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ARS Home » Northeast Area » Ithaca, New York » Robert W. Holley Center for Agriculture & Health » Plant, Soil and Nutrition Research » Research » Publications at this Location » Publication #365717

Research Project: Database Tools for Managing and Analyzing Big Data Sets to Enhance Small Grains Breeding

Location: Plant, Soil and Nutrition Research

Title: A framework for genomics-informed ecophysiological modeling in plants

item WANG, DIANE - University Of Buffalo
item GUADAGNO, CARMELA - University Of Wyoming
item MAO, XIAOWEI - Cornell University - New York
item MACKAY, SCOTT - University Of Buffalo
item PLEBAN, JONATHAN - University Of Buffalo
item BAKER, ROBERT - Miami University - Ohio
item WEINIG, CYNTHIA - University Of Wyoming
item Jannink, Jean-Luc
item EWERS, BRENT - University Of Wyoming

Submitted to: Journal of Experimental Botany
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/2/2019
Publication Date: 4/15/2019
Citation: Wang, D.R., Guadagno, C.R., Mao, X., Mackay, S.D., Pleban, J.R., Baker, J.R., Weinig, C., Jannink, J., Ewers, B.E. 2019. A framework for genomics-informed ecophysiological modeling in plants. Journal of Experimental Botany. 70(9):2561-2574.

Interpretive Summary: Crop breeding may find a useful tool in computer simulation models of plant growth. We develop a new canopy growth model and explore its predictions under non-stressed and mild water-stressed conditions. We find evidence that the updated plant growth model can distill plant genotype interactions with the growth environment into components amenable to breeding. This also represents progress toward prediction of new breeding lines under untested environmental scenarios, potentially improving breeders’ ability to adapt varieties to changing environments.

Technical Abstract: 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.