The optimal number of partial least squares components in genomic selection for pork pH
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFLA |
Texto Completo: | http://repositorio.ufla.br/jspui/handle/1/31888 |
Resumo: | The main application of genomic selection (GS) is the early identification of genetically superior animals for traits difficult-to-measure or lately evaluated, such as meat pH (measured after slaughter). Because the number of markers in GS is generally larger than the number of genotyped animals and these markers are highly correlated owing to linkage disequilibrium, statistical methods based on dimensionality reduction have been proposed. Among them, the partial least squares (PLS) technique stands out, because of its simplicity and high predictive accuracy. However, choosing the optimal number of components remains a relevant issue for PLS applications. Thus, we applied PLS (and principal component and traditional multiple regression) techniques to GS for pork pH traits (with pH measured at 45min and 24h after slaughter) and also identified the optimal number of PLS components based on the degree-of-freedom (DoF) and cross-validation (CV) methods. The PLS method out performs the principal component and traditional multiple regression techniques, enabling satisfactory predictions for pork pH traits using only genotypic data (low-density SNP panel). Furthermore, the SNP marker estimates from PLS revealed a relevant region on chromosome 4, which may affect these traits. The DoF and CV methods showed similar results for determining the optimal number of components in PLS analysis; thus, from the statistical viewpoint, the DoF method should be preferred because of its theoretical background (based on the "statistical information theory"), whereas CV is an empirical method based on computational effort. |
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The optimal number of partial least squares components in genomic selection for pork pHNúmero ótimo de componentes nos quadrados mínimos parciais aplicados à seleção genômica para pH da carne suínaSeleção genômicaQuadrados mínimos parciaisQualidade de carnePredição genômicaGenomic selectionPartial least squaresQuality of meatGenomic PredictionThe main application of genomic selection (GS) is the early identification of genetically superior animals for traits difficult-to-measure or lately evaluated, such as meat pH (measured after slaughter). Because the number of markers in GS is generally larger than the number of genotyped animals and these markers are highly correlated owing to linkage disequilibrium, statistical methods based on dimensionality reduction have been proposed. Among them, the partial least squares (PLS) technique stands out, because of its simplicity and high predictive accuracy. However, choosing the optimal number of components remains a relevant issue for PLS applications. Thus, we applied PLS (and principal component and traditional multiple regression) techniques to GS for pork pH traits (with pH measured at 45min and 24h after slaughter) and also identified the optimal number of PLS components based on the degree-of-freedom (DoF) and cross-validation (CV) methods. The PLS method out performs the principal component and traditional multiple regression techniques, enabling satisfactory predictions for pork pH traits using only genotypic data (low-density SNP panel). Furthermore, the SNP marker estimates from PLS revealed a relevant region on chromosome 4, which may affect these traits. The DoF and CV methods showed similar results for determining the optimal number of components in PLS analysis; thus, from the statistical viewpoint, the DoF method should be preferred because of its theoretical background (based on the "statistical information theory"), whereas CV is an empirical method based on computational effort.A principal contribuição da seleção genômica (SG) é a identificação de animais geneticamente superiores para características de difícil mensuração e/ou avaliadas tardiamente nos animais, tal como o pH da carne suína. Na SG, uma vez que o número de marcadores é geralmente maior que o número de animais genotipados, e tais marcadores são altamente correlacionados (devido ao desequilíbrio de ligação), métodos estatísticos baseados na redução de dimensionalidade têm sido propostos. Dentre estes, destaca-se o Quadrados Mínimos Parciais (PLS) pela simplicidade e alta acurácia de predição. Porém, a determinação do número ótimo de componentes a ser utilizado no PLS ainda se caracteriza como um desafio para a aplicação do método. Assim, objetivou-se aplicar o PLS (e também regressões em componentes principais e a múltipla tradicional) na SG para pH da carne suína (medido aos 45min e às 24 horas após o abate), bem como identificar o número ótimo de componentes por meio dos métodos do grau de liberdade (GL) e validação cruzada (VC). O primeiro é baseado na Teoria de Informação Estatística e VC é empírica e fundamentada em amostras independentes do arquivo original. O PLS superou os demais métodos de regressão, fornecendo predições satisfatórias quando utilizadas apenas informações genotípicas (painel de SNP de baixa densidade). Além disso, os efeitos dos SNPs estimados via PLS possibilitaram identificar uma região relevante no cromossomo 4 que pode influenciar as características estudadas. Os métodos GL e VC foram similares quanto à determinação do número ótimo de componentes na análise PLS, porém o método GL pode ser recomendado devido a sua maior fundamentação estatística.Universidade Federal de Santa Maria2018-11-23T10:16:40Z2018-11-23T10:16:40Z2017info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfSILVEIRA, F. G. da et al. The optimal number of partial least squares components in genomic selection for pork pH. Ciência Rural, Santa Maria, v. 47, n. 1, p. 1-5, 2017. doi: 10.1590/0103-8478cr20151563.http://repositorio.ufla.br/jspui/handle/1/31888Ciência Ruralreponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessSilveira, Fernanda Gomes daDuarte, Darlene Ana SouzaChaves, Lucas MonteiroSilva, Fabyano Fonseca eCarvalho Filho, IvanDuarte, Marcio de SouzaLopes, Paulo SávioGuimarães, Simone Eliza Facionieng2023-05-26T19:43:47Zoai:localhost:1/31888Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2023-05-26T19:43:47Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false |
