The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment

Anna R. Rogers, Jeffrey C. Dunne, Cinta Romay, Martin Bohn, Edward S. Buckler, Ignacio A. Ciampitti, Jode Edwards, David Ertl, Sherry Flint-Garcia, Michael A. Gore, Christopher Graham, Candice N. Hirsch, Elizabeth Hood, David C. Hooker, Joseph Knoll, Elizabeth C. Lee, Aaron Lorenz, Jonathan P. Lynch, John McKay, Stephen P. MooseSeth C. Murray, Rebecca Nelson, Torbert Rocheford, James C. Schnable, Patrick S. Schnable, Rajandeep Sekhon, Maninder Singh, Margaret Smith, Nathan Springer, Kurt Thelen, Peter Thomison, Addie Thompson, Mitch Tuinstra, Jason Wallace, Randall J. Wisser, Wenwei Xu, A. R. Gilmour, Shawn M. Kaeppler, Natalia De Leon, James B. Holland

Research output: Contribution to journalArticlepeer-review

42 Scopus citations


High-dimensional and high-throughput genomic, field performance, and environmental data are becoming increasingly available to crop breeding programs, and their integration can facilitate genomic prediction within and across environments and provide insights into the genetic architecture of complex traits and the nature of genotype-by-environment interactions. To partition trait variation into additive and dominance (main effect) genetic and corresponding genetic-by-environment variances, and to identify specific environmental factors that influence genotype-by-environment interactions, we curated and analyzed genotypic and phenotypic data on 1918 maize (Zea mays L.) hybrids and environmental data from 65 testing environments. For grain yield, dominance variance was similar in magnitude to additive variance, and genetic-by-environment variances were more important than genetic main effect variances. Models involving both additive and dominance relationships best fit the data and modeling unique genetic covariances among all environments provided the best characterization of the genotype-by-environment interaction patterns. Similarity of relative hybrid performance among environments was modeled as a function of underlying weather variables, permitting identification of weather covariates driving correlations of genetic effects across environments. The resulting models can be used for genomic prediction of mean hybrid performance across populations of environments tested or for environment-specific predictions. These results can also guide efforts to incorporate high-throughput environmental data into genomic prediction models and predict values in new environments characterized with the same environmental characteristics.

Original languageEnglish (US)
Article numberjkaa050
JournalG3: Genes, Genomes, Genetics
Issue number2
StatePublished - Feb 2021

All Science Journal Classification (ASJC) codes

  • Molecular Biology
  • Genetics
  • Genetics(clinical)


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