ArticleG3 (Bethesda, Md.)2022
Environment-specific genomic prediction ability in maize using environmental covariates depends on environmental similarity to training data.
Article in G3 (Bethesda, Md.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- A haplotype-based approach to explore genotype-by-environment interactions for barley yield.The plant genome · 2026Article
- Scalable medium-density genotyping platforms for cultivar identification, pedigree authentication, marker-assisted and genomic selection, and other applications in strawberry.G3 (Bethesda, Md.) · 2026Article
- Tailoring AI and ML models for genotype-by-environment prediction leveraging environmental covariates: A European rye example.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
- Variability in crop responses as a function of environment affects the NDVI relationship with grain yield in wheat.Plant phenomics (Washington, D.C.) · 2026Article
- Impact of environmental covariates summarization on predictive ability in genomic selection.The plant genome · 2026Article
- The next frontier for genomic prediction is computational.G3 (Bethesda, Md.) · 2026Article
- Variation in responses to temperature in admixed Populus genotypes predicts geographic shifts in regions where hybrids are favored.The New phytologist · 2026Article
- Review
- Genomic prediction for grain yield and biotic stress resistance in field pea (Frontiers in plant science · 2026Article
- Enhancing enviromics based predictions in common bean multi-environment trials.Scientific reports · 2025Article
- Genomic prediction of agronomic traits in perennial ryegrass (Lolium perenne L.) and genotype x environment interactions at the limit of the species distribution.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Prediction of Australian wheat genotype by environment interactions and mega-environments.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Harnessing big data for enhanced genome-wide prediction in winter wheat breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Integration of crop modeling and sensing into molecular breeding for nutritional quality and stress tolerance.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Toward a general framework for AI-enabled prediction in crop improvement.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 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
- GIS-based G × E modeling of maize hybrids through enviromic markers engineering.The New phytologist · 2025Article
Corrections and comments
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Technology advances have made possible the collection of a wealth of genomic, environmental, and phenotypic data for use in plant breeding. Incorporation of environmental data into environment-specific genomic prediction is hindered in part because of inherently high data dimensionality. Computationally efficient approaches to combining genomic and environmental information may facilitate extension of genomic prediction models to new environments and germplasm, and better understanding of genotype-by-environment (G × E) interactions. Using genomic, yield trial, and environmental data on 1,918 unique hybrids evaluated in 59 environments from the maize Genomes to Fields project, we determined that a set of 10,153 SNP dominance coefficients and a 5-day temporal window size for summarizing environmental variables were optimal for genomic prediction using only genetic and environmental main effects. Adding marker-by-environment variable interactions required dimension reduction, and we found that reducing dimensionality of the genetic data while keeping the full set of environmental covariates was best for environment-specific genomic prediction of grain yield, leading to an increase in prediction ability of 2.7% to achieve a prediction ability of 80% across environments when data were masked at random. We then measured how prediction ability within environments was affected under stratified training-testing sets to approximate scenarios commonly encountered by plant breeders, finding that incorporation of marker-by-environment effects improved prediction ability in cases where training and test sets shared environments, but did not improve prediction in new untested environments. The environmental similarity between training and testing sets had a greater impact on the efficacy of prediction than genetic similarity between training and test sets.
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