dc.title.none.fl_str_mv |
The optimal number of partial least squares components in genomic selection for pork pH Número ótimo de componentes nos quadrados mínimos parciais aplicados à seleção genômica para pH da carne suína |
title |
The optimal number of partial least squares components in genomic selection for pork pH |
spellingShingle |
The optimal number of partial least squares components in genomic selection for pork pH Silveira, Fernanda Gomes da Seleção genômica Quadrados mínimos parciais Qualidade de carne Predição genômica Genomic selection Partial least squares Quality of meat Genomic Prediction |
title_short |
The optimal number of partial least squares components in genomic selection for pork pH |
title_full |
The optimal number of partial least squares components in genomic selection for pork pH |
title_fullStr |
The optimal number of partial least squares components in genomic selection for pork pH |
title_full_unstemmed |
The optimal number of partial least squares components in genomic selection for pork pH |
title_sort |
The optimal number of partial least squares components in genomic selection for pork pH |
author |
Silveira, Fernanda Gomes da |
author_facet |
Silveira, Fernanda Gomes da Duarte, Darlene Ana Souza Chaves, Lucas Monteiro Silva, Fabyano Fonseca e Carvalho Filho, Ivan Duarte, Marcio de Souza Lopes, Paulo Sávio Guimarães, Simone Eliza Facioni |
author_role |
author |
author2 |
Duarte, Darlene Ana Souza Chaves, Lucas Monteiro Silva, Fabyano Fonseca e Carvalho Filho, Ivan Duarte, Marcio de Souza Lopes, Paulo Sávio Guimarães, Simone Eliza Facioni |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Silveira, Fernanda Gomes da Duarte, Darlene Ana Souza Chaves, Lucas Monteiro Silva, Fabyano Fonseca e Carvalho Filho, Ivan Duarte, Marcio de Souza Lopes, Paulo Sávio Guimarães, Simone Eliza Facioni |
dc.subject.por.fl_str_mv |
Seleção genômica Quadrados mínimos parciais Qualidade de carne Predição genômica Genomic selection Partial least squares Quality of meat Genomic Prediction |
topic |
Seleção genômica Quadrados mínimos parciais Qualidade de carne Predição genômica Genomic selection Partial least squares Quality of meat Genomic Prediction |
description |
The main application of genomic selection (GS) is the early identification of genetically superior animals for traits difficult-to-measure or lately evaluated, such as meat pH (measured after slaughter). Because the number of markers in GS is generally larger than the number of genotyped animals and these markers are highly correlated owing to linkage disequilibrium, statistical methods based on dimensionality reduction have been proposed. Among them, the partial least squares (PLS) technique stands out, because of its simplicity and high predictive accuracy. However, choosing the optimal number of components remains a relevant issue for PLS applications. Thus, we applied PLS (and principal component and traditional multiple regression) techniques to GS for pork pH traits (with pH measured at 45min and 24h after slaughter) and also identified the optimal number of PLS components based on the degree-of-freedom (DoF) and cross-validation (CV) methods. The PLS method out performs the principal component and traditional multiple regression techniques, enabling satisfactory predictions for pork pH traits using only genotypic data (low-density SNP panel). Furthermore, the SNP marker estimates from PLS revealed a relevant region on chromosome 4, which may affect these traits. The DoF and CV methods showed similar results for determining the optimal number of components in PLS analysis; thus, from the statistical viewpoint, the DoF method should be preferred because of its theoretical background (based on the "statistical information theory"), whereas CV is an empirical method based on computational effort. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017 2018-11-23T10:16:40Z 2018-11-23T10:16:40Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
SILVEIRA, F. G. da et al. The optimal number of partial least squares components in genomic selection for pork pH. Ciência Rural, Santa Maria, v. 47, n. 1, p. 1-5, 2017. doi: 10.1590/0103-8478cr20151563. http://repositorio.ufla.br/jspui/handle/1/31888 |
identifier_str_mv |
SILVEIRA, F. G. da et al. The optimal number of partial least squares components in genomic selection for pork pH. Ciência Rural, Santa Maria, v. 47, n. 1, p. 1-5, 2017. doi: 10.1590/0103-8478cr20151563. |
url |
http://repositorio.ufla.br/jspui/handle/1/31888 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
http://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 Federal de Santa Maria |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria |
dc.source.none.fl_str_mv |
Ciência Rural reponame:Repositório Institucional da UFLA instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
Repositório Institucional da UFLA |
collection |
Repositório Institucional da UFLA |
repository.name.fl_str_mv |
Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
nivaldo@ufla.br || repositorio.biblioteca@ufla.br |
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1807835221618327552 |