ArticleNature communications2023
Leveraging data from the Genomes-to-Fields Initiative to investigate genotype-by-environment interactions in maize in North America.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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24 citing papers in PubMed.
- Predictive models of the genetic bases underlying budding yeast fitness in multiple environments.NAR genomics and bioinformatics · 2026Article
- From QTL to candidate genes: a data-driven approach to unravel the genetic architecture of yellow rust resistance in central European wheat.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- GE-BiFormer: bidirectional cross-attention integration of genomic and Enviromic data for genotype-by-environment prediction in maize.Briefings in bioinformatics · 2026Article
- Omics-driven plant breeding through phenomics-enviromics crosstalk.Nature communications · 2026Review
- Critical evaluation of the theory and practice of feed-forward neural networks for genomic prediction.G3 (Bethesda, Md.) · 2026Article
- Impact of environmental covariates summarization on predictive ability in genomic selection.The plant genome · 2026Article
- Sharing approaches in predictive genomics across animals, plants and humans.Nature genetics · 2026Review
- When does accounting for gene-environment interactions improve complex trait prediction? A case study with Drosophila lifespan.G3 (Bethesda, Md.) · 2026Article
- Crop wild relatives of legumes: evolutionary resources for climate-responsive pre-breeding.Frontiers in plant science · 2026Review
- Predictive models of the genetic bases underlying budding yeast fitness in multiple environments.bioRxiv : the preprint server for biology · 2025Article
- Order from entropy: big data from FAIR data cohorts in the digital age of plant breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Improving genomic prediction for plant disease using environmental covariates.Plant methods · 2025Article
- Breeding perspectives on tackling trait genome-to-phenome (G2P) dimensionality using ensemble-based genomic prediction.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- EXGEP: a framework for predicting genotype-by-environment interactions using ensembles of explainable machine-learning models.Briefings in bioinformatics · 2025Article
- Unravelling root system architecture plasticity in response to abiotic stresses in maize.Scientific reports · 2025Article
- Multi-trait/environment sparse genomic prediction using the SFSI R-package.The plant genome · 2025Article
- Penalized factorial regression as a flexible and computationally attractive reaction norm model for prediction in the presence of GxE.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Article
- MegaLMM improves genomic predictions in new environments using environmental covariates.Genetics · 2025Article
- Analyses of crop yield dynamics and the development of a multimodal neural network prediction model with G×E×M interactions.Frontiers in plant science · 2025Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genotype-by-environment (G×E) interactions can significantly affect crop performance and stability. Investigating G×E requires extensive data sets with diverse cultivars tested over multiple locations and years. The Genomes-to-Fields (G2F) Initiative has tested maize hybrids in more than 130 year-locations in North America since 2014. Here, we curate and expand this data set by generating environmental covariates (using a crop model) for each of the trials. The resulting data set includes DNA genotypes and environmental data linked to more than 70,000 phenotypic records of grain yield and flowering traits for more than 4000 hybrids. We show how this valuable data set can serve as a benchmark in agricultural modeling and prediction, paving the way for countless G×E investigations in maize. We use multivariate analyses to characterize the data set's genetic and environmental structure, study the association of key environmental factors with traits, and provide benchmarks using genomic prediction models.
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