Molecular Breeding

, Volume 21, Issue 2, pp 205–216

Crop model based QTL analysis across environments and QTL based estimation of time to floral induction and flowering in Brassica oleracea

Authors

    • Institute of Biological Production SystemsLeibniz Universität Hannover
  • Tobias Schrag
    • Institute of Biological Production SystemsLeibniz Universität Hannover
    • Institute of Plant GeneticsLeibniz Universität Hannover
    • Institute of Plant Breeding, Seed Science and Population GeneticsUniversity of Hohenheim
  • Hartmut Stützel
    • Institute of Biological Production SystemsLeibniz Universität Hannover
  • Elisabeth Esch
    • Institute of Plant GeneticsLeibniz Universität Hannover
Article

DOI: 10.1007/s11032-007-9121-y

Cite this article as:
Uptmoor, R., Schrag, T., Stützel, H. et al. Mol Breeding (2008) 21: 205. doi:10.1007/s11032-007-9121-y

Abstract

Studying quantitative traits is complicated due to genotype by environment interactions. One strategy to overcome these difficulties is to combine quantitative trait loci (QTL) and ecophysiological models, e.g. by identifying QTLs for the response curves of adaptive traits to influential environmental factors. A B. oleracea DH-population segregating for time to flowering was cultivated at different temperature regimes. Composite interval mapping was carried out on the three parameters of a model describing time to flowering as a function of temperature, i.e. on the intercept and slope of the response of time to floral induction to temperature and on the duration from transition to flowering. The additive effects of QTLs detected for the parameters have been used to estimate time to floral induction and flowering in the B. oleracea DH-population. The combined QTL and crop model explained 66% of the phenotypic variation for time to floral induction and 56% of the phenotypic variation for time to flowering. Estimation of time to floral induction and flowering based on environment specific QTLs explained 61 and 41% of the phenotypic variation. Results suggest that flowering time can be predicted effectively by coupling QTL and crop models and that using crop modelling tools for QTL analysis increases the power of QTL detection.

Keywords

Combining QTL and crop modelsTime to floweringFloral inductionFacultative vernalizationG × E interactions

Copyright information

© Springer Science+Business Media B.V. 2007