Genetic evaluation models for resistance to ticks in a crossbred Angus-Nellore population
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Ciência Rural |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782021001000653 |
Resumo: | ABSTRACT: This research identified the animal model that best describes the genetic and residual variations for tick counts in yearlings from a crossbred Angus-Nellore population of 6,951 animals that are progenies of 382 bulls and 6,198 cows. Genetic values were predicted by the Bayesian inference methodology. The models tested were: Traditional Animal, and Crossbred Animal with and without segregation, considering residual homoscedastic and heteroscedastic variances. The criteria of choice were the number of parameters, deviance information, and predictive order, which indicated the best fitfor the Traditional Animal model and Crossbred Animal model (with segregation), both with residual heteroscedastic Gaussian variance. The mean values of fixed genetic effects were positive and similar in the both models, indicating that animals with higher proportion of the Angus breed had greater infestation, and the Nellore breed was an important addition for resistance to ticks. The estimated genetic variation by the heteroscedastic Gaussian Animal model for the Nellore breed was 4.54-fold higher than that estimated for the Angus breed. The estimates of heritability of the different genetic groups ranged from 0.12 to 0.15 and from 0.01 to 0.35, respectively, for the Traditional Animal model and for the heteroscedastic Gaussian crossbred model. The Spearman’s rank-order correlation for the predicted genetic values was 0.94, considering all sires. However, when considering the top 10%, 20%, and 30% sires, differences in ranking were more evident (0.28 to 0.67). The Crossbred Animal model with segregation and heterogeneous residual variances was the most appropriate for genetic evaluation of tick counts on animals from Angus-Nellore crossings. |
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Ciência rural (Online) |
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Genetic evaluation models for resistance to ticks in a crossbred Angus-Nellore populationheteroscedasticity of variancescrossbred animal modelSpearman’s correlationABSTRACT: This research identified the animal model that best describes the genetic and residual variations for tick counts in yearlings from a crossbred Angus-Nellore population of 6,951 animals that are progenies of 382 bulls and 6,198 cows. Genetic values were predicted by the Bayesian inference methodology. The models tested were: Traditional Animal, and Crossbred Animal with and without segregation, considering residual homoscedastic and heteroscedastic variances. The criteria of choice were the number of parameters, deviance information, and predictive order, which indicated the best fitfor the Traditional Animal model and Crossbred Animal model (with segregation), both with residual heteroscedastic Gaussian variance. The mean values of fixed genetic effects were positive and similar in the both models, indicating that animals with higher proportion of the Angus breed had greater infestation, and the Nellore breed was an important addition for resistance to ticks. The estimated genetic variation by the heteroscedastic Gaussian Animal model for the Nellore breed was 4.54-fold higher than that estimated for the Angus breed. The estimates of heritability of the different genetic groups ranged from 0.12 to 0.15 and from 0.01 to 0.35, respectively, for the Traditional Animal model and for the heteroscedastic Gaussian crossbred model. The Spearman’s rank-order correlation for the predicted genetic values was 0.94, considering all sires. However, when considering the top 10%, 20%, and 30% sires, differences in ranking were more evident (0.28 to 0.67). The Crossbred Animal model with segregation and heterogeneous residual variances was the most appropriate for genetic evaluation of tick counts on animals from Angus-Nellore crossings.Universidade Federal de Santa Maria2021-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782021001000653Ciência Rural v.51 n.10 2021reponame:Ciência Ruralinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSM10.1590/0103-8478cr20200500info:eu-repo/semantics/openAccessBravo,André PadilhaPrestes,Alan MirandaOliveira,Mauricio Morgado deRoso,Vanerlei MozaquatroMello,Fernanda Cristina BredaRorato,Paulo Roberto Nogaraeng2021-07-15T00:00:00ZRevista |
dc.title.none.fl_str_mv |
Genetic evaluation models for resistance to ticks in a crossbred Angus-Nellore population |
title |
Genetic evaluation models for resistance to ticks in a crossbred Angus-Nellore population |
spellingShingle |
Genetic evaluation models for resistance to ticks in a crossbred Angus-Nellore population Bravo,André Padilha heteroscedasticity of variances crossbred animal model Spearman’s correlation |
title_short |
Genetic evaluation models for resistance to ticks in a crossbred Angus-Nellore population |
title_full |
Genetic evaluation models for resistance to ticks in a crossbred Angus-Nellore population |
title_fullStr |
Genetic evaluation models for resistance to ticks in a crossbred Angus-Nellore population |
title_full_unstemmed |
Genetic evaluation models for resistance to ticks in a crossbred Angus-Nellore population |
title_sort |
Genetic evaluation models for resistance to ticks in a crossbred Angus-Nellore population |
author |
Bravo,André Padilha |
author_facet |
Bravo,André Padilha Prestes,Alan Miranda Oliveira,Mauricio Morgado de Roso,Vanerlei Mozaquatro Mello,Fernanda Cristina Breda Rorato,Paulo Roberto Nogara |
author_role |
author |
author2 |
Prestes,Alan Miranda Oliveira,Mauricio Morgado de Roso,Vanerlei Mozaquatro Mello,Fernanda Cristina Breda Rorato,Paulo Roberto Nogara |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Bravo,André Padilha Prestes,Alan Miranda Oliveira,Mauricio Morgado de Roso,Vanerlei Mozaquatro Mello,Fernanda Cristina Breda Rorato,Paulo Roberto Nogara |
dc.subject.por.fl_str_mv |
heteroscedasticity of variances crossbred animal model Spearman’s correlation |
topic |
heteroscedasticity of variances crossbred animal model Spearman’s correlation |
description |
ABSTRACT: This research identified the animal model that best describes the genetic and residual variations for tick counts in yearlings from a crossbred Angus-Nellore population of 6,951 animals that are progenies of 382 bulls and 6,198 cows. Genetic values were predicted by the Bayesian inference methodology. The models tested were: Traditional Animal, and Crossbred Animal with and without segregation, considering residual homoscedastic and heteroscedastic variances. The criteria of choice were the number of parameters, deviance information, and predictive order, which indicated the best fitfor the Traditional Animal model and Crossbred Animal model (with segregation), both with residual heteroscedastic Gaussian variance. The mean values of fixed genetic effects were positive and similar in the both models, indicating that animals with higher proportion of the Angus breed had greater infestation, and the Nellore breed was an important addition for resistance to ticks. The estimated genetic variation by the heteroscedastic Gaussian Animal model for the Nellore breed was 4.54-fold higher than that estimated for the Angus breed. The estimates of heritability of the different genetic groups ranged from 0.12 to 0.15 and from 0.01 to 0.35, respectively, for the Traditional Animal model and for the heteroscedastic Gaussian crossbred model. The Spearman’s rank-order correlation for the predicted genetic values was 0.94, considering all sires. However, when considering the top 10%, 20%, and 30% sires, differences in ranking were more evident (0.28 to 0.67). The Crossbred Animal model with segregation and heterogeneous residual variances was the most appropriate for genetic evaluation of tick counts on animals from Angus-Nellore crossings. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-01-01 |
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=S0103-84782021001000653 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782021001000653 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0103-8478cr20200500 |
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.51 n.10 2021 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_ |
1749140556161744896 |