Jarquin D, Natalia De Leon, Maria Cinta Romay, Martin O Bohn, Edward S Buckler, Ignacio Antonio Ciampitti, Jode Warren Edwards, David Erlt, Sherry Flint-Garcia, Michael A Gore, Christopher Graham, Candice Hirsch, James Holland, David Hooker, Shawn Kaeppler, Joseph Knoll, Elizabeth C Lee, Carolyn J Lawrence-Dill, Jonathan Lynch, Stephen Moose, Seth C Murray, Rebecca Nelson, Torbert Richard Rocheford, James C Schnable, Pat Schnable, Margaret Smith, Nathan M Springer, Peter Thomison, Mitch Tuinstra, Randall J Wisser, Wenwei Xu, Jianming Yu, Aaron J Lorenz


Front. Genet.

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Genomic prediction provides an efficient alternative to conventional phenotypic selection for developing improved cultivars with desirable characteristics. New and improved methods to genomic prediction are continually being developed that attempt to deal with the integration of data types beyond genomic information. Modern automated weather systems offer the opportunity to capture continuous data on a range of environmental parameters at specific field locations. In principle, this information could characterize training and target environments and enhance predictive ability by incorporating weather characteristics as part of the genotype-by-environment (G×E) interaction component in prediction models. We assessed the usefulness of including weather data variables in genomic prediction models using a naïve environmental kinship model across 30 environments comprising the Genomes to Fields (G2F) initiative in 2014 and 2015. Specifically four different prediction scenarios were evaluated (i) tested genotypes in observed environments; (ii) untested genotypes in observed environments; (iii) tested genotypes in unobserved environments; and (iv) untested genotypes in unobserved environments. A set of 1,481 unique hybrids were evaluated for grain yield. Evaluations were conducted using five different models including main effect of environments; general combining ability (GCA) effects of the maternal and paternal parents modeled using the genomic relationship matrix; specific combining ability (SCA) effects between maternal and paternal parents; interactions between genetic (GCA and SCA) effects and environmental effects; and finally interactions between the genetics effects and environmental covariates. Incorporation of the genotype-by-environment interaction term improved predictive ability across all scenarios. However, predictive ability was not improved through inclusion of naive environmental covariates in G×E models. More research should be conducted to link the observed weather conditions with important physiological aspects in plant development to improve predictive ability through the inclusion of weather data.