ArticleGenetics2025
Global genotype by environment prediction competition reveals that diverse modeling strategies can deliver satisfactory maize yield estimates.
Article in Genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Structured multimodal deep learning improves genomic prediction in future environments.Genetics · 2026Article
- Machine learning to predict genotypes and genotype-environment interaction associated with complex traits for genomic selection.Plant phenomics (Washington, D.C.) · 2026Article
- Genomic prediction in quinoa across contrasting environments using statistical and machine learning models.The plant genome · 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
- OptimGS: a dual integrative genomic prediction framework for improving cold stress tolerance in wheat.Briefings in bioinformatics · 2026Article
- Ensemble-based genomic prediction for maize flowering time improves prediction accuracy and reveals novel insights into trait genetic variation.G3 (Bethesda, Md.) · 2026Article
- A fast method for breeding by design via G × E interactions detected in large-scale climatic, phenomic and genomic data.National science review · 2026Article
- LSTM-attention-guided graph neural networks for integrated genotype-Environment modeling in maize yield prediction.PLoS computational biology · 2026Article
- Ensemble AnalySis with Interpretable Genomic Prediction (EasiGP): Computational tool for interpreting ensembles of genomic prediction models.The plant genome · 2025Article
- EasyGeSe - a resource for benchmarking genomic prediction methods.BMC genomics · 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
- 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
- Improved genomic prediction performance with ensembles of diverse models.G3 (Bethesda, Md.) · 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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59 authors.
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Abstract
Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of the planet, and create other positive outcomes. In 2022 and 2023, the first open-to-the-public Genomes to Fields initiative Genotype by Environment prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements, and field management notes gathered by the project over 9 years. The competition attracted registrants from around the world with representation from academic, government, industry, and nonprofit institutions as well as unaffiliated. These participants came from diverse disciplines, including plant science, animal science, breeding, statistics, computational biology, and others. Some participants had no formal genetics or plant-related training, and some were just beginning their graduate education. The teams applied varied methods and strategies, providing a wealth of modeling knowledge based on a common dataset. The winner's strategy involved 2 models combining machine learning and traditional breeding tools: 1 model emphasized environment using features extracted by random forest, ridge regression, and least squares, and 1 focused on genetics. Other high-performing teams' methods included quantitative genetics, machine learning/deep learning, mechanistic models, and model ensembles. The dataset factors used, such as genetics, weather, and management data, were also diverse, demonstrating that no single model or strategy is far superior to all others within the context of this competition.
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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.