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Theoretical and Applied Genetics

, Volume 113, Issue 2, pp 206–224 | Cite as

Connected populations for detecting quantitative trait loci and testing for epistasis: an application in maize

  • G. Blanc
  • A. Charcosset
  • B. Mangin
  • A. Gallais
  • L. Moreau
Original Paper

Abstract

Quantitative trait loci (QTL) detection experiments have often been restricted to large biallelic populations. Use of connected multiparental crosses has been proposed to increase the genetic variability addressed and to test for epistatic interactions between QTL and the genetic background. We present here the results of a QTL detection performed on six connected F2 populations of 150 F2:3 families each, derived from four maize inbreds and evaluated for three traits of agronomic interest. The QTL detection was carried out by composite interval mapping on each population separately, then on the global design either by taking into account the connections between populations or not. Epistatic interactions between loci and with the genetic background were tested. Taking into account the connections between populations increased the number of QTL detected and the accuracy of QTL position estimates. We detected many epistatic interactions, particularly for grain yield QTL (R 2 increase of 9.6%). Use of connections for the QTL detection also allowed a global ranking of alleles at each QTL. Allelic relationships and epistasis both contribute to the lack of consistency for QTL positions observed among populations, in addition to the limited power of the tests. The potential benefit of assembling favorable alleles by marker-assisted selection are discussed.

Keywords

Quantitative Trait Locus Epistatic Interaction Epistatic Effect Quantitative Trait Locus Effect Allelic Effect 
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.

Notes

Acknowledgments

This research program was funded by INRA and Agriobtention. We are grateful to our colleagues involved in marker analyses at le Moulon (D. Madur, V. Combes, F. Dumas), and colleagues involved in material production and field testing at INRA le Moulon (P. Bertin, P. Jamin, D. Coubriche, S. Jouanne), at INRA Dreux, at INRA Rennes, at INRA Lusignan and at INRA Mons. We also thank the BIA unit at INRA Toulouse for the maintenance of MCQTL software (B. Mangin, B. Ngom, J. Marcel). We are grateful to Guy Decoux for the informatic program used for the graphical display of the maps. We are grateful to Rex Bernardo for very helpful advices on the writing of this manuscript.

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Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  • G. Blanc
    • 1
  • A. Charcosset
    • 1
  • B. Mangin
    • 2
  • A. Gallais
    • 1
  • L. Moreau
    • 1
  1. 1.INRA/INA-PG/UPS/CNRS, UMR de Génétique VégétaleGif sur YvetteFrance
  2. 2.INRA, Unité de Biométrie et Intelligence ArtificielleCastanet Tolosan CedexFrance

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