Influencia de la inclusión del efecto materno en la estimación de parámetros genéticos del peso al destete en un hato de ganado de carne

ABSTRACT: The component of (co)variance and genetic parameters for weaning weight trait in animals of pure (Brahman) or crossbreed (Zebu cattle) breeds were estimated, with the aim to estimate and to compare the genetic parameters and genetic value with or without including the maternal genetic effe...

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Autores:
Quintero, Juan Carlos
Triana, Juan G.
Quijano Bernal, Jorge Humberto
Arboleda Zapata, Elkin Mauricio
Tipo de recurso:
Article of investigation
Fecha de publicación:
2007
Institución:
Universidad de Antioquia
Repositorio:
Repositorio UdeA
Idioma:
spa
OAI Identifier:
oai:bibliotecadigital.udea.edu.co:10495/7779
Acceso en línea:
http://hdl.handle.net/10495/7779
Palabra clave:
Brahman
Cebú comercial
Efecto genético directo
Efecto genético materno
Ganado brahman
Ganado cebú
Ganado de carne
Parámetros genéticos
Peso al destete
Rights
openAccess
License
Atribución-NoComercial-CompartirIgual 2.5 Colombia (CC BY-NC-SA 2.5 CO)
Description
Summary:ABSTRACT: The component of (co)variance and genetic parameters for weaning weight trait in animals of pure (Brahman) or crossbreed (Zebu cattle) breeds were estimated, with the aim to estimate and to compare the genetic parameters and genetic value with or without including the maternal genetic effects by mean of Animal model, using MTDFREML program. Five models were considered including 1) the maternal genetic group, 2) calving, and 3) contemporary group as fixed effects, 4) direct and maternal genetic effects and 5) maternal permanent environmental effects, as random effects, and finally the residual error effect. Direct heritability coefficients varied form median to low (h2 a, 0.10 to 0.29) in all models. Models that included the maternal genetic effect presented the lowest values of maternal heritability (h2 m, 0.07 to 0.17). The genetic correlations between direct and maternal effects were negative and with low magnitude (-0.18 and -0.29). The models were compared between each other using the Maximum Likelihood method (- 2 log L) based on a X2 test, and it gave as result that the best model was the five one (Y = Xb + Z1a + Z3m + Z2ep + e) which included all effects.