Unravelling biological biotypes for growth, visual score and reproductive traits in Nellore cattle via principal component analysis

Detalhes bibliográficos
Autor(a) principal: Vargas, Giovana [UNESP]
Data de Publicação: 2018
Outros Autores: Schenkel, Flavio Schramm, Brito, Luiz Fernando, Neves, Haroldo Henrique de Rezende, Munari, Danísio Prado [UNESP], Boligon, Arione Augusti, Carvalheiro, Roberto [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1016/j.livsci.2018.09.010
http://hdl.handle.net/11449/189762
Resumo: Principal component analysis (PCA) is used to summarize important information from multivariate data in sets of new variables named principal components (PCs). In animal breeding, these new composite variables can be used to study the associations among multiple traits using the magnitude and direction of the PCA coefficients (in the eigenvectors) for each trait. Phenotypic data from 355 524 Nellore animals were used to estimate genetic parameters and explore the relationship among growth (weaning and post-weaning weight gain), visual score (weaning and yearling conformation, finishing precocity and muscling) and reproductive (scrotal circumference) traits using PCA. Genetic parameters were estimated by multi-trait analysis using a mixed linear animal model. The eigen-decomposition of the additive genetic (co)variance matrix (AT matrix) obtained using multi-trait analysis were used to calculate the PCs. In addition, PCA using the (co)variance matrix of the breeding values (EBVs) from single- and multi-trait analyses were investigated for comparison purposes. The direct heritability estimates for the weaning and yearling traits ranged from 0.17 (birth-to-weaning weight gain and conformation) to 0.21 (finishing precocity) and from 0.18 (weaning-to-yearling weight gain) to 0.46 (scrotal circumference), respectively. Genetic correlations estimated among all analyzed traits were positive (favorable) ranging from 0.15 (conformation at weaning and scrotal circumference) to 0.96 (finishing precocity and muscling at weaning). The first three PCs from multi-trait analysis using the eigen-decomposition of the AT matrix, explained 87.11% of the total additive genetic variance for the traits. The first PC (PC1) had negative and relatively similar coefficients for all traits, the second PC (PC2) contrasted the animals with early or late biotype, and the third PC (PC3) characterized a contrast between weaning and yearling traits. Our findings suggest that the PCA could be explored in breeding programs to select Nellore cattle to tailor selection towards specific PC, targeting, for instance, faster growth and precocious biotype.
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spelling Unravelling biological biotypes for growth, visual score and reproductive traits in Nellore cattle via principal component analysisBeef cattleBos taurus indicusEigen-decompositionGenetic correlationPrincipal componentsPrincipal component analysis (PCA) is used to summarize important information from multivariate data in sets of new variables named principal components (PCs). In animal breeding, these new composite variables can be used to study the associations among multiple traits using the magnitude and direction of the PCA coefficients (in the eigenvectors) for each trait. Phenotypic data from 355 524 Nellore animals were used to estimate genetic parameters and explore the relationship among growth (weaning and post-weaning weight gain), visual score (weaning and yearling conformation, finishing precocity and muscling) and reproductive (scrotal circumference) traits using PCA. Genetic parameters were estimated by multi-trait analysis using a mixed linear animal model. The eigen-decomposition of the additive genetic (co)variance matrix (AT matrix) obtained using multi-trait analysis were used to calculate the PCs. In addition, PCA using the (co)variance matrix of the breeding values (EBVs) from single- and multi-trait analyses were investigated for comparison purposes. The direct heritability estimates for the weaning and yearling traits ranged from 0.17 (birth-to-weaning weight gain and conformation) to 0.21 (finishing precocity) and from 0.18 (weaning-to-yearling weight gain) to 0.46 (scrotal circumference), respectively. Genetic correlations estimated among all analyzed traits were positive (favorable) ranging from 0.15 (conformation at weaning and scrotal circumference) to 0.96 (finishing precocity and muscling at weaning). The first three PCs from multi-trait analysis using the eigen-decomposition of the AT matrix, explained 87.11% of the total additive genetic variance for the traits. The first PC (PC1) had negative and relatively similar coefficients for all traits, the second PC (PC2) contrasted the animals with early or late biotype, and the third PC (PC3) characterized a contrast between weaning and yearling traits. Our findings suggest that the PCA could be explored in breeding programs to select Nellore cattle to tailor selection towards specific PC, targeting, for instance, faster growth and precocious biotype.Department of Animal Science School of Agricultural and Veterinarian Sciences Sao Paulo State University (UNESP), Via de Acesso Prof. Paulo Donato CastellaneCentre for Genetic Improvement of Livestock Department of Animal Biosciences University of Guelph, 50 Stone Road EastGenSys Associated ConsultantsDepartment of Exact Sciences School of Agricultural and Veterinarian Sciences Sao Paulo State University (UNESP), Via de Acesso Prof. Paulo Donato CastellaneNational Council for Science and Technological DevelopmentDepartment of Animal Science Universidade Federal de PelotasDepartment of Animal Science School of Agricultural and Veterinarian Sciences Sao Paulo State University (UNESP), Via de Acesso Prof. Paulo Donato CastellaneDepartment of Exact Sciences School of Agricultural and Veterinarian Sciences Sao Paulo State University (UNESP), Via de Acesso Prof. Paulo Donato CastellaneUniversidade Estadual Paulista (Unesp)University of GuelphGenSys Associated