Comparison of repeatability and multiple trait threshold models for litter size in sheep using observed and simulated data in Bayesian analyses

Bayesian analyses were used to estimate genetic parameters on 5580 records of litter size in the first four parities from 1758 Mule ewes. To examine the appropriateness of fitting repeatability (RM) or multiple trait threshold models (MTM) to litter size of different parities, both models were used to estimate genetic parameters on the observed data and were thereafter compared in a simulation study. Posterior means of the heritabilities of litter size in different parities using a MTM ranged from 0.12 to 0.18 and were higher than the heritability based on the RM (0.08). Posterior means of the genetic correlations between litter sizes of different parities were positive and ranged from 0.24 to 0.71. Data sets were simulated based on the same pedigree structure and genetic parameters of the Mule ewe population obtained from both models. The simulation showed that the relative loss in accuracy and increase in mean squared error (MSE) was substantially higher when using the RM, given that the parameters estimated from the observed data using the opposite model are the true parameters. In contrast, Bayesian information criterion (BIC) selected the RM as most appropriate model given the data because of substantial penalty for the higher number of parameters to be estimated in the MTM model. In conclusion, when the relative change in accuracy and MSE is of main interest for estimation of breeding values of litter size of different parities, the MTM is recommended for the given population. When reduction in risk of using the wrong model is the main aim, the BIC suggest that the RM is the most appropriate model.