Arne Rosen, Yaser Hasan, William Briggs, Ralf Uptmoor
Abstract
The development of cauliflower (Brassica oleraceavar.botrytis) is highly dependent on temperature due to vernalization requirements, which often causes delay and unevenness in maturity during months with warm temperatures. Integrating quantitative genetic analyses with phenology modeling was suggested to accelerate breeding strategies toward wide-adaptation cauliflower. The present study aims at establishing a genome-based model simulating the development of doubled haploid (DH) cauliflower lines to predict time to curd induction of DH lines not used for model parameterization and test hybrids derived from the bi-parental cross. Leaf appearance rate and the relation between temperature and thermal time to curd induction were examined in greenhouse trials on 180 DH lines at seven temperatures. Quantitative trait loci (QTL) analyses carried out on model parameters revealed ten QTL for leaf appearance rate (LAR), five for the slope and two for the intercept of linear temperature-response functions. Results of the QTL-based phenology model were compared to a genomic selection (GS) model. Model validation was carried out on data comprising four field trials with 72 independent DH lines, 160 hybrids derived from the parameterization set, and 34 hybrids derived from independent lines of the population. The QTL model resulted in a moderately accurate prediction of time to curd induction (R2= 0.42-0.51) while the GS model generated slightly better results (R2= 0.52-0.61). Predictions of time to curd induction of test hybrids from independent DH lines were less precise withR2= 0.40 for the QTL andR2= 0.48 for the GS model. Implementation of juvenile-to-adult phase transition is proposed for model improvement.
Pharmacology, Toxicology and Pharmaceutical Science
1
4%
Biochemistry, Genetics and Molecular Biology
1
4%
Psychology
1
4%
Unknown
9
33%
Attention Score in Context
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 March 2018.
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#14,969,772
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Outputs from Frontiers in Plant Science
#9,428
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#254,351
of 437,345 outputs
Outputs of similar age from Frontiers in Plant Science
#253
of 452 outputs
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