ConsultantsNational Council for Science and Technological DevelopmentUniversidade Federal de PelotasVargas, Giovana [UNESP]Schenkel, Flavio SchrammBrito, Luiz FernandoNeves, Haroldo Henrique de RezendeMunari, Danísio Prado [UNESP]Boligon, Arione AugustiCarvalheiro, Roberto [UNESP]2019-10-06T16:51:22Z2019-10-06T16:51:22Z2018-11-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article37-43http://dx.doi.org/10.1016/j.livsci.2018.09.010Livestock Science, v. 217, p. 37-43.1871-1413http://hdl.handle.net/11449/18976210.1016/j.livsci.2018.09.0102-s2.0-85053824364Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLivestock Scienceinfo:eu-repo/semantics/openAccess2024-06-06T13:43:31Zoai:repositorio.unesp.br:11449/189762Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T21:03:33.843601Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Unravelling biological biotypes for growth, visual score and reproductive traits in Nellore cattle via principal component analysis
title Unravelling biological biotypes for growth, visual score and reproductive traits in Nellore cattle via principal component analysis
spellingShingle Unravelling biological biotypes for growth, visual score and reproductive traits in Nellore cattle via principal component analysis
Vargas, Giovana [UNESP]
Beef cattle
Bos taurus indicus
Eigen-decomposition
Genetic correlation
Principal components
title_short Unravelling biological biotypes for growth, visual score and reproductive traits in Nellore cattle via principal component analysis
title_full Unravelling biological biotypes for growth, visual score and reproductive traits in Nellore cattle via principal component analysis
title_fullStr Unravelling biological biotypes for growth, visual score and reproductive traits in Nellore cattle via principal component analysis
title_full_unstemmed Unravelling biological biotypes for growth, visual score and reproductive traits in Nellore cattle via principal component analysis
title_sort Unravelling biological biotypes for growth, visual score and reproductive traits in Nellore cattle via principal component analysis
author Vargas, Giovana [UNESP]
author_facet Vargas, Giovana [UNESP]
Schenkel, Flavio Schramm
Brito, Luiz Fernando
Neves, Haroldo Henrique de Rezende
Munari, Danísio Prado [UNESP]
Boligon, Arione Augusti
Carvalheiro, Roberto [UNESP]
author_role author
author2 Schenkel, Flavio Schramm
Brito, Luiz Fernando
Neves, Haroldo Henrique de Rezende
Munari, Danísio Prado [UNESP]
Boligon, Arione Augusti
Carvalheiro, Roberto [UNESP]
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
University of Guelph
GenSys Associated Consultants
National Council for Science and Technological Development
Universidade Federal de Pelotas
dc.contributor.author.fl_str_mv Vargas, Giovana [UNESP]
Schenkel, Flavio Schramm
Brito, Luiz Fernando
Neves, Haroldo Henrique de Rezende
Munari, Danísio Prado [UNESP]
Boligon, Arione Augusti
Carvalheiro, Roberto [UNESP]
dc.subject.por.fl_str_mv Beef cattle
Bos taurus indicus
Eigen-decomposition
Genetic correlation
Principal components
topic Beef cattle
Bos taurus indicus
Eigen-decomposition
Genetic correlation
Principal components
description Principal component analysis (PCA) is used to summarize important information from multivariate data in sets of new variables named principal components (PCs). In animal breeding, these new composite variables can be used to study the associations among multiple traits using the magnitude and direction of the PCA coefficients (in the eigenvectors) for each trait. Phenotypic data from 355 524 Nellore animals were used to estimate genetic parameters and explore the relationship among growth (weaning and post-weaning weight gain), visual score (weaning and yearling conformation, finishing precocity and muscling) and reproductive (scrotal circumference) traits using PCA. Genetic parameters were estimated by multi-trait analysis using a mixed linear animal model. The eigen-decomposition of the additive genetic (co)variance matrix (AT matrix) obtained using multi-trait analysis were used to calculate the PCs. In addition, PCA using the (co)variance matrix of the breeding values (EBVs) from single- and multi-trait analyses were investigated for comparison purposes. The direct heritability estimates for the weaning and yearling traits ranged from 0.17 (birth-to-weaning weight gain and conformation) to 0.21 (finishing precocity) and from 0.18 (weaning-to-yearling weight gain) to 0.46 (scrotal circumference), respectively. Genetic correlations estimated among all analyzed traits were positive (favorable) ranging from 0.15 (conformation at weaning and scrotal circumference) to 0.96 (finishing precocity and muscling at weaning). The first three PCs from multi-trait analysis using the eigen-decomposition of the AT matrix, explained 87.11% of the total additive genetic variance for the traits. The first PC (PC1) had negative and relatively similar coefficients for all traits, the second PC (PC2) contrasted the animals with early or late biotype, and the third PC (PC3) characterized a contrast between weaning and yearling traits. Our findings suggest that the PCA could be explored in breeding programs to select Nellore cattle to tailor selection towards specific PC, targeting, for instance, faster growth and precocious biotype.
publishDate 2018
dc.date.none.fl_str_mv 2018-11-01
2019-10-06T16:51:22Z
2019-10-06T16:51:22Z
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 http://dx.doi.org/10.1016/j.livsci.2018.09.010
Livestock Science, v. 217, p. 37-43.
1871-1413
http://hdl.handle.net/11449/189762
10.1016/j.livsci.2018.09.010
2-s2.0-85053824364
url http://dx.doi.org/10.1016/j.livsci.2018.09.010
http://hdl.handle.net/11449/189762
identifier_str_mv Livestock Science, v. 217, p. 37-43.
1871-1413
10.1016/j.livsci.2018.09.010
2-s2.0-85053824364
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Livestock Science
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 37-43
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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