Estimating parametric phenotypes that determine anthesis date in Zea mays: Challenges in combining ecophysiological models with genetics

Authors: LAMSAL, ABHISHES - Kansas State University; WELCH, STEPHEN - Kansas State University; White, Jeffrey; Thorp, Kelly; BELLO, NORA - Kansas State University

Submitted to: PLOS ONE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/12/2018
Publication Date: 4/19/2018
Citation: Lamsal, A., Welch, S.M., White, J.W., Thorp, K.R., Bello, N. 2018. Estimating parametric phenotypes that determine anthesis date in Zea mays: Challenges in combining ecophysiological models with genetics. PLoS One. 13(4):e0195841. https://doi.org/10.1371/journal.pone.0195841.
DOI: https://doi.org/10.1371/journal.pone.0195841

Interpretive Summary: Computer models of crops can describe how different cultivars respond to environmental conditions and crop management and thus are used in applications ranging from basic physiology to predicting in-season requirements for irrigation and fertilizers. To represent cultivar differences, the models use different values of parameters for traits such as response of flowering time to temperature or final grain size. Accurate estimation of these parameters is crucial, especially as researchers try to link these parameters to specific genes. The original goal of this study was to estimate model parameters affecting flowering time for a dataset of 5266 maize lines grown at 11 site-years and then use those results to understand the genetic control of flowering. The crop model was the widely used CERES-Maize. Despite the high predictive quality of the flowering data obtained, numerous artifacts emerged during the estimations of parameter values. Two categories of problems were noted. The first was where the model was unable to express the observed data for many lines, which thus ended up sharing the same parameter value when the known measured flowering times differed. In the second (2254 lines), the model reproduced the data, but there were often many parameter sets that did so equally well (termed “equifinality”). These artifacts made our original goal of mapping completely unachievable but revealed important problems inherent in using crop models in genetic studies. They thus provide guidance to researchers on how best to use computer models in genetic studies.

Technical Abstract: Ecophysiological crop models encode intra-species behaviors using parameters that are presumed to summarize genotypic properties of individual lines or cultivars. These genotype-specific parameters (GSP’s) can be interpreted as quantitative traits that can be mapped or otherwise analyzed, as are more conventional traits. The goal of this study was to investigate the estimation of parameters controlling maize anthesis date with the CERES-Maize model, based on 5,266 maize lines from 11 plantings at locations across the eastern United States. High performance computing was used to develop a database of 356 million simulated anthesis dates in response to four CERES-Maize model parameters. Although the resulting estimates showed high predictive value (R2 = 0.94), three issues presented serious challenges for use of GSP’s as traits. First (expressivity), the model was unable to express the observed data for 168 to 3,339 lines (depending on the combination of site-years), many of which ended up sharing the same parameter value irrespective of genetics. Second, for 2,254 lines, the model reproduced the data, but multiple parameter sets were equally effective (equifinality). Third, parameter values were highly dependent (p<.0001) on the sets of environments used to estimate them (instability), calling in to question the assumption that they represent fundamental genetic traits. The issues of expressivity, equifinality and instability must be addressed before the genetic mapping of GSP’s becomes a robust means to help solve the genotype-to-phenotype problem in crops.