Data-driven identification of environmental variables influencing phenotypic plasticity to facilitate breeding for future climates: a case study involving grain yield of hybrid maize

Authors: KUSMEC, AARON - Iowa State University; YEH, CHENG-TING - Iowa State University; NETTLETON, DAN - Iowa State University; ALKHALIFAH, NASER - University Of Wisconsin; BOHN, MARTIN - University Of Illinois; Buckler Iv, Edward; CAMPBELL, DARWIN - Iowa State University; CIAMPITTI, IGNACIO - Kansas State University; ERTL, DAVID - Iowa Corn Promotion Board; Flint Garcia, Sherry; GARDINER, JACK - University Of Missouri; GORE, MICHAEL - Cornell University; HIRSCH, CANDICE - University Of Minnesota; KAEPPLER, SHAWN - University Of Wisconsin; Knoll, Joseph; KOLKMAN, JUDITH - Cornell University; KRUGER, GREG - University Of Nebraska; Lauter, Nicholas; LAWRENCE-DILL, CAROLYN - Iowa State University; LEE, ELIZABETH - University Of Guelph; DE LEON, NATALIA - University Of Wisconsin; LIU, SANZHEN - Kansas State University; LORENCE, ARGELIA - Arkansas State University; MCFARLAND, BRIDGET - University Of Wisconsin; POUDYA, CHRISTINA - University Of Minnesota; ROMAY, MARIA CINTA - Cornell University; SCHNABLE, JAMES - University Of Nebraska; SEKHON, RAJANDEEP - Clemson University; SILVERSTEIN, KEVIN - University Of Minnesota; SMITH, MARGARET - Cornell University; SPRINGER, NATHAN - University Of Minnesota; THELEN, KURT - Michigan State University; WALLACE, JASON - University Of Georgia; WALLS, RAMONA - University Of Arizona; WALTON, RENEE - Iowa State University; WELDEKIDAN, TECLEMARIAM - University Of Delaware; WILLIS, DAVID - University Of Georgia; WISSER, RANDALL - State Of Delaware; SCHNABLE, PATRICK - Iowa State University

Submitted to: New Phytologist
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/10/2023
Publication Date: 8/25/2024
Citation: Kusmec, A., Yeh, C., Nettleton, D., Alkhalifah, N., Bohn, M.O., Buckler Iv, E.S., Campbell, D.A., Ciampitti, I.A., Ertl, D.S., Flint Garcia, S.A., Gardiner, J., Gore, M., Hirsch, C.N., Kaeppler, S.M., Knoll, J.E., Kolkman, J.M., Kruger, G.R., Lauter, N.C., Lawrence-Dill, C.J., Lee, E.C., De Leon, N., Liu, S., Lorence, A., Mcfarland, B.A., Poudya, C., Romay, M., Schnable, J.C., Sekhon, R.S., Silverstein, K.A., Smith, M.E., Springer, N.M., Thelen, K.D., Wallace, J.G., Walls, R.L., Walton, R.A., Weldekidan, T., Willis, D.M., Wisser, R.J., Schnable, P.S. 2024. Data-driven identification of environmental variables influencing phenotypic plasticity to facilitate breeding for future climates: a case study involving grain yield of hybrid maize. New Phytologist. Vol. 244, Issue 2, pp. 618-634. https://doi.org/10.1111/nph.19937.
DOI: https://doi.org/10.1111/nph.19937

Interpretive Summary: The ability of genotypes to produce varying types of phenotypes depending on differing environmental factors is called “phenotypic plasticity”. When measuring phenotypic plasticity, it’s important to identify the set of environmental factors impacting the final phenotype. Identifying environmental impacts is challenging, however, due to lack of data caused by the shifting effects of climate change. Further, environmental impacts are multi-faceted, making it difficult to determine all the environmental factors influencing final phenotypes. 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 tested the algorithm by applying it to a hybrid maize dataset. Through this study, we were able to demonstrate the utility of the algorithm for characterizing phenotypic plasticity. We also identified possible directions for future research into the biology of plastic responses.

Technical 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.