ArticlePloS one2014
Enhancing genome-enabled prediction by bagging genomic BLUP.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Evolutionary structure constrains genomic prediction accuracy more than model complexity in mango (Mangifera indica L.).G3 (Bethesda, Md.) · 2026Article
- Feed-forward neural networks for genomic selection: insights into varying levels of dominance heritability.BMC genomics · 2026Article
- Performance of multi-trait and single-trait genomic selection for grain Fe and Zn concentrations in sorghum under different breeding constraints.Scientific reports · 2025Article
- Ensemble learning for integrative prediction of genetic values with genomic variants.BMC bioinformatics · 2024Article
- Modeling genotype × environment interaction for single and multitrait genomic prediction in potato (Solanum tuberosum L.).G3 (Bethesda, Md.) · 2023Article
- A divide-and-conquer approach for genomic prediction in rubber tree using machine learning.Scientific reports · 2022Article
- Performance of Bayesian and BLUP alphabets for genomic prediction: analysis, comparison and results.Heredity · 2022Article
- Genome-Based Genotype × Environment Prediction Enhances Potato (Frontiers in plant science · 2022Article
- Response to Early Generation Genomic Selection for Yield in Wheat.Frontiers in plant science · 2021Article
- Approximate Genome-Based Kernel Models for Large Data Sets Including Main Effects and Interactions.Frontiers in genetics · 2020Article
- Genomic Prediction of Grain Yield and Drought-Adaptation Capacity in Sorghum Is Enhanced by Multi-Trait Analysis.Frontiers in plant science · 2019Article
- Genomic Selection in Rubber Tree Breeding: A Comparison of Models and Methods for Managing G×E Interactions.Frontiers in plant science · 2019Article
- Applications of Machine Learning Methods to Genomic Selection in Breeding Wheat for Rust Resistance.The plant genome · 2018Article
- Application of Entropy Ensemble Filter in Neural Network Forecasts of Tropical Pacific Sea Surface Temperatures.Entropy (Basel, Switzerland) · 2018Article
- Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction.G3 (Bethesda, Md.) · 2017Article
- Genomic prediction using subsampling.BMC bioinformatics · 2017Article
- Cross-Validation Without Doing Cross-Validation in Genome-Enabled Prediction.G3 (Bethesda, Md.) · 2016Article
- Genomic selection to resistance to Stenocarpella maydis in maize lines using DArTseq markers.BMC genetics · 2016Article
- Multivariate Analysis of Genotype-Phenotype Association.Genetics · 2016Article
- On the Accuracy of Genomic Selection.PloS one · 2016Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We examined whether or not the predictive ability of genomic best linear unbiased prediction (GBLUP) could be improved via a resampling method used in machine learning: bootstrap aggregating sampling ("bagging"). In theory, bagging can be useful when the predictor has large variance or when the number of markers is much larger than sample size, preventing effective regularization. After presenting a brief review of GBLUP, bagging was adapted to the context of GBLUP, both at the level of the genetic signal and of marker effects. The performance of bagging was evaluated with four simulated case studies including known or unknown quantitative trait loci, and an application was made to real data on grain yield in wheat planted in four environments. A metric aimed to quantify candidate-specific cross-validation uncertainty was proposed and assessed; as expected, model derived theoretical reliabilities bore no relationship with cross-validation accuracy. It was found that bagging can ameliorate predictive performance of GBLUP and make it more robust against over-fitting. Seemingly, 25-50 bootstrap samples was enough to attain reasonable predictions as well as stable measures of individual predictive mean squared errors.
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