Estimate of genetic parameters for carcass traits and visual scores inmeat sheep using Bayesian inference via threshold and linear models

Detalhes bibliográficos
Autor(a) principal: Figueiredo Filho,Luiz Antonio Silva
Data de Publicação: 2017
Outros Autores: Sarmento,José Lindenberg Rocha, Ó,Alan Oliveira do, Santos,Natanael Pereira da Silva, Sena,Luciano Silva, Sousa Júnior,Antonio de
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Ciência Rural
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782017000300653
Resumo: ABSTRACT: The aim of this study was to estimate the variance components and genetic parameters for marbling in the ribeye area (MRA) and body condition score (BCS) using Bayesian inference via mixed linear and threshold animal models. Data were obtained from Santa Ines breed sheep reared in the Brazilian Mid-North region. Analyses considering the Monte Carlo methods were performed with Markov chains from 500000 cycles onward. A 200000-cycle initial burn-in was considered with values taken at every 250 cycles, in a total of 1200 samples. The Monte Carlo Error deviations were low for the means heritability in all chains by both linear and threshold models. Additive variances estimated by threshold model were higher than those estimated by the linear model. Marble meat from the ribeye area and body condition score can be used as selection criteria to obtain genetic progress in Santa Inês sheep.
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spelling Estimate of genetic parameters for carcass traits and visual scores inmeat sheep using Bayesian inference via threshold and linear modelsGibbs samplingcategorical datamarble meatultrasoundABSTRACT: The aim of this study was to estimate the variance components and genetic parameters for marbling in the ribeye area (MRA) and body condition score (BCS) using Bayesian inference via mixed linear and threshold animal models. Data were obtained from Santa Ines breed sheep reared in the Brazilian Mid-North region. Analyses considering the Monte Carlo methods were performed with Markov chains from 500000 cycles onward. A 200000-cycle initial burn-in was considered with values taken at every 250 cycles, in a total of 1200 samples. The Monte Carlo Error deviations were low for the means heritability in all chains by both linear and threshold models. Additive variances estimated by threshold model were higher than those estimated by the linear model. Marble meat from the ribeye area and body condition score can be used as selection criteria to obtain genetic progress in Santa Inês sheep.Universidade Federal de Santa Maria2017-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782017000300653Ciência Rural v.47 n.3 2017reponame:Ciência Ruralinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSM10.1590/0103-8478cr20160174info:eu-repo/semantics/openAccessFigueiredo Filho,Luiz Antonio SilvaSarmento,José Lindenberg RochaÓ,Alan Oliveira doSantos,Natanael Pereira da SilvaSena,Luciano SilvaSousa Júnior,Antonio deeng2017-01-18T00:00:00ZRevista
dc.title.none.fl_str_mv Estimate of genetic parameters for carcass traits and visual scores inmeat sheep using Bayesian inference via threshold and linear models
title Estimate of genetic parameters for carcass traits and visual scores inmeat sheep using Bayesian inference via threshold and linear models
spellingShingle Estimate of genetic parameters for carcass traits and visual scores inmeat sheep using Bayesian inference via threshold and linear models
Figueiredo Filho,Luiz Antonio Silva
Gibbs sampling
categorical data
marble meat
ultrasound
title_short Estimate of genetic parameters for carcass traits and visual scores inmeat sheep using Bayesian inference via threshold and linear models
title_full Estimate of genetic parameters for carcass traits and visual scores inmeat sheep using Bayesian inference via threshold and linear models
title_fullStr Estimate of genetic parameters for carcass traits and visual scores inmeat sheep using Bayesian inference via threshold and linear models
title_full_unstemmed Estimate of genetic parameters for carcass traits and visual scores inmeat sheep using Bayesian inference via threshold and linear models
title_sort Estimate of genetic parameters for carcass traits and visual scores inmeat sheep using Bayesian inference via threshold and linear models
author Figueiredo Filho,Luiz Antonio Silva
author_facet Figueiredo Filho,Luiz Antonio Silva
Sarmento,José Lindenberg Rocha
Ó,Alan Oliveira do
Santos,Natanael Pereira da Silva
Sena,Luciano Silva
Sousa Júnior,Antonio de
author_role author
author2 Sarmento,José Lindenberg Rocha
Ó,Alan Oliveira do
Santos,Natanael Pereira da Silva
Sena,Luciano Silva
Sousa Júnior,Antonio de
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Figueiredo Filho,Luiz Antonio Silva
Sarmento,José Lindenberg Rocha
Ó,Alan Oliveira do
Santos,Natanael Pereira da Silva
Sena,Luciano Silva
Sousa Júnior,Antonio de
dc.subject.por.fl_str_mv Gibbs sampling
categorical data
marble meat
ultrasound
topic Gibbs sampling
categorical data
marble meat
ultrasound
description ABSTRACT: The aim of this study was to estimate the variance components and genetic parameters for marbling in the ribeye area (MRA) and body condition score (BCS) using Bayesian inference via mixed linear and threshold animal models. Data were obtained from Santa Ines breed sheep reared in the Brazilian Mid-North region. Analyses considering the Monte Carlo methods were performed with Markov chains from 500000 cycles onward. A 200000-cycle initial burn-in was considered with values taken at every 250 cycles, in a total of 1200 samples. The Monte Carlo Error deviations were low for the means heritability in all chains by both linear and threshold models. Additive variances estimated by threshold model were higher than those estimated by the linear model. Marble meat from the ribeye area and body condition score can be used as selection criteria to obtain genetic progress in Santa Inês sheep.
publishDate 2017
dc.date.none.fl_str_mv 2017-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782017000300653
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782017000300653
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0103-8478cr20160174
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
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 v.47 n.3 2017
reponame:Ciência Rural
instname:Universidade Federal de Santa Maria (UFSM)
instacron:UFSM
instname_str Universidade Federal de Santa Maria (UFSM)
instacron_str UFSM
institution UFSM
reponame_str Ciência Rural
collection Ciência Rural
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1749140551228194816

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