Genomic prediction for additive and dominance effects of censored traits in pigs.
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
Texto Completo: | http://www.alice.cnptia.embrapa.br/alice/handle/doc/1072712 http://dx.doi.org/10.4238/gmr15048764 |
Resumo: | Age at the time of slaughter is a commonly used trait in animal breeding programs. Since studying this trait involves incomplete observations (censoring), analysis can be performed using survival models or modified linear models, for example, by sampling censored data from truncated normal distributions. For genomic selection, the greatest genetic gains can be achieved by including non-additive genetic effects like dominance. Thus, censored traits with effects on both survival models have not yet been studied under a genomic selection approach. We aimed to predict genomic values using the Cox model with dominance effects and compare these results with the linear model with and without censoring. Linear models were fitted via the maximum likelihood method. For censored data, sampling through the truncated normal distribution was used, and the model was called the truncated normal linear via Gibbs sampling (TNL). We used an F2 pig population; the response variable was time (days) from birth to slaughter. Data were previously adjusted for fixed effects of sex and contemporary group. The model predictive ability was calculated based on correlation of predicted genomic values with adjusted phenotypic values. The results showed that both with and without censoring, there was high agreement between Cox and linear models in selection of individuals and markers. Despite including the dominance effect, there was no increase in predictive ability. This study showed, for the first time, the possibility of performing genomic prediction of traits with censored records while using the Cox survival model with additive and dominance effects. |
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Genomic prediction for additive and dominance effects of censored traits in pigs.GBLUPModelo mixtoCensored dataMixed modelSurvival modelsPorcoSuínoSwineAnimal breedingAge at the time of slaughter is a commonly used trait in animal breeding programs. Since studying this trait involves incomplete observations (censoring), analysis can be performed using survival models or modified linear models, for example, by sampling censored data from truncated normal distributions. For genomic selection, the greatest genetic gains can be achieved by including non-additive genetic effects like dominance. Thus, censored traits with effects on both survival models have not yet been studied under a genomic selection approach. We aimed to predict genomic values using the Cox model with dominance effects and compare these results with the linear model with and without censoring. Linear models were fitted via the maximum likelihood method. For censored data, sampling through the truncated normal distribution was used, and the model was called the truncated normal linear via Gibbs sampling (TNL). We used an F2 pig population; the response variable was time (days) from birth to slaughter. Data were previously adjusted for fixed effects of sex and contemporary group. The model predictive ability was calculated based on correlation of predicted genomic values with adjusted phenotypic values. The results showed that both with and without censoring, there was high agreement between Cox and linear models in selection of individuals and markers. Despite including the dominance effect, there was no increase in predictive ability. This study showed, for the first time, the possibility of performing genomic prediction of traits with censored records while using the Cox survival model with additive and dominance effects.V. S. SANTOS, Departamento de Estatística, Universidade Federal de Viçosa; S. MARTINS FILHO, Departamento de Estatística, Universidade Federal de Viçosa; MARCOS DEON VILELA DE RESENDE, CNPF; C. F. AZEVEDO, Departamento de Estatística, Universidade Federal de Viçosa; P. S. LOPES, Departamento de Zootecnia, Universidade Federal de Viçosa; S. E. F. GUIMARÃES, Departamento de Zootecnia, Universidade Federal de Viçosa; F. F. SILVA, Departamento de Zootecnia, Universidade Federal de Viçosa.SANTOS, V. S.MARTINS FILHO, S.RESENDE, M. D. V. deAZEVEDO, C. F.LOPES, P. S.GUIMARÃES, S. E. F.SILVA, F. F.2018-01-03T23:18:37Z2018-01-03T23:18:37Z2017-07-1420162018-01-03T23:18:37Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleGenetics and Molecular Research, v. 15, n. 4, gmr15048764, Oct. 2016.http://www.alice.cnptia.embrapa.br/alice/handle/doc/1072712http://dx.doi.org/10.4238/gmr15048764enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2018-01-03T23:18:43Zoai:www.alice.cnptia.embrapa.br:doc/1072712Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542018-01-03T23:18:43falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542018-01-03T23:18:43Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false |
