosanFrance
  2. 2.Laboratoire des Interactions Plantes-Microorganismes (LIPM)CNRS, UMR2594Castanet-TolosanFrance
  3. 3.BIOGEMMA SAS, Domaine de Sandreau, MondonvilleBlagnacFrance
  4. 4.INRA, UMR 1095Domaine de CrouelleClermont-FerrandFrance
  5. 5.SYNGENTA SEEDSSaint SauveurFrance
  6. 6.EURALIS SemencesDomaine de SandreauBlagnacFrance
  7. 7.RAGT 2NRodezFrance
  8. 8.INRA, Unité de Biométrie et Intelligence Artificielle UR875Castanet-TolosanFrance

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