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

Authors: ROGERS, ANNA - North Carolina State University; DUNNE, JEFFREY - North Carolina State University; ROMAY, MARIA - Cornell University; BOHN, MARTIN - University Of Illinois; Buckler, Edward - Ed; CIAMPITTI, IGNACIO - Arkansas State University; Edwards, Jode; ERTL, DAVID - National Corn Growers Association; Flint-Garcia, Sherry; GORE, MICHAEL - Cornell University; GRAHAM, CHRISTOPHER - South Dakota State University; HIRSCH, CANDICE - University Of Minnesota; HOOD, ELIZABETH - Arkansas State University; HOOKER, DAVID - University Of Guelph; Knoll, Joseph - Joe; LEE, ELIZABETH - University Of Guelph; LORENZ, AARON - University Of Minnesota; LYNCH, JOHNATHAN - Pennsylvania State University; MCKAY, JOHN - Colorado State University; MOOSE, STEPHEN - University Of Illinois; MURRAY, SETH - Texas A&M University; NELSON, REBECCA - Cornell University; ROCHEFORD, TORBERT - Purdue University; SCHNABLE, JAMES - University Of Nebraska; SCHNABLE, PATRICK - Iowa State University; SEKHON, RAJANDEEP - Clemson University; SINGH, MANINDER - Michigan State University; SMITH, MARGARET - Cornell University; SPRINGER, NATHAN - University Of Nebraska; THELEN, KURT - Michigan State University; THOMISON, PETER - The Ohio State University; THOMPSON, ADDIE - Michigan State University; TUINSTRA, MITCH - Purdue University; WALLACE, JASON - University Of Georgia; WISSER, RANDALL - University Of Delaware; XU, WENWEI - Texas A&M University; GILMOUR, A.R. - New South Wales Agriculture; KAEPPLER, SHAWN - University Of Wisconsin; DELEON, NATALIA - University Of Wisconsin; Holland, Jim - Jim

Submitted to: Genes, Genomes, Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/7/2020
Publication Date: 1/4/2021
Citation: Rogers, A.R., Dunne, J.C., Romay, M.C., Bohn, M., Buckler IV, E.S., Ciampitti, I.C., Edwards, J.W., Ertl, D., Flint Garcia, S.A., Gore, M.A., Graham, C., Hirsch, C.N., Hood, E.C., Hooker, D., Knoll, J.E., Lee, E.C., Lorenz, A., Lynch, J.P., Mckay, J., Moose, S.P., Murray, S.C., Nelson, R., Rocheford, T., Schnable, J.C., Schnable, P.S., Sekhon, R., Singh, M., Smith, M., Springer, N., Thelen, K., Thomison, P., Thompson, A., Tuinstra, M., Wallace, J., Wisser, R., Xu, W., Gilmour, A., Kaeppler, S.M., Deleon, N., Holland, J.B. 2021. The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment. Genes, Genomes, Genetics. 11(2):jkaa050. https://doi.org/10.1093/g3journal/jkaa050.
DOI: https://doi.org/10.1093/g3journal/jkaa050

Interpretive Summary: Genotype-by-environment interactions arise when the relative trait values of families differ depending on the environment. Crop breeders typically attempt to target relatively homogeneous subsets of environments to reduce genotype-by-environment interactions. Understanding the relationships between specific environmental variables and polygenic breeding values may provide an alternative approach to targeting selection for optimal performance in different sets of environments. In this study, thousands of maize hybrids were genotyped and evaluated across 65 diverse environments to identify environmental factors contributing to similarity of yield performance and to provide a basis for environment-specific genomic prediction.

Technical Abstract: 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 1917 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.