ArticleG3 (Bethesda, Md.)2021
The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment.
Article in G3 (Bethesda, Md.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.
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Who cites it
41 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Optimizing Genomic-Enabled Prediction in Small-Scale Maize Hybrid Breeding Programs: A Roadmap Review.Frontiers in plant science · 2021Pooled it
- Genomic prediction of single cross families of perennial ryegrass under two nitrogen managements.The plant genome · 2026Article
- Dissecting genetic variance structure and evaluating genomic prediction models for single-cross hybrids derived from Stiff Stalk and Non-Stiff Stalk maize heterotic groups.G3 (Bethesda, Md.) · 2026Article
- MeNet: A mixed-effect deep neural network for multi-environment genomic prediction of agronomic traits.Plant communications · 2026Article
- 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
- 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
- A reaction norm for flowering time plasticity reveals physiological footprints of maize adaptation.G3 (Bethesda, Md.) · 2025Article
- Improvement of polygenic modeling of blood pressure traits using lifestyle information in the UK Biobank.Genetics · 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
- Article
- MegaLMM improves genomic predictions in new environments using environmental covariates.Genetics · 2025Article
- Leveraging window-pane analysis with environmental factor loadings of genotype-by-environment interaction to identify high-resolution weather-based variables associated with plant disease.Frontiers in plant science · 2025Article
- Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates.bioRxiv : the preprint server for biology · 2024Article
- Genetic analysis of yield components in buckwheat using high-throughput sequencing analysis and wild resource populations.Physiology and molecular biology of plants : an international journal of functional plant biology · 2024Article
- Enhancing winter wheat prediction with genomics, phenomics and environmental data.BMC genomics · 2024Article
- Utilizing genomic prediction to boost hybrid performance in a sweet corn breeding program.Frontiers in plant science · 2024Article
- Feature engineering of environmental covariates improves plant genomic-enabled prediction.Frontiers in plant science · 2024Article
- Leveraging data from the Genomes-to-Fields Initiative to investigate genotype-by-environment interactions in maize in North America.Nature communications · 2023Article
- Identifying QTLs involved in hybrid performance and heterotic group complementarity: new GWAS models applied to factorial and admixed diallel maize hybrid panels.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Yield prediction through integration of genetic, environment, and management data through deep learning.G3 (Bethesda, Md.) · 2023Article
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40 authors.
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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 1918 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.
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