ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2014
Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions.
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 167 papers, 1 of them a synthesis that pooled it.
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Who cites it
167 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
- Structured multimodal deep learning improves genomic prediction in future environments.Genetics · 2026Article
- Yield-graph: multi-stage growth-aware maize yield prediction via graph neural networks.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Multimodality, interaction modeling, and multimodule architectures in genomic prediction: A unified conceptual framework.The plant genome · 2026Article
- Sparse phenotyping for wheat grain yield enabled by multiomics prediction.The plant genome · 2026Article
- Article
- 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
- Multimodal genomic prediction is not a buzzword: why modern plant breeding must integrate genomics, enviromics, and phenomics.G3 (Bethesda, Md.) · 2026Article
- Phenomics-derived temporal maize health and environmental index enhance physiology-informed genomic prediction of yield across environments.Plant physiology · 2026Article
- Improving genomic prediction in wheat with random regression models with genotype-specific phenology-driven environmental covariates.The plant genome · 2026Article
- Optimizing training sets for genomic selection to identify superior genotypes across multiple environments.G3 (Bethesda, Md.) · 2026Article
- Predictions of wheat phenotypic variability by integrating high-throughput phenotyping observations into a crop growth model.Plant phenomics (Washington, D.C.) · 2026Article
- When does accounting for gene-environment interactions improve complex trait prediction? A case study with Drosophila lifespan.G3 (Bethesda, Md.) · 2026Article
- Predicting enviromically adapted varieties with big data.Genome biology · 2026Article
- A novel matrix multiplication framework for modeling genotype-by-environment interaction in genomic prediction.Frontiers in genetics · 2026Article
- Optimizing genomic selection models for wheat breeding under contrasting water regimes in a mediterranean environment.Plant methods · 2025Article
- Harnessing genotype-by-environment interaction and stability analysis of taro (Colocasia esculenta (L.) Schott) genotypes for resilient breeding and food security in Southwest Ethiopia.BMC plant biology · 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
- Comparing artificial and convolutional neural networks with traditional models for Genomic prediction in wheat.Molecular breeding : new strategies in plant improvement · 2025Article
107 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
key messageDevelopment of models to predict genotype by environment interactions, in unobserved environments, using environmental covariates, a crop model and genomic selection. Application to a large winter wheat dataset. Genotype by environment interaction (G*E) is one of the key issues when analyzing phenotypes. The use of environment data to model G*E has long been a subject of interest but is limited by the same problems as those addressed by genomic selection methods: a large number of correlated predictors each explaining a small amount of the total variance. In addition, non-linear responses of genotypes to stresses are expected to further complicate the analysis. Using a crop model to derive stress covariates from daily weather data for predicted crop development stages, we propose an extension of the factorial regression model to genomic selection. This model is further extended to the marker level, enabling the modeling of quantitative trait loci (QTL) by environment interaction (Q*E), on a genome-wide scale. A newly developed ensemble method, soft rule fit, was used to improve this model and capture non-linear responses of QTL to stresses. The method is tested using a large winter wheat dataset, representative of the type of data available in a large-scale commercial breeding program. Accuracy in predicting genotype performance in unobserved environments for which weather data were available increased by 11.1% on average and the variability in prediction accuracy decreased by 10.8%. By leveraging agronomic knowledge and the large historical datasets generated by breeding programs, this new model provides insight into the genetic architecture of genotype by environment interactions and could predict genotype performance based on past and future weather scenarios.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.