ArticleG3 (Bethesda, Md.)2018
BGGE: A New Package for Genomic-Enabled Prediction Incorporating Genotype × Environment Interaction Models.
Article in G3 (Bethesda, Md.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
32 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
- Critical evaluation of the theory and practice of feed-forward neural networks for genomic prediction.G3 (Bethesda, Md.) · 2026Article
- Enhancing genomic prediction ability of blast resistance using genome-wide association study-derived marker weights in two rice (Oryza sativa L.) populations.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Article
- Integrative multi-environmental genomic prediction in apple.Horticulture research · 2025Article
- Evaluating and Predicting the Performance of Sorghum Lines in an Elite by Exotic Backcross-Nested Association Mapping Population.Plants (Basel, Switzerland) · 2024Article
- Genomic selection for tolerance to aluminum toxicity in a synthetic population of upland rice.PloS one · 2024Article
- Performance of phenomic selection in rice: Effects of population size and genotype-environment interactions on predictive ability.PloS one · 2024Article
- Optimization of Multi-Generation Multi-location Genomic Prediction Models for Recurrent Genomic Selection in an Upland Rice Population.Rice (New York, N.Y.) · 2023Article
- Genomic prediction for complex traits across multiples harvests in alfalfa (Medicago sativa L.) is enhanced by enviromics.The plant genome · 2023Article
- Prediction of near-term climate change impacts on UK wheat quality and the potential for adaptation through plant breeding.Global change biology · 2023Article
- Envirome-wide associations enhance multi-year genome-based prediction of historical wheat breeding data.G3 (Bethesda, Md.) · 2023Article
- Multi-trait and multi-environment genomic prediction for flowering traits in maize: a deep learning approach.Frontiers in plant science · 2023Article
- Comparison of linear and semi-parametric models incorporating genomic, pedigree, and associated loci information for the prediction of resistance to stripe rust in an Austrian winter wheat breeding program.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Phenomic selection in slash pine multi-temporally using UAV-multispectral imagery.Frontiers in plant science · 2023Article
- Genome-wide family prediction unveils molecular mechanisms underlying the regulation of agronomic traits inFrontiers in plant science · 2023Article
- learnMET: an R package to apply machine learning methods for genomic prediction using multi-environment trial data.G3 (Bethesda, Md.) · 2022Article
- A divide-and-conquer approach for genomic prediction in rubber tree using machine learning.Scientific reports · 2022Article
- Environment-specific genomic prediction ability in maize using environmental covariates depends on environmental similarity to training data.G3 (Bethesda, Md.) · 2022Article
- Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction.Methods in molecular biology (Clifton, N.J.) · 2022Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One of the major issues in plant breeding is the occurrence of genotype × environment (GE) interaction. Several models have been created to understand this phenomenon and explore it. In the genomic era, several models were employed to improve selection by using markers and account for GE interaction simultaneously. Some of these models use special genetic covariance matrices. In addition, the scale of multi-environment trials is getting larger, and this increases the computational challenges. In this context, we propose an R package that, in general, allows building GE genomic covariance matrices and fitting linear mixed models, in particular, to a few genomic GE models. Here we propose two functions: one to prepare the genomic kernels accounting for the genomic GE and another to perform genomic prediction using a Bayesian linear mixed model. A specific treatment is given for sparse covariance matrices, in particular, to block diagonal matrices that are present in some GE models in order to decrease the computational demand. In empirical comparisons with Bayesian Genomic Linear Regression (BGLR), accuracies and the mean squared error were similar; however, the computational time was up to five times lower than when using the classic approach. Bayesian Genomic Genotype × Environment Interaction (BGGE) is a fast, efficient option for creating genomic GE kernels and making genomic predictions.
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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.