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Enhancing Genome-Enabled Prediction by Bagging Genomic BLUP

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 numb...

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Autores principales: Gianola, Daniel, Weigel, Kent A., Krämer, Nicole, Stella, Alessandra, Schön, Chris-Carolin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3982963/
https://www.ncbi.nlm.nih.gov/pubmed/24722227
http://dx.doi.org/10.1371/journal.pone.0091693
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author Gianola, Daniel
Weigel, Kent A.
Krämer, Nicole
Stella, Alessandra
Schön, Chris-Carolin
author_facet Gianola, Daniel
Weigel, Kent A.
Krämer, Nicole
Stella, Alessandra
Schön, Chris-Carolin
author_sort Gianola, Daniel
collection PubMed
description 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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spelling pubmed-39829632014-04-15 Enhancing Genome-Enabled Prediction by Bagging Genomic BLUP Gianola, Daniel Weigel, Kent A. Krämer, Nicole Stella, Alessandra Schön, Chris-Carolin PLoS One Research Article 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. Public Library of Science 2014-04-10 /pmc/articles/PMC3982963/ /pubmed/24722227 http://dx.doi.org/10.1371/journal.pone.0091693 Text en © 2014 Gianola et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Gianola, Daniel
Weigel, Kent A.
Krämer, Nicole
Stella, Alessandra
Schön, Chris-Carolin
Enhancing Genome-Enabled Prediction by Bagging Genomic BLUP
title Enhancing Genome-Enabled Prediction by Bagging Genomic BLUP
title_full Enhancing Genome-Enabled Prediction by Bagging Genomic BLUP
title_fullStr Enhancing Genome-Enabled Prediction by Bagging Genomic BLUP
title_full_unstemmed Enhancing Genome-Enabled Prediction by Bagging Genomic BLUP
title_short Enhancing Genome-Enabled Prediction by Bagging Genomic BLUP
title_sort enhancing genome-enabled prediction by bagging genomic blup
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3982963/
https://www.ncbi.nlm.nih.gov/pubmed/24722227
http://dx.doi.org/10.1371/journal.pone.0091693
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