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Data-Driven Identification of Environmental Variables Influencing Phenotypic Plasticity to Facilitate Breeding for Future Climates: A Case Study Involving Grain Yield of Hybrid Maize

276 Pages Posted: 2 Oct 2020 Publication Status: Review Complete

See all articles by Aaron Kusmec

Aaron Kusmec

Iowa State University - Department of Agronomy

Cheng-Ting "Eddy" Yeh

Iowa State University - Department of Agronomy

The Genomes to Fields Initiative

Independent

Patrick S. Schnable

Iowa State University - Department of Agronomy

More...

Abstract

Phenotypic plasticity describes the ability of a genotype to produce different phenotypes in response to different environments. A key component for the quantification of phenotypic plasticity is the set of environmental variables that influence a particular phenotype. These variables are typically selected using domain-specific knowledge or, when the set of variables is suitably small, exhaustive search. Two factors complicate these strategies. First, environments are shifting and becoming more variable due to global climate change which may introduce novel stresses that are not yet captured by domain-specific knowledge. Second, environments are inherently infinite-dimensional not only in terms of the variables that can be measured and their temporal resolution but also on the timescales at which organisms perceive different environmental variables throughout development. This size makes exhaustive search unfeasible without potentially erroneous simplifying assumptions, especially when assessing the simultaneous influence of multiple environmental variables on a phenotype. To address these challenges, we propose the use of a genetic algorithm to efficiently identify informative sets of environmental variables for the quantification of phenotypic plasticity. We apply this procedure to a hybrid maize dataset and demonstrate its utility for characterizing phenotypic plasticity and identifying directions for future research into the biology of plastic responses.

Keywords: genotype-environment interactions, phenotypic plasticity, maize, genetic algorithm, environmental covariates, Climate Change, breeding for future climates

Suggested Citation

Kusmec, Aaron and Yeh, Cheng-Ting "Eddy" and Initiative, The Genomes to Fields and Schnable, Patrick S., Data-Driven Identification of Environmental Variables Influencing Phenotypic Plasticity to Facilitate Breeding for Future Climates: A Case Study Involving Grain Yield of Hybrid Maize. Available at SSRN: https://ssrn.com/abstract=3684755 or http://dx.doi.org/10.2139/ssrn.3684755
This version of the paper has not been formally peer reviewed.

Aaron Kusmec

Iowa State University - Department of Agronomy ( email )

United States

Cheng-Ting "Eddy" Yeh

Iowa State University - Department of Agronomy ( email )

United States

Patrick S. Schnable (Contact Author)

Iowa State University - Department of Agronomy ( email )

United States

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