ArticleHeredity2021
Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials.
Article in Heredity, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 1 of them a synthesis that pooled it.
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Who cites it
72 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
- Machine learning to predict genotypes and genotype-environment interaction associated with complex traits for genomic selection.Plant phenomics (Washington, D.C.) · 2026Article
- Innovations in hybrid rice seed production: Integrating agronomy, mechanization, and biotechnology.Plant communications · 2026Review
- GE-BiFormer: bidirectional cross-attention integration of genomic and Enviromic data for genotype-by-environment prediction in maize.Briefings in bioinformatics · 2026Article
- Omics-driven plant breeding through phenomics-enviromics crosstalk.Nature communications · 2026Review
- Improving genomic prediction in wheat with random regression models with genotype-specific phenology-driven environmental covariates.The plant genome · 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
- Multimodal deep learning improves cross-environment prediction of durum wheat yield components.BMC plant biology · 2026Article
- Critical evaluation of the theory and practice of feed-forward neural networks for genomic prediction.G3 (Bethesda, Md.) · 2026Article
- Impact of environmental covariates summarization on predictive ability in genomic selection.The plant genome · 2026Article
- Genomic prediction of feed efficiency in boars by deep learning.G3 (Bethesda, Md.) · 2026Article
- Editorial: Utilizing machine learning with phenotypic and genotypic data to enhance effective breeding in agricultural and horticultural crops.Frontiers in plant science · 2026Article
- A novel matrix multiplication framework for modeling genotype-by-environment interaction in genomic prediction.Frontiers in genetics · 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
- Evaluating effect of different dominance genotype encodings on genome-wide association studies and genomic selection.Animal bioscience · 2025Article
- Improving genomic prediction for plant disease using environmental covariates.Plant methods · 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
- Leveraging Automated Machine Learning for Environmental Data-Driven Genetic Analysis and Genomic Prediction in Maize Hybrids.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
12 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Modern whole-genome prediction (WGP) frameworks that focus on multi-environment trials (MET) integrate large-scale genomics, phenomics, and envirotyping data. However, the more complex the statistical model, the longer the computational processing times, which do not always result in accuracy gains. We investigated the use of new kernel methods and modeling structures involving genomics and nongenomic sources of variation in two MET maize data sets. Five WGP models were considered, advancing in complexity from a main-effect additive model (A) to more complex structures, including dominance deviations (D), genotype × environment interaction (AE and DE), and the reaction-norm model using environmental covariables (W) and their interaction with A and D (AW + DW). A combination of those models built with three different kernel methods, Gaussian kernel (GK), Deep kernel (DK), and the benchmark genomic best linear-unbiased predictor (GBLUP/GB), was tested under three prediction scenarios: newly developed hybrids (CV1), sparse MET conditions (CV2), and new environments (CV0). GK and DK outperformed GB in prediction accuracy and reduction of computation time (~up to 20%) under all model-kernel scenarios. GK was more efficient in capturing the variation due to A + AE and D + DE effects and translated it into accuracy gains (~up to 85% compared with GB). DK provided more consistent predictions, even for more complex structures such as W + AW + DW. Our results suggest that DK and GK are more efficient in translating model complexity into accuracy, and more suitable for including dominance and reaction-norm effects in a biologically accurate and faster way.
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Registered trials
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.