Genetic analysis of days from calving to first insemination and days open in Danish Holsteins using different models and censoring scenarios.

The objectives of this study were to estimate genetic parameters and evaluate models for genetic evaluation of days from calving to first insemination (ICF) and days open (DO). Data including 509,512 first-parity records of Danish Holstein cows were analyzed using 5 alternative sire models that dealt with censored records in different ways: 1) a conventional linear model (LM) in which a penalty of 21 d was added to censored records; 2) a bivariate threshold-linear model (TLM), which included a threshold model for censoring status (0, 1) of the observations, and a linear model for ICF or DO without any penalty on censored records; 3) a right-censored linear model (CLM); 4) a Weibull proportional hazard model (SMW); and 5) a Cox proportional hazard model (SMC) constructed with piecewise constant baseline hazard function. The variance components for ICF and DO estimated from LM and TLM were similar, whereas CLM gave higher estimates of both additive genetic and residual components. Estimates of heritability from models LM, TLM, and CLM were very similar (0.102 to 0.108 for ICF, and 0.066 to 0.069 for DO). Heritabilities estimated using model SMW were 0.213 for ICF and 0.121 for DO in logarithmic scale. Using SMC, the estimates of heritability, defined as the log-hazard proportional factor for ICF and DO, were 0.013 and 0.009, respectively. Correlations between predicted transmitting ability from different models for sires with records from at least 20 daughters were far from unity, indicating that different models could lead to different rankings. The largest reranking was found between SMW and SMC, whereas negligible reranking was found among LM, TLM, and CLM. The 5 models were evaluated by comparing correlations between predicted transmitting ability from different data sets (the whole data set and 2 subsets, each containing half of the whole data set), for sires with records from at least 20 daughters, and chi(2) statistics based on predicted and observed daughter frequencies using a cross validation. The model comparisons showed that SMC had the best performance in predicting breeding values of the 2 traits. No significant difference was found among models LM, TLM, and CLM. The SMW model had a relatively poor performance, probably because the data are far from a Weibull distribution. The results from the present study suggest that SMC could be a good alternative for predicting breeding values of ICF and DO in the Danish Holstein population.