Skip to main content
ARS Home » Northeast Area » Ithaca, New York » Robert W. Holley Center for Agriculture & Health » Plant, Soil and Nutrition Research » Research » Publications at this Location » Publication #386694

Research Project: Improving Crop Efficiency Using Genomic Diversity and Computational Modeling

Location: Plant, Soil and Nutrition Research

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

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

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.