Restricted maximum likelihood (REML) was used to determine the choice of statistical model, additive
genetic maternal and common litter effects and consequences of ignoring these effects on estimates of
variance–covariance components under random and phenotypic selection in swine using computer
simulation. Two closed herds of different size and two traits, (i) pre-weaning average daily gain and (ii) litter size at birth, were considered. Three levels of additive direct and maternal genetic correlations (rdm) were assumed to each trait. Four mixed models (denoted as GRM1 through GRM4) were used to generate data sets. Model GRM1 included only additive direct genetic effects, GRM2 included only additive direct genetic and common litter effects, GRM3 included only additive direct and maternal genetic effects and GRM4 included all the random effects. Four mixed animal models (defined as EPM1 through EPM4) were defined for estimating genetic parameters similar to GRM. Data from
each GRM were fitted with EPM1 through EPM4. The largest biased estimates of additive genetic
variance were obtained when EPM1 was fitted to data generated assuming the presence of either
additive maternal genetic, common litter effects or a combination thereof. The bias of estimated
additive direct genetic variance (VAd) increased and those of recidual variance (VE) decreased with an
increase in level of rdm when GRM3 was used. EPM1, EPM2 and EPM3 resulted in biased estimation
of the direct genetic variances. EPM4 was the most accurate in each GRM. Phenotypic selection
substantially increased bias of estimated additive direct genetic effect and its mean square error in trait 1, but decreased those in trait 2 when ignored in the statistical model. For trait 2, estimates under phenotypic selection were more biased than those under random selection. It was concluded that
statistical models for estimating variance components should include all random effects considered to
avoid bias.