Genomic Prediction for Lean Meat Percentage and Average Daily Gain using Models Including Dominance and Imprinting Effects

Dominance and imprinting effects which may be important for complex traits are usually ignored in the genomic prediction of complex traits in livestock. The objectives of this study were to estimate genetic parameters of additive, dominance and imprinting effects and assess genomic predictions considering dominance and imprinting effects for lean meat Percentage (LMP) and average daily gain (ADG) in Danish Duroc pigs.

Corrected phenotypes of 8,113 genotyped pigs in both breeding and multiplier herds were used for the analysis. Four genomic BLUP models were compared with regard to prediction accuracy and bias: 1) additive model (GA), 2) additive and dominance model (GAD), 3) additive and imprinting model (GAI), 4) additive, dominance and imprinting model (GADI).

Based on GADI model, the estimated heritabilities were 0.37 (LMP) and 0.21 (ADG). GADI resulted to the smallest estimates of additive, litter and residual variances, respectively, while GA led the largest. Dominance is significant and accounted for about 3% of the phenotypic variances while imprinting is not significant and accounted for less than 1% of the phenotypic variances. Among all the models, GADI led to the highest reliability (51.4%