dc.title.none.fl_str_mv |
Genomic prediction for additive and dominance effects of censored traits in pigs. |
title |
Genomic prediction for additive and dominance effects of censored traits in pigs. |
spellingShingle |
Genomic prediction for additive and dominance effects of censored traits in pigs. SANTOS, V. S. GBLUP Modelo mixto Censored data Mixed model Survival models Porco Suíno Swine Animal breeding |
title_short |
Genomic prediction for additive and dominance effects of censored traits in pigs. |
title_full |
Genomic prediction for additive and dominance effects of censored traits in pigs. |
title_fullStr |
Genomic prediction for additive and dominance effects of censored traits in pigs. |
title_full_unstemmed |
Genomic prediction for additive and dominance effects of censored traits in pigs. |
title_sort |
Genomic prediction for additive and dominance effects of censored traits in pigs. |
author |
SANTOS, V. S. |
author_facet |
SANTOS, V. S. MARTINS FILHO, S. RESENDE, M. D. V. de AZEVEDO, C. F. LOPES, P. S. GUIMARÃES, S. E. F. SILVA, F. F. |
author_role |
author |
author2 |
MARTINS FILHO, S. RESENDE, M. D. V. de AZEVEDO, C. F. LOPES, P. S. GUIMARÃES, S. E. F. SILVA, F. F. |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
V. S. SANTOS, Departamento de Estatística, Universidade Federal de Viçosa; S. MARTINS FILHO, Departamento de Estatística, Universidade Federal de Viçosa; MARCOS DEON VILELA DE RESENDE, CNPF; C. F. AZEVEDO, Departamento de Estatística, Universidade Federal de Viçosa; P. S. LOPES, Departamento de Zootecnia, Universidade Federal de Viçosa; S. E. F. GUIMARÃES, Departamento de Zootecnia, Universidade Federal de Viçosa; F. F. SILVA, Departamento de Zootecnia, Universidade Federal de Viçosa. |
dc.contributor.author.fl_str_mv |
SANTOS, V. S. MARTINS FILHO, S. RESENDE, M. D. V. de AZEVEDO, C. F. LOPES, P. S. GUIMARÃES, S. E. F. SILVA, F. F. |
dc.subject.por.fl_str_mv |
GBLUP Modelo mixto Censored data Mixed model Survival models Porco Suíno Swine Animal breeding |
topic |
GBLUP Modelo mixto Censored data Mixed model Survival models Porco Suíno Swine Animal breeding |
description |
Age at the time of slaughter is a commonly used trait in animal breeding programs. Since studying this trait involves incomplete observations (censoring), analysis can be performed using survival models or modified linear models, for example, by sampling censored data from truncated normal distributions. For genomic selection, the greatest genetic gains can be achieved by including non-additive genetic effects like dominance. Thus, censored traits with effects on both survival models have not yet been studied under a genomic selection approach. We aimed to predict genomic values using the Cox model with dominance effects and compare these results with the linear model with and without censoring. Linear models were fitted via the maximum likelihood method. For censored data, sampling through the truncated normal distribution was used, and the model was called the truncated normal linear via Gibbs sampling (TNL). We used an F2 pig population; the response variable was time (days) from birth to slaughter. Data were previously adjusted for fixed effects of sex and contemporary group. The model predictive ability was calculated based on correlation of predicted genomic values with adjusted phenotypic values. The results showed that both with and without censoring, there was high agreement between Cox and linear models in selection of individuals and markers. Despite including the dominance effect, there was no increase in predictive ability. This study showed, for the first time, the possibility of performing genomic prediction of traits with censored records while using the Cox survival model with additive and dominance effects. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016 2017-07-14 2018-01-03T23:18:37Z 2018-01-03T23:18:37Z 2018-01-03T23:18:37Z |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
Genetics and Molecular Research, v. 15, n. 4, gmr15048764, Oct. 2016. http://www.alice.cnptia.embrapa.br/alice/handle/doc/1072712 http://dx.doi.org/10.4238/gmr15048764 |
identifier_str_mv |
Genetics and Molecular Research, v. 15, n. 4, gmr15048764, Oct. 2016. |
url |
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1072712 http://dx.doi.org/10.4238/gmr15048764 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA |
instname_str |
Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
instacron_str |
EMBRAPA |
institution |
EMBRAPA |
reponame_str |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
collection |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
repository.name.fl_str_mv |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
repository.mail.fl_str_mv |
cg-riaa@embrapa.br |
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1794503447216652288 |