QTL analysis and QTL-based prediction of flowering phenology in recombinant inbred lines of barley

• Published in: Journal of experimental botany 2005-03, Vol.56 (413), p.967-976
• Main Authors: Yin, Xinyou, Struik, Paul C., van Eeuwijk, Fred A., Stam, Piet, Tang, Jianjun
• Format: Article
• Language: English
• Subjects: Barley; Biological and medical sciences; Biology and morphogenesis of the reproductive apparatus. Photoperiodism, vernalisation; Chromosome Mapping; Chromosomes; Ecophysiological modelling; Ecophysiology; flowering time; Flowers - genetics; Flowers - growth & development; Fundamental and applied biological sciences. Psychology; Genetic loci; Genotypes; genotype–phenotype relationship; growth; Hordeum - genetics; Hordeum - growth & development; Hordeum - physiology; Hordeum vulgare; Hordeum vulgare L; Inbreeding; Linear Models; Logistic Models; model-input traits; Modeling; Models, Biological; Parametric models; Phenology; Phenotype; Phenotypic traits; photoperiod; Plant physiology and development; Quantitative trait loci; Quantitative Trait Loci - physiology; RESEARCH PAPER; temperatures; Time Factors; Vegetative and sexual reproduction, floral biology, fructification; water-deficit; yield
• ISSN: 0022-0957; 1460-2431; 1460-2431
• DOI: 10.1093/jxb/eri090

Combining ecophysiological modelling and genetic mapping has increasingly received attention from researchers who wish to predict complex plant or crop traits under diverse environmental conditions. The potential for using this combined approach to predict flowering time of individual genotypes in a recombinant inbred line (RIL) population of spring barley (Hordeum vulgare L.) was examined. An ecophysiological phenology model predicts preflowering duration as affected by temperature and photoperiod, based on the following four input traits: fo (the minimum number of days to flowering at the optimum temperature and photoperiod), θ1 and θ2 (the development stages for the start and the end of the photoperiod-sensitive phase, respectively), and δ (the photoperiod sensitivity). The model-input trait values were obtained from a photoperiod-controlled greenhouse experiment. Assuming additivity of QTL effects, a multiple QTL model was fitted for the model-input traits using composite interval mapping. Four to seven QTL were identified for each trait. Each trait had at least one QTL specific to that trait alone. Other QTL were shared by two or all traits. Values of the model-input traits predicted for the RILs from the QTL model were fed back into the ecophysiological model. This QTL-based ecophysiological model was subsequently used to predict preflowering duration (d) for eight field trial environments. The model accounted for 72% of the observed variation among 94 RILs and 94% of the variation among the two parents across the eight environments, when observations in different environments were pooled. However, due to the low percentage (34–41%) of phenotypic variation accounted for by the identified QTL for three model-input traits (θ1, θ2 and δ), the QTL-based model accounted for somewhat less variation among the RILs than the model using original phenotypic input trait values. Nevertheless, days to flowering as predicted from the QTL-based ecophysiological model were highly correlated with days to flowering as predicted from QTL-models per environment for days to flowering per se. The ecophysiological phenology model was thus capable of extrapolating (QTL) information from one environment to another.