Unravelling biological biotypes for growth, visual score and reproductive traits in Nellore cattle via principal component analysis
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , , |
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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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 |
|
_version_ |
1808129279444123648 |