Linear and Poisson models for genetic evaluation of tick resistance in cross-bred Hereford x Nellore cattle
Nenhuma Miniatura disponível
Data
2013-04-15
Autores
Ayres, D. R.
Pereira, R. J.
Boligon, A. A.
Silva, F.F.
Schenkel, F. S.
Roso, V. M.
Albuquerque, L. G.
Título da Revista
ISSN da Revista
Título de Volume
Editor
Resumo
Cattle resistance to ticks is measured by the number of ticks infesting the animal. The model used for the genetic analysis of cattle resistance to ticks frequently requires logarithmic transformation of the observations. The objective of this study was to evaluate the predictive ability and goodness of fit of different models for the analysis of this trait in cross-bred Hereford x Nellore cattle. Three models were tested: a linear model using logarithmic transformation of the observations (MLOG); a linear model without transformation of the observations (MLIN); and a generalized linear Poisson model with residual term (MPOI). All models included the classificatory effects of contemporary group and genetic group and the covariates age of animal at the time of recording and individual heterozygosis, as well as additive genetic effects as random effects. Heritability estimates were 0.08 ± 0.02, 0.10 ± 0.02 and 0.14 ± 0.04 for MLIN, MLOG and MPOI models, respectively. The model fit quality, verified by deviance information criterion (DIC) and residual mean square, indicated fit superiority of MPOI model. The predictive ability of the models was compared by validation test in independent sample. The MPOI model was slightly superior in terms of goodness of fit and predictive ability, whereas the correlations between observed and predicted tick counts were practically the same for all models. A higher rank correlation between breeding values was observed between models MLOG and MPOI. Poisson model can be used for the selection of tick-resistant animals. © 2013 Blackwell Verlag GmbH.
Descrição
Palavras-chave
Generalized linear model, Model comparison, Validation test
Como citar
Journal of Animal Breeding and Genetics.