Predictions of heading date in bread wheat (Triticum aestivum L.) using QTL-based parameters of an ecophysiological model
Open accessAbstract
Prediction of wheat phenology facilitates the selection of cultivars with specific adaptations to a particular environment. However, while QTL analysis for heading date can identify major genes controlling phenology, the results are limited to the environments and genotypes tested. Moreover, while ecophysiological models allow accurate predictions in new environments, they may require substantial phenotypic data to parameterize each genotype. Also, the model parameters are rarely related to all underlying genes, and all the possible allelic combinations that could be obtained by breeding cannot be tested with models. In this study, a QTL-based model is proposed to predict heading date in bread wheat (Triticum aestivum L.). Two parameters of an ecophysiological model (V sat and P base , representing genotype vernalization requirements and photoperiod sensitivity, respectively) were optimized for 210 genotypes grown in 10 contrasting location × sowing date combinations. Multiple linear regression models predicting V sat and P base with 11 and 12 associated genetic markers accounted for 71 and 68% of the variance of these parameters, respectively. QTL-based V sat and P base estimates were able to predict heading date of an independent validation data set (88 genotypes in six location × sowing date combinations) with a root mean square error of prediction of 5 to 8.6 days, explaining 48 to 63% of the variation for heading date. The QTL-based model proposed in this study may be used for agronomic purposes and to assist breeders in suggesting locally adapted ideotypes for wheat phenology.
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Component trait QTL have been successfully used to parametrize CGMs for studying genotype dependent response to environmental conditions.
“In fact, such component trait QTL have been successfully used to parametrize CGMs for studying genotype dependent response to environmental conditions [28, 29, 73, 94, 95].”
From Integrating Crop Growth Models with Whole Genome Prediction through Approximate Bayesian Computation (2015) · cites this paper as [94]
Component physiological traits may be realistically modeled based on a relatively simple genetic architecture, and for such traits QTL explaining a sizable proportion of genetic variance can be mapped and characterized.
“In contrast to the complex trait of interest, component physiological traits may be realistically modeled based on a relatively simple genetic architecture, and for such traits, QTL explaining a sizable proportion of genetic variance can be mapped and characterized [71, 93–96].”
From Integrating Crop Growth Models with Whole Genome Prediction through Approximate Bayesian Computation (2015) · cites this paper as [94]
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Technow, F., Messina, C. D., Totir, L. R., & Cooper, M. (2015) · PLOS ONE · doi:10.1371/journal.pone.0130855 · Cited by 265 · see citing works
“In fact, such component trait QTL have been successfully used to parametrize CGMs for studying genotype dependent response to environmental conditions [28, 29, 73, 94, 95].”
Methodvan Eeuwijk, F. A., Bustos-Korts, D., Millet, E. J., Boer, M. P., Kruijer, W., Thompson, A., Malosetti, M., Iwata, H., Quiroz, R., Kuppe, C., et al. (2019) · Plant Science · doi:10.1016/j.plantsci.2018.06.018 · Cited by 217 · see citing works
Self-citationZheng, B., Chenu, K., & Chapman, S. C. (2016) · Global Change Biology · doi:10.1111/gcb.13118 · Cited by 64 · see citing works
Self-citationBogard, M., Biddulph, B., Zheng, B., Hayden, M., Kuchel, H., Mullan, D., Allard, V., Gouis, J. L., & Chapman, S. C. (2020) · Crop Science · doi:10.1002/csc2.20113 · Cited by 29 · see citing works
Self-citation
- Authors
- Bogard, M., Ravel, C., Paux, E., Bordes, J., Balfourier, F., Chapman, S. C., Le Gouis, J., & Allard, V.
- Affiliations
- Génétique, Diversité, Écophysiologie des Céréales (FR)
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Corresponding author
- Allard, V.
- Venue
- Journal of Experimental Botany
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-08-22
- Volume
- 65
- Issue
- 20
- Pages
- 5849-5865
- ISSN
- 0022-0957 (ISSN-L)
- 1460-2431
- PMC
- PMC4203124
- OpenAlex
- W2167486287
- Cited by
- 87
- References
- 3
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 6 · 2023: 5 · 2022: 9 · 2021: 9 · 2020: 8 · 2019: 5 · 2018: 8 · 2017: 3 · 2016: 16 · 2015: 12 · 2014: 1
- FWCI
- 12.47
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Journal of Experimental Botany (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion, cc-by)
- HighWire Press Open Archive (submittedVersion)
- Funders
- Agence Nationale de la Recherche
- Institut National de la Recherche Agronomique (UMR 1095)
- FP7 Food, Agriculture and Fisheries, Biotechnology
- Topics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Wheat and Barley Genetics and Pathology
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Genetics and Plant Breeding
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- phenology prediction, ecophysiological model, vernalization requirement, photoperiod sensitivity, genetic markers, wheat
- MeSH terms
- Adaptation, Physiological, Breeding, Environment, Genotype, Models, Biological, Phenotype, Time Factors, Triticum, Photoperiod, Genome, Plant, Flowers, Quantitative Trait Loci
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