Data/hora: 08/06/2024 17:59:57

Provedor de dados: 	Pesquisa Agropecuária Brasileira	
País: 	Brazil	
Título: 	Genetic evaluation models for post-weaning weight gain in a multibreed Angus-Nelore population	
Autores: 	Prestes,Alan Miranda; Oliveira,Mauricio Morgado de; Mello,Fernanda Cristina Breda; Rorato,Paulo Roberto Nogara; Lopes,Jader Silva; Feltes,Giovani Luis; Bravo,André Padilha.	
Data: 	2019-01-01	
Ano: 	2019	
Palavras-chave: 	Animal breeding; Crossbreeding; Dominance; Epistatic losses; Genetic parameters.	
Resumo: 	Abstract: The objective of this work was to identify the most suitable model for the genetic evaluation of post-weaning weight gain in a multibreed Angus-Nelore population. Three models were tested using the Bayesian inference method: traditional animal model (M1), multibreed animal model without (M2) and with segregation (M3). The choice of the best model followed the criteria: number of parameters (Np), deviance information criterion (DIC), conditional predictive ordinate (CPO), and deviance based on Bayes factors. Spearman’s rank correlations were estimated for the top 10, 20, and 30% sires. M1 presented the highest values for all criteria, except for Np, and the lowest direct heritability estimate of 0.15±0.01. The heritability estimates for M2 and M3 were higher and similar, being 0.29±0.02 and 0.27±0.02, respectively. M3 showed the lowest values for mean deviance, DIC, and CPO, being the best-fitting model among the three tested. Spearman’s correlation between the predicted genetic values for the models ranged from 0.69 to 0.99. The multibreed models are the most suitable for the genetic evaluation of multibreed populations, and M3 shows the best fit for the studied population.	
Tipo: 	Info:eu-repo/semantics/article	
Idioma: 	Inglês	
Identificador: 	http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2019000102803	
Editor: 	Embrapa Secretaria de Pesquisa e Desenvolvimento

Pesquisa Agropecuária Brasileira	
Relação: 	10.1590/s1678-3921.pab2019.v54.00694	
Formato: 	text/html	
Fonte: 	Pesquisa Agropecuária Brasileira  v.54 2019	
Fonte: 	info:eu-repo/semantics/openAccess