Genomic prediction for beef fatty acid profile in Nellore cattle

Detalhes bibliográficos
Autor(a) principal: Chiaia, Hermenegildo Lucas Justino [UNESP]
Data de Publicação: 2017
Outros Autores: Peripoli, Elisa [UNESP], Silva, Rafael Medeiros de Oliveira [UNESP], Aboujaoude, Carolyn [UNESP], Feitosa, Fabiele Loise Braga [UNESP], Lemos, Marcos Vinicius Antunes de [UNESP], Berton, Mariana Piatto [UNESP], Olivieri, Bianca Ferreira [UNESP], Espigolan, Rafael [UNESP], Tonussi, Rafael Lara [UNESP], Gordo, Daniel Gustavo Mansan [UNESP], Bresolin, Tiago [UNESP], Magalhães, Ana Fabrícia Braga [UNESP], Júnior, Gerardo Alves Fernandes [UNESP], Albuquerque, Lúcia Galvão de [UNESP], Oliveira, Henrique Nunes de [UNESP], Furlan, Joyce de Jesus Mangini, Ferrinho, Adrielle Mathias, Mueller, Lenise Freitas, Tonhati, Humberto [UNESP], Pereira, Angélica Simone Cravo, Baldi, Fernando [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1016/j.meatsci.2017.02.007
http://hdl.handle.net/11449/178657
Resumo: The objective of this study was to compare SNP-BLUP, BayesCπ, BayesC and Bayesian Lasso methodologies to predict the direct genomic value for saturated, monounsaturated, and polyunsaturated fatty acid profile, omega 3 and 6 in the Longissimus thoracis muscle of Nellore cattle finished in feedlot. A total of 963 Nellore bulls with phenotype for fatty acid profiles, were genotyped using the Illumina BovineHD BeadChip (Illumina, San Diego, CA) with 777,962 SNP. The predictive ability was evaluated using cross validation. To compare the methodologies, the correlation between DGV and pseudo-phenotypes was calculated. The accuracy varied from − 0.40 to 0.62. Our results indicate that none of the methods excelled in terms of accuracy, however, the SNP-BLUP method allows obtaining less biased genomic evaluations, thereby; this method is more feasible when taking into account the analyses' operating cost. Despite the lowest bias observed for EBV, the adjusted phenotype is the preferred pseudophenotype considering the genomic prediction accuracies regarding the context of the present study.
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spelling Genomic prediction for beef fatty acid profile in Nellore cattleBos indicusGenomic selectionLipid profileMeat qualityThe objective of this study was to compare SNP-BLUP, BayesCπ, BayesC and Bayesian Lasso methodologies to predict the direct genomic value for saturated, monounsaturated, and polyunsaturated fatty acid profile, omega 3 and 6 in the Longissimus thoracis muscle of Nellore cattle finished in feedlot. A total of 963 Nellore bulls with phenotype for fatty acid profiles, were genotyped using the Illumina BovineHD BeadChip (Illumina, San Diego, CA) with 777,962 SNP. The predictive ability was evaluated using cross validation. To compare the methodologies, the correlation between DGV and pseudo-phenotypes was calculated. The accuracy varied from − 0.40 to 0.62. Our results indicate that none of the methods excelled in terms of accuracy, however, the SNP-BLUP method allows obtaining less biased genomic evaluations, thereby; this method is more feasible when taking into account the analyses' operating cost. Despite the lowest bias observed for EBV, the adjusted phenotype is the preferred pseudophenotype considering the genomic prediction accuracies regarding the context of the present study.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Faculdade de Ciências Agrárias e Veterinárias UNESP JaboticabalFaculdade de Medicina Veterinária e Zootecnia USP PirassunungaFaculdade de Zootecnia e Engenharia de Alimentos USP PirassunungaFaculdade de Ciências Agrárias e Veterinárias UNESP JaboticabalFAPESP: #2009/16118-5FAPESP: (#2011/2141-0Universidade Estadual Paulista (Unesp)Universidade de São Paulo (USP)Chiaia, Hermenegildo Lucas Justino [UNESP]Peripoli, Elisa [UNESP]Silva, Rafael Medeiros de Oliveira [UNESP]Aboujaoude, Carolyn [UNESP]Feitosa, Fabiele Loise Braga [UNESP]Lemos, Marcos Vinicius Antunes de [UNESP]Berton, Mariana Piatto [UNESP]Olivieri, Bianca Ferreira [UNESP]Espigolan, Rafael [UNESP]Tonussi, Rafael Lara [UNESP]Gordo, Daniel Gustavo Mansan [UNESP]Bresolin, Tiago [UNESP]Magalhães, Ana Fabrícia Braga [UNESP]Júnior, Gerardo Alves Fernandes [UNESP]Albuquerque, Lúcia Galvão de [UNESP]Oliveira, Henrique Nunes de [UNESP]Furlan, Joyce de Jesus ManginiFerrinho, Adrielle MathiasMueller, Lenise FreitasTonhati, Humberto [UNESP]Pereira, Angélica Simone CravoBaldi, Fernando [UNESP]2018-12-11T17:31:30Z2018-12-11T17:31:30Z2017-06-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article60-67application/pdfhttp://dx.doi.org/10.1016/j.meatsci.2017.02.007Meat Science, v. 128, p. 60-67.0309-1740http://hdl.handle.net/11449/17865710.1016/j.meatsci.2017.02.0072-s2.0-850132778932-s2.0-85013277893.pdfScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengMeat Science1,643info:eu-repo/semantics/openAccess2024-06-07T18:43:35Zoai:repositorio.unesp.br:11449/178657Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T20:45:05.809305Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Genomic prediction for beef fatty acid profile in Nellore cattle
title Genomic prediction for beef fatty acid profile in Nellore cattle
spellingShingle Genomic prediction for beef fatty acid profile in Nellore cattle
Chiaia, Hermenegildo Lucas Justino [UNESP]
Bos indicus
Genomic selection
Lipid profile
Meat quality
title_short Genomic prediction for beef fatty acid profile in Nellore cattle
title_full Genomic prediction for beef fatty acid profile in Nellore cattle
title_fullStr Genomic prediction for beef fatty acid profile in Nellore cattle
title_full_unstemmed Genomic prediction for beef fatty acid profile in Nellore cattle
title_sort Genomic prediction for beef fatty acid profile in Nellore cattle
author Chiaia, Hermenegildo Lucas Justino [UNESP]
author_facet Chiaia, Hermenegildo Lucas Justino [UNESP]
Peripoli, Elisa [UNESP]
Silva, Rafael Medeiros de Oliveira [UNESP]
Aboujaoude, Carolyn [UNESP]
Feitosa, Fabiele Loise Braga [UNESP]
Lemos, Marcos Vinicius Antunes de [UNESP]
Berton, Mariana Piatto [UNESP]
Olivieri, Bianca Ferreira [UNESP]
Espigolan, Rafael [UNESP]
Tonussi, Rafael Lara [UNESP]
Gordo, Daniel Gustavo Mansan [UNESP]
Bresolin, Tiago [UNESP]
Magalhães, Ana Fabrícia Braga [UNESP]
Júnior, Gerardo Alves Fernandes [UNESP]
Albuquerque, Lúcia Galvão de [UNESP]
Oliveira, Henrique Nunes de [UNESP]
Furlan, Joyce de Jesus Mangini
Ferrinho, Adrielle Mathias
Mueller, Lenise Freitas
Tonhati, Humberto [UNESP]
Pereira, Angélica Simone Cravo
Baldi, Fernando [UNESP]
author_role author
author2 Peripoli, Elisa [UNESP]
Silva, Rafael Medeiros de Oliveira [UNESP]
Aboujaoude, Carolyn [UNESP]
Feitosa, Fabiele Loise Braga [UNESP]
Lemos, Marcos Vinicius Antunes de [UNESP]
Berton, Mariana Piatto [UNESP]
Olivieri, Bianca Ferreira [UNESP]
Espigolan, Rafael [UNESP]
Tonussi, Rafael Lara [UNESP]
Gordo, Daniel Gustavo Mansan [UNESP]
Bresolin, Tiago [UNESP]
Magalhães, Ana Fabrícia Braga [UNESP]
Júnior, Gerardo Alves Fernandes [UNESP]
Albuquerque, Lúcia Galvão de [UNESP]
Oliveira, Henrique Nunes de [UNESP]
Furlan, Joyce de Jesus Mangini
Ferrinho, Adrielle Mathias
Mueller, Lenise Freitas
Tonhati, Humberto [UNESP]
Pereira, Angélica Simone Cravo
Baldi, Fernando [UNESP]
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
Universidade de São Paulo (USP)
dc.contributor.author.fl_str_mv Chiaia, Hermenegildo Lucas Justino [UNESP]
Peripoli, Elisa [UNESP]
Silva, Rafael Medeiros de Oliveira [UNESP]
Aboujaoude, Carolyn [UNESP]
Feitosa, Fabiele Loise Braga [UNESP]
Lemos, Marcos Vinicius Antunes de [UNESP]
Berton, Mariana Piatto [UNESP]
Olivieri, Bianca Ferreira [UNESP]
Espigolan, Rafael [UNESP]
Tonussi, Rafael Lara [UNESP]
Gordo, Daniel Gustavo Mansan [UNESP]
Bresolin, Tiago [UNESP]
Magalhães, Ana Fabrícia Braga [UNESP]
Júnior, Gerardo Alves Fernandes [UNESP]
Albuquerque, Lúcia Galvão de [UNESP]
Oliveira, Henrique Nunes de [UNESP]
Furlan, Joyce de Jesus Mangini
Ferrinho, Adrielle Mathias
Mueller, Lenise Freitas
Tonhati, Humberto [UNESP]
Pereira, Angélica Simone Cravo
Baldi, Fernando [UNESP]
dc.subject.por.fl_str_mv Bos indicus
Genomic selection
Lipid profile
Meat quality
topic Bos indicus
Genomic selection
Lipid profile
Meat quality
description The objective of this study was to compare SNP-BLUP, BayesCπ, BayesC and Bayesian Lasso methodologies to predict the direct genomic value for saturated, monounsaturated, and polyunsaturated fatty acid profile, omega 3 and 6 in the Longissimus thoracis muscle of Nellore cattle finished in feedlot. A total of 963 Nellore bulls with phenotype for fatty acid profiles, were genotyped using the Illumina BovineHD BeadChip (Illumina, San Diego, CA) with 777,962 SNP. The predictive ability was evaluated using cross validation. To compare the methodologies, the correlation between DGV and pseudo-phenotypes was calculated. The accuracy varied from − 0.40 to 0.62. Our results indicate that none of the methods excelled in terms of accuracy, however, the SNP-BLUP method allows obtaining less biased genomic evaluations, thereby; this method is more feasible when taking into account the analyses' operating cost. Despite the lowest bias observed for EBV, the adjusted phenotype is the preferred pseudophenotype considering the genomic prediction accuracies regarding the context of the present study.
publishDate 2017
dc.date.none.fl_str_mv 2017-06-01
2018-12-11T17:31:30Z
2018-12-11T17:31:30Z
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.meatsci.2017.02.007
Meat Science, v. 128, p. 60-67.
0309-1740
http://hdl.handle.net/11449/178657
10.1016/j.meatsci.2017.02.007
2-s2.0-85013277893
2-s2.0-85013277893.pdf
url http://dx.doi.org/10.1016/j.meatsci.2017.02.007
http://hdl.handle.net/11449/178657
identifier_str_mv Meat Science, v. 128, p. 60-67.
0309-1740
10.1016/j.meatsci.2017.02.007
2-s2.0-85013277893
2-s2.0-85013277893.pdf
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Meat Science
1,643
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 60-67
application/pdf
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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