Linear and Poisson models for genetic evaluation of tick resistance in cross‐bred Hereford x Nellore cattle

Abstract

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.

Genotype by environment interaction for tick resistance of Hereford and Braford beef cattle using reaction norm models

“…4a). Similar heritability estimates have been reported in the literature, using models such as M 1 and logarithmic transformations of the observed data [1, 5]. With M 12 , average heritability estimates were higher, which also indirectly indicates the better fit of one-step versus two-step models that consider residual heteroscedasticity.…”

“…1), a log-transformation was used such that LTTC = log 10 (TC + 1.001), which was used as the response variable [1, 20]. The constant 1.001 was included because some TC were equal to 0 [1, 20].…”

“…1), a log-transformation was used such that LTTC = log 10 (TC + 1.001), which was used as the response variable [1, 20]. The constant 1.001 was included because some TC were equal to 0 [1, 20]. Skewness and kurtosis tests were performed and ensured the normality of the residuals from the fitted models.

Fig.

…”

“…To ensure market competitiveness, one strategy might be to increase the contribution of the Bos taurus breeds to Brazilian herds because they are more advantageous in terms of productive traits [1], such as carcass yield, gain weight, meat quality and sexual precocity compared to Bos indicus breeds. However, Bos taurus breeds tend to have greater susceptibility to tick infestation than Bos indicus breeds [2, 3].…”

A comparison of nonlinear mixed models and response to selection of tick-infestation on lambs

“…However, the raw distribution of tick count data (Fig 1) diverges clearly from the distribution expected for litter size. In fact, the only precedent in analysing tick count data was a study of crossbreed Hereford x Nellore cattle [54] suggesting that the Poisson model fits better than linear model. However, there was also a clear excess of zeroes (48.6%) in that data set, indicating that zero inflated distributions could have performed even better as shown by the model comparison analysis described above.…”