Details
| Original language | English |
|---|---|
| Pages (from-to) | 205-216 |
| Number of pages | 12 |
| Journal | Molecular breeding |
| Volume | 21 |
| Issue number | 2 |
| Publication status | Published - 29 Jul 2007 |
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 models, Facultative vernalization, Floral induction, G x E interactions, Time to flowering
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)
- Biotechnology
- Biochemistry, Genetics and Molecular Biology(all)
- Molecular Biology
- Agricultural and Biological Sciences(all)
- Agronomy and Crop Science
- Biochemistry, Genetics and Molecular Biology(all)
- Genetics
- Agricultural and Biological Sciences(all)
- Plant Science
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In: Molecular breeding, Vol. 21, No. 2, 29.07.2007, p. 205-216.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Crop model based QTL analysis across environments and QTL based estimation of time to floral induction and flowering in Brassica oleracea
AU - Uptmoor, Ralf
AU - Schrag, Tobias
AU - Stützel, Hartmut
AU - Esch, Elisabeth
PY - 2007/7/29
Y1 - 2007/7/29
N2 - 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.
AB - 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.
KW - Combining QTL and crop models
KW - Facultative vernalization
KW - Floral induction
KW - G x E interactions
KW - Time to flowering
UR - http://www.scopus.com/inward/record.url?scp=37549065187&partnerID=8YFLogxK
U2 - 10.1007/s11032-007-9121-y
DO - 10.1007/s11032-007-9121-y
M3 - Article
AN - SCOPUS:37549065187
VL - 21
SP - 205
EP - 216
JO - Molecular breeding
JF - Molecular breeding
SN - 1380-3743
IS - 2
ER -