Theoretical and Applied Genetics

, Volume 113, Issue 2, pp 206–224

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

DOI: 10.1007/s00122-006-0287-1

Cite this article as:
Blanc, G., Charcosset, A., Mangin, B. et al. Theor Appl Genet (2006) 113: 206. doi:10.1007/s00122-006-0287-1

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 (R2 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.

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