ArticleFrontiers in plant science2021
Enviromic Assembly Increases Accuracy and Reduces Costs of the Genomic Prediction for Yield Plasticity in Maize.
Article in Frontiers in plant science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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31 citing papers in PubMed.
- Association mapping and genome prediction reveal a complex genetic architecture for resistance to black and frosty pod rot in cacao (Theobroma cacao L.) populations.The plant genome · 2026Article
- Yield-graph: multi-stage growth-aware maize yield prediction via graph neural networks.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Genomic prediction in quinoa across contrasting environments using statistical and machine learning models.The plant genome · 2026Article
- Multimodal genomic prediction is not a buzzword: why modern plant breeding must integrate genomics, enviromics, and phenomics.G3 (Bethesda, Md.) · 2026Article
- Leveraging Genetic Diversity for Abiotic Stress Tolerance.International journal of molecular sciences · 2026Article
- Omics Technologies in Molecular Biology.International journal of molecular sciences · 2026Article
- Impact of environmental covariates summarization on predictive ability in genomic selection.The plant genome · 2026Article
- Unveiling the Genomic Architecture of Phenotypic Plasticity Using Multiple GWAS Approaches Under Contrasting Conditions of Water Availability: A Model for Barley.International journal of molecular sciences · 2026Article
- Editorial: Utilizing machine learning with phenotypic and genotypic data to enhance effective breeding in agricultural and horticultural crops.Frontiers in plant science · 2026Article
- Enhancing enviromics based predictions in common bean multi-environment trials.Scientific reports · 2025Article
- Review
- 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
- 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
- Environmental data provide marginal benefit for predicting climate adaptation.PLoS genetics · 2025Article
- Prediction of biomass sorghum hybrids using environmental feature-enriched genomic combining ability models in tropical environments.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 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
- GIS-based G × E modeling of maize hybrids through enviromic markers engineering.The New phytologist · 2025Article
- Assessing the role of genotype by environment interaction of winter wheat cultivars using envirotyping techniques in North China.Frontiers in plant science · 2025Article
- Factor analytic selection tools and environmental feature-integration enable holistic decision-making in Eucalyptus breeding.Scientific reports · 2024Article
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3 authors.
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Abstract
Quantitative genetics states that phenotypic variation is a consequence of the interaction between genetic and environmental factors. Predictive breeding is based on this statement, and because of this, ways of modeling genetic effects are still evolving. At the same time, the same refinement must be used for processing environmental information. Here, we present an "enviromic assembly approach," which includes using ecophysiology knowledge in shaping environmental relatedness into whole-genome predictions (GP) for plant breeding (referred to as enviromic-aided genomic prediction, E-GP). We propose that the quality of an environment is defined by the core of environmental typologies and their frequencies, which describe different zones of plant adaptation. From this, we derived markers of environmental similarity cost-effectively. Combined with the traditional additive and non-additive effects, this approach may better represent the putative phenotypic variation observed across diverse growing conditions (i.e., phenotypic plasticity). Then, we designed optimized multi-environment trials coupling genetic algorithms, enviromic assembly, and genomic kinships capable of providing
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