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Molecular Breeding

, Volume 31, Issue 3, pp 587–599 | Cite as

Identifying wheat genomic regions for improving grain protein concentration independently of grain yield using multiple inter-related populations

  • Matthieu Bogard
  • Vincent Allard
  • Pierre Martre
  • Emmanuel Heumez
  • John W. Snape
  • Simon Orford
  • Simon Griffiths
  • Oorbessy Gaju
  • John Foulkes
  • Jacques Le Gouis
Article

Abstract

Grain yield (GY) and grain protein concentration (GPC) are two major traits contributing to the economic value of the wheat crop. These are, consequently, major targets in wheat breeding programs, but their simultaneous improvement is hampered by the negative correlation between GPC and GY. Identifying the genetic determinants of GPC and GY through quantitative trait loci (QTL) analysis would be one way to identify chromosomal regions, allowing improvement of GPC without reducing GY using marker-assisted selection. Therefore, QTL detection was carried out for GY and GPC using three inter-connected doubled haploid populations grown in a large multi-environment trial network. Chromosomes 2A, 2D, 3B, 7B and 7D showed co-location of QTL for GPC and GY with antagonistic effects, thus contributing to the negative GPC–GY relationship. Nonetheless, genomic regions determining GPC independently of GY across experiments were found on chromosomes 3A and 5D and could help breeders to move the GPC–GY relationship in a desirable direction.

Keywords

Grain protein concentration Grain yield QTL MCQTL Triticum aestivum

Abbreviations

GPC

Grain protein concentration

GY

Grain yield

QTL

Quantitative trait loci

Notes

Acknowledgments

This work was supported by the “NUE traits” project [IN-BB-06] (http://www.rothamsted.bbsrc.ac.uk/bab/mas-projects/NUE.html); funded by the Institut National de la Recherche Agronomique (INRA) and the UK Biotechnology and Biological Sciences Research Council (BBSRC). Authors gratefully acknowledge the experimental work carried out by Joëlle Messaoud, Séverine Rougeol, Jean-Louis Joseph and Pascal Lemaire (INRA, Clermont-Ferrand), Dominique Brasseur (INRA, Estrées-Mons), Rob Perdue (University of Nottingham, Sutton Bonington), and Lesley Fish (John Innes Centre, Norwich).

Supplementary material

11032_2012_9817_MOESM1_ESM.doc (458 kb)
The online version of this article contains supplementary material (Table S1 and Figure S1). Results of QTL detection are publicly available at http://urgi.versailles.inra.fr/GnpMap/mapping/qtl/queryQtlSelect.do (DOC 458 kb)

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Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • Matthieu Bogard
    • 1
    • 2
  • Vincent Allard
    • 1
    • 2
  • Pierre Martre
    • 1
    • 2
  • Emmanuel Heumez
    • 3
  • John W. Snape
    • 4
  • Simon Orford
    • 4
  • Simon Griffiths
    • 4
  • Oorbessy Gaju
    • 5
  • John Foulkes
    • 5
  • Jacques Le Gouis
    • 1
    • 2
  1. 1.INRA, UMR 1095 Génétique, Diversité et Ecophysiologie des CéréalesClermont-FerrandFrance
  2. 2.UMR 1095 Génétique, Diversité et Ecophysiologie des CéréalesUniversité Blaise PascalAubière CedexFrance
  3. 3.INRA, UMR 1281 Stress Abiotiques et Différenciation des Végétaux Cultivés, Estrées-MonsPéronneFrance
  4. 4.Crop Genetics DepartmentJohn Innes CentreNorwichUK
  5. 5.Division of Agricultural SciencesUniversity of NottinghamLeicestershireUK

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