Genomic prediction using the lmekin function from the coxme R package

Detalhes bibliográficos
Autor(a) principal: Souza, Clemeson Silva de
Data de Publicação: 2023
Outros Autores: Santos, Vinícius Silva dos, Martins Filho, Sebastião
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Acta Scientiarum. Agronomy (Online)
Texto Completo: http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/64243
Resumo: The increasing use of genomic selection (GS) in plant and animal breeding programs has led to the development of software that fits models based on unique scenarios. Accordingly, several R packages have been developed for GS. The lmekin function from the coxme R package was one of the first functions implemented in R to fit models with random family effects using the pedigree–based relationship matrix. The function allows the user to provide the covariance structures for the random effects; thus, the GBLUP model can be fitted. This fitting process consists of replacing, in the traditional BLUP model, the additive relationship matrix derived from a pedigree by the additive relationship matrix derived from markers. Thus, the objective of this study was to employ the lmekin function in the context of genomic prediction by comparing the results of this function with those obtained using five R packages for GS: rrBLUP, BGLR, sommer, lme4qtl, and lme4GS. The comparisons were performed considering the computational times and predicted values for a wheat dataset and simulated big data. In addition, we implemented a 5-fold cross-validation scheme through considering the values predicted by the lmekin function for the wheat dataset. The results indicated that the lmekin function was effective in predicting genomic breeding values considering multiple random effects and relatively small sample sizes. The rrBLUP package processed the fastest for the scenario with only one genetic random effect, and the high temporal efficiency of the sommer package was confirmed for the scenario with more than one genetic random effect. Differences in computational times occurred because of the different algorithms implemented in the packages to estimate the variance components.
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spelling Genomic prediction using the lmekin function from the coxme R packageGenomic prediction using the lmekin function from the coxme R packagemixed models; GBLUP; genomic relationship matrix; pedigree; genetic breeding.mixed models; GBLUP; genomic relationship matrix; pedigree; genetic breeding.The increasing use of genomic selection (GS) in plant and animal breeding programs has led to the development of software that fits models based on unique scenarios. Accordingly, several R packages have been developed for GS. The lmekin function from the coxme R package was one of the first functions implemented in R to fit models with random family effects using the pedigree–based relationship matrix. The function allows the user to provide the covariance structures for the random effects; thus, the GBLUP model can be fitted. This fitting process consists of replacing, in the traditional BLUP model, the additive relationship matrix derived from a pedigree by the additive relationship matrix derived from markers. Thus, the objective of this study was to employ the lmekin function in the context of genomic prediction by comparing the results of this function with those obtained using five R packages for GS: rrBLUP, BGLR, sommer, lme4qtl, and lme4GS. The comparisons were performed considering the computational times and predicted values for a wheat dataset and simulated big data. In addition, we implemented a 5-fold cross-validation scheme through considering the values predicted by the lmekin function for the wheat dataset. The results indicated that the lmekin function was effective in predicting genomic breeding values considering multiple random effects and relatively small sample sizes. The rrBLUP package processed the fastest for the scenario with only one genetic random effect, and the high temporal efficiency of the sommer package was confirmed for the scenario with more than one genetic random effect. Differences in computational times occurred because of the different algorithms implemented in the packages to estimate the variance components.The increasing use of genomic selection (GS) in plant and animal breeding programs has led to the development of software that fits models based on unique scenarios. Accordingly, several R packages have been developed for GS. The lmekin function from the coxme R package was one of the first functions implemented in R to fit models with random family effects using the pedigree–based relationship matrix. The function allows the user to provide the covariance structures for the random effects; thus, the GBLUP model can be fitted. This fitting process consists of replacing, in the traditional BLUP model, the additive relationship matrix derived from a pedigree by the additive relationship matrix derived from markers. Thus, the objective of this study was to employ the lmekin function in the context of genomic prediction by comparing the results of this function with those obtained using five R packages for GS: rrBLUP, BGLR, sommer, lme4qtl, and lme4GS. The comparisons were performed considering the computational times and predicted values for a wheat dataset and simulated big data. In addition, we implemented a 5-fold cross-validation scheme through considering the values predicted by the lmekin function for the wheat dataset. The results indicated that the lmekin function was effective in predicting genomic breeding values considering multiple random effects and relatively small sample sizes. The rrBLUP package processed the fastest for the scenario with only one genetic random effect, and the high temporal efficiency of the sommer package was confirmed for the scenario with more than one genetic random effect. Differences in computational times occurred because of the different algorithms implemented in the packages to estimate the variance components.Universidade Estadual de Maringá2023-12-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/6424310.4025/actasciagron.v46i1.64243Acta Scientiarum. Agronomy; Vol 46 No 1 (2024): Publicação contínua; e64243Acta Scientiarum. Agronomy; v. 46 n. 1 (2024): Publicação contínua; e642431807-86211679-9275reponame:Acta Scientiarum. Agronomy (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/64243/751375156923Copyright (c) 2024 Acta Scientiarum. Agronomyhttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessSouza, Clemeson Silva de Santos, Vinícius Silva dos Martins Filho, Sebastião 2024-02-08T19:38:46Zoai:periodicos.uem.br/ojs:article/64243Revistahttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgronPUBhttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/oaiactaagron@uem.br||actaagron@uem.br|| edamasio@uem.br1807-86211679-9275opendoar:2024-02-08T19:38:46Acta Scientiarum. Agronomy (Online) - Universidade Estadual de Maringá (UEM)false
dc.title.none.fl_str_mv Genomic prediction using the lmekin function from the coxme R package
Genomic prediction using the lmekin function from the coxme R package
title Genomic prediction using the lmekin function from the coxme R package
spellingShingle Genomic prediction using the lmekin function from the coxme R package
Souza, Clemeson Silva de
mixed models; GBLUP; genomic relationship matrix; pedigree; genetic breeding.
mixed models; GBLUP; genomic relationship matrix; pedigree; genetic breeding.
title_short Genomic prediction using the lmekin function from the coxme R package
title_full Genomic prediction using the lmekin function from the coxme R package
title_fullStr Genomic prediction using the lmekin function from the coxme R package
title_full_unstemmed Genomic prediction using the lmekin function from the coxme R package
title_sort Genomic prediction using the lmekin function from the coxme R package
author Souza, Clemeson Silva de
author_facet Souza, Clemeson Silva de
Santos, Vinícius Silva dos
Martins Filho, Sebastião
author_role author
author2 Santos, Vinícius Silva dos
Martins Filho, Sebastião
author2_role author
author
dc.contributor.author.fl_str_mv Souza, Clemeson Silva de
Santos, Vinícius Silva dos
Martins Filho, Sebastião
dc.subject.por.fl_str_mv mixed models; GBLUP; genomic relationship matrix; pedigree; genetic breeding.
mixed models; GBLUP; genomic relationship matrix; pedigree; genetic breeding.
topic mixed models; GBLUP; genomic relationship matrix; pedigree; genetic breeding.
mixed models; GBLUP; genomic relationship matrix; pedigree; genetic breeding.
description The increasing use of genomic selection (GS) in plant and animal breeding programs has led to the development of software that fits models based on unique scenarios. Accordingly, several R packages have been developed for GS. The lmekin function from the coxme R package was one of the first functions implemented in R to fit models with random family effects using the pedigree–based relationship matrix. The function allows the user to provide the covariance structures for the random effects; thus, the GBLUP model can be fitted. This fitting process consists of replacing, in the traditional BLUP model, the additive relationship matrix derived from a pedigree by the additive relationship matrix derived from markers. Thus, the objective of this study was to employ the lmekin function in the context of genomic prediction by comparing the results of this function with those obtained using five R packages for GS: rrBLUP, BGLR, sommer, lme4qtl, and lme4GS. The comparisons were performed considering the computational times and predicted values for a wheat dataset and simulated big data. In addition, we implemented a 5-fold cross-validation scheme through considering the values predicted by the lmekin function for the wheat dataset. The results indicated that the lmekin function was effective in predicting genomic breeding values considering multiple random effects and relatively small sample sizes. The rrBLUP package processed the fastest for the scenario with only one genetic random effect, and the high temporal efficiency of the sommer package was confirmed for the scenario with more than one genetic random effect. Differences in computational times occurred because of the different algorithms implemented in the packages to estimate the variance components.
publishDate 2023
dc.date.none.fl_str_mv 2023-12-12
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/64243
10.4025/actasciagron.v46i1.64243
url http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/64243
identifier_str_mv 10.4025/actasciagron.v46i1.64243
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/64243/751375156923
dc.rights.driver.fl_str_mv Copyright (c) 2024 Acta Scientiarum. Agronomy
https://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2024 Acta Scientiarum. Agronomy
https://creativecommons.org/licenses/by/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Estadual de Maringá
publisher.none.fl_str_mv Universidade Estadual de Maringá
dc.source.none.fl_str_mv Acta Scientiarum. Agronomy; Vol 46 No 1 (2024): Publicação contínua; e64243
Acta Scientiarum. Agronomy; v. 46 n. 1 (2024): Publicação contínua; e64243
1807-8621
1679-9275
reponame:Acta Scientiarum. Agronomy (Online)
instname:Universidade Estadual de Maringá (UEM)
instacron:UEM
instname_str Universidade Estadual de Maringá (UEM)
instacron_str UEM
institution UEM
reponame_str Acta Scientiarum. Agronomy (Online)
collection Acta Scientiarum. Agronomy (Online)
repository.name.fl_str_mv Acta Scientiarum. Agronomy (Online) - Universidade Estadual de Maringá (UEM)
repository.mail.fl_str_mv actaagron@uem.br||actaagron@uem.br|| edamasio@uem.br
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