Leveraging Data from the Genomes to Fields Initiative to Investigate G×E in Maize in North America

Abstract

Genotype-by-environment (GxE) interactions play a significant role in crop performance and stability. Investigating GxE requires extensive data where diverse genotypes are tested over multiple locations and years. Since 2014, the Genomes to Fields (G2F) initiative has collected phenotype and genotype data for more than 4,000 diverse hybrids tested in more than 130 year-locations combinations in the US and Canada. We curated this data set and expanded it by generating (using a crop model) environmental covariates for each of the trials conducted by the G2F initiative since 2014. The resulting data set includes DNA genotypes and environmental data linked to more than 70,000 phenotypic records of grain yield and flowering traits in North America. We used multivariate analyses to characterize the data set’s genetic and environmental structure, study the association of key environmental factors with grain yield and flowering traits, and provide benchmarks using state-of-the-art genomic prediction models. The workflows used to generate and analyze the data set are provided as open-source code. The data set that we introduce in this study can serve as a benchmark in agricultural modeling and prediction, and paves the way for countless GxE investigations in maize.