Symmetric differences squared and analysis of variance procedures for estimating genetic and environmental variances and covariances for beef XXXX weaning weight: I. Comparison via simulation.

@entity291 @entity214
Analysis of variance (ANOVA) and symmetric differences squared ( @entity214 ) methods for estimating genetic and environmental variances and covariances associated with beef @entity291 weaning weight were compared via simulation. Simulation was based on the pedigree and record structure of 503 beef weaning weights collected over 19 yr from a university herd. The @entity214 methodology was used with four models. The simplest model included direct (g) and maternal (gm) additive genetic effects, genetic covariance between direct and maternal additive genetic effects (sigma ggm), permanent maternal environmental effects (m) and temporary environmental effects (e). The second model also allowed for a nonzero environmental covariance (sigma mem) between dam and offspring weaning weights. Models 3 and 4 were models 1 and 2, respectively, expanded to include a grandmaternal genetic effect (gn) and covariances sigma ggn and sigma gmgn. Two ANOVA solution sets for the parameters of model 4 were obtained using sire, dam, maternal grandsire, maternal grandam and phenotypic variances and offspring-dam (covOD), offspring-sire (covOS), offspring-grandam (covOGD), and offspring-maternal half-aunt or uncle (covOMH) covariances. Four ANOVA solution sets for the parameters of model 2 were obtained using sire, dam, within dam and maternal grandsire variances, covOD and either covOS or covOGD. Two sets of 1,000 replicates of the data were simulated. These data were used to compare precision and accuracy of @entity214 and ANOVA estimators, to estimate correlations among @entity214 and ANOVA estimators, and to study the importance of taking inbreeding into account with @entity214 methodology. All ANOVA estimators for rho ggm were biased downward. The @entity214 procedure had a clear advantage over ANOVA. Averages of @entity214 estimates were closer to parameter values used to simulate the data and their standard deviations were generally smaller. The standard deviations of both @entity214 and ANOVA estimates of rho ggm were very large. It is important to allow for a nonzero sigma mem (at least when it is negative) when using @entity214 methods; otherwise estimators of sigma 2gm and sigma ggm are biased upward and downward, respectively.
