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Using Factor Analysis Modeling Multiple Traits in Genetic Improvement of Nelore Beef Cattle
Using Factor Analysis Modeling Multiple Traits in Genetic Improvement of Nelore Beef Cattle
Friday, August 22, 2014
Posters (The Westin Bayshore)
Abstract Text: Genetic parameters for ultrasound carcass
and growth traits were estimated by factor analyses used as
a special case of structural equation models in a Bayesian
framework. Data were analyzed using the standard multitrait
mixed models with sire model (Model 1; SMTMs).
The factor analyses (FA) were done by four alternative FA
models. The results indicate that FA models could estimate
breeding values of the bulls practically equal relative to the
SMTMs. The FA models may reduce the ranking model
and give a parsimonious estimation of genetic covariance
matrices. Although the FA models may reduce covariance
matrices ranks and give a parsimonious estimation of
dispersion parameters, these models have to be tested in
order to implement the benefits, as an alternative of
SMTMs. Keywords: animal breeding, genetic parameters, structural equation models
and growth traits were estimated by factor analyses used as
a special case of structural equation models in a Bayesian
framework. Data were analyzed using the standard multitrait
mixed models with sire model (Model 1; SMTMs).
The factor analyses (FA) were done by four alternative FA
models. The results indicate that FA models could estimate
breeding values of the bulls practically equal relative to the
SMTMs. The FA models may reduce the ranking model
and give a parsimonious estimation of genetic covariance
matrices. Although the FA models may reduce covariance
matrices ranks and give a parsimonious estimation of
dispersion parameters, these models have to be tested in
order to implement the benefits, as an alternative of
SMTMs. Keywords: animal breeding, genetic parameters, structural equation models
