Bayesian linear-threshold censored models for genetic evaluation of age at first calving and stayability in Nellore cattle

Abstract

We aimed to evaluate multiple-trait Bayesian models for handling censored records in genetic evaluations of age at first calving (AFC) and stayability (STAY) in Nellore cattle. Phenotypic and pedigree datasets were provided by Brazilian Association of Zebu Breeders. Four models were evaluated: linear-threshold model (LTM), penalty-threshold model (PTM), modified penalty-threshold model (MPTM) and linear-threshold-threshold model (LTTM). Predictive ability of the models was compared using cross-validation analysis. The validation population was composed by the youngest individuals with both parents known and phenotyped for AFC. Pearson's correlations between predict breeding values of the validation population with and without phenotypic information were computed. Percentages of animals selected in common among percentiles 1% and 10% were also calculated. In general, the correlations between predicted breeding values were high in magnitude, being the highest one observed for LTM (0.89). The heritability estimates (posterior mean) for AFC presented low magnitude and ranged from 0.02 (PTM and MPTM) to 0.07 (LTM); as well as the heritability estimates for STAY (0.09 to 0.16). The genetic correlations between AFC and STAY ranged from −0.23 to −0.51, being the highest one observed when using PTM. The inclusion of censored records in the genetic evaluation increased the residual variances. Genetic correlations between traits were not substantially influenced by the inclusion of censored data. In addition, the linear-threshold model without censored data presented the best predictive ability, thus we recommend this model for genetic evaluation of age at first calving and stayability in Nellore cattle.