ArticleG3 (Bethesda, Md.)2017
Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction.
Article in G3 (Bethesda, Md.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 1 of them a synthesis that pooled it.
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Who cites it
55 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
- Improving hybrid breeding efficiency in winter oilseed rape under sparse multi-environment testing.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- When does accounting for gene-environment interactions improve complex trait prediction? A case study with Drosophila lifespan.G3 (Bethesda, Md.) · 2026Article
- Genomic prediction models for morpho-phenological traits in durum wheat based on Vrn, Ppd, and Rht alleles.The plant genome · 2025Article
- Genomic prediction of stalk lodging resistance and the associated intermediate phenotypes in maize using whole-genome resequence and multi-environmental data.The plant genome · 2025Article
- When does accounting for gene-environment interactions improve complex trait prediction? A case study withbioRxiv : the preprint server for biology · 2025Article
- Cross-species analysis of genetic architecture and polygenic risk scores for non-contact ACL rupture in dogs and humans.Communications biology · 2025Article
- GIS-based G × E modeling of maize hybrids through enviromic markers engineering.The New phytologist · 2025Article
- Incorporating gene expression and environment for genomic prediction in wheat.Frontiers in plant science · 2025Article
- Genomic dissection of additive and non-additive genetic effects and genomic prediction in an open-pollinated family test of Japanese larch.BMC genomics · 2024Article
- Utilizing genomic prediction to boost hybrid performance in a sweet corn breeding program.Frontiers in plant science · 2024Article
- Including marker x environment interactions improves genomic prediction in red clover (Frontiers in plant science · 2024Article
- Partial least squares enhance multi-trait genomic prediction of potato cultivars in new environments.Scientific reports · 2023Article
- Efficacy of plant breeding using genomic information.The plant genome · 2023Article
- Modeling genotype × environment interaction for single and multitrait genomic prediction in potato (Solanum tuberosum L.).G3 (Bethesda, Md.) · 2023Article
- Envirome-wide associations enhance multi-year genome-based prediction of historical wheat breeding data.G3 (Bethesda, Md.) · 2023Article
- Enviromic-based kernels may optimize resource allocation with multi-trait multi-environment genomic prediction for tropical Maize.BMC plant biology · 2023Article
- Multi-environment analysis enhances genomic prediction accuracy of agronomic traits in sesame.Frontiers in genetics · 2023Article
- Phenomic selection in slash pine multi-temporally using UAV-multispectral imagery.Frontiers in plant science · 2023Article
- Genome-wide association study as a powerful tool for dissecting competitive traits in legumes.Frontiers in plant science · 2023Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: (1) single-environment, main genotypic effect model (SM); (2) multi-environment, main genotypic effects model (MM); (3) multi-environment, single variance G×E deviation model (MDs); and (4) multi-environment, environment-specific variance G×E deviation model (MDe). Each of these four models were fitted using two kernel methods: a linear kernel Genomic Best Linear Unbiased Predictor, GBLUP (GB), and a nonlinear kernel Gaussian kernel (GK). The eight model-method combinations were applied to two extensive Brazilian maize data sets (HEL and USP data sets), having different numbers of maize hybrids evaluated in different environments for grain yield (GY), plant height (PH), and ear height (EH). Results show that the MDe and the MDs models fitted with the Gaussian kernel (MDe-GK, and MDs-GK) had the highest prediction accuracy. For GY in the HEL data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 9 to 32%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 9 to 49%. For GY in the USP data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 0 to 7%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 34 to 70%. For traits PH and EH, gains in prediction accuracy of models with GK compared to models with GB were smaller than those achieved in GY. Also, these gains in prediction accuracy decreased when a more difficult prediction problem was studied.
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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.