Crop model based QTL analysis across environments and QTL based estimation of time to floral induction and flowering in <Emphasis Type="Italic">Brassica oleracea</Emphasis> |
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Authors: | Ralf Uptmoor Tobias Schrag Hartmut Stützel Elisabeth Esch |
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Institution: | 1.Institute of Biological Production Systems,Leibniz Universit?t Hannover,Hannover,Germany;2.Institute of Plant Genetics,Leibniz Universit?t Hannover,Hannover,Germany;3.Institute of Plant Breeding, Seed Science and Population Genetics,University of Hohenheim,Stuttgart,Germany |
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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. |
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Keywords: | Combining QTL and crop models Time to flowering Floral induction Facultative vernalization G × E interactions |
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