A framework for genomics-informed ecophysiological modeling in plants
| Autor: | J. R. Pleban, D. Scott Mackay, Robert L. Baker, Brent E. Ewers, Cynthia Weinig, Carmela R. Guadagno, Diane R. Wang, Jean-Luc Jannink, Xiaowei Mao |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0106 biological sciences
0301 basic medicine Genotype Physiology Process (engineering) In silico growth Population G×E Genomics Plant Science Variation (game tree) Computational biology Biology 01 natural sciences Intraspecific competition 03 medical and health sciences Stress Physiological Genetic variation Brassica rapa Gene–environment interaction education development genomic prediction Ecosystem education.field_of_study Models Genetic Plants Abiotic stress Research Papers process-based models 030104 developmental biology 010606 plant biology & botany |
| Zdroj: | Journal of Experimental Botany |
| ISSN: | 1460-2431 0022-0957 |
| Popis: | Genomic prediction is used to parameterize canopy growth of a process-based whole-plant model. The updated model formalizes components of genotype by environment interaction (G×E). Dynamic process-based plant models capture complex physiological response across time, carrying the potential to extend simulations out to novel environments and lend mechanistic insight to observed phenotypes. Despite the translational opportunities for varietal crop improvement that could be unlocked by linking natural genetic variation to first principles-based modeling, these models are challenging to apply to large populations of related individuals. Here we use a combination of model development, experimental evaluation, and genomic prediction in Brassica rapa L. to set the stage for future large-scale process-based modeling of intraspecific variation. We develop a new canopy growth submodel for B. rapa within the process-based model Terrestrial Regional Ecosystem Exchange Simulator (TREES), test input parameters for feasibility of direct estimation with observed phenotypes across cultivated morphotypes and indirect estimation using genomic prediction on a recombinant inbred line population, and explore model performance on an in silico population under non-stressed and mild water-stressed conditions. We find evidence that the updated whole-plant model has the capacity to distill genotype by environment interaction (G×E) into tractable components. The framework presented offers a means to link genetic variation with environment-modulated plant response and serves as a stepping stone towards large-scale prediction of unphenotyped, genetically related individuals under untested environmental scenarios. |
| Databáze: | OpenAIRE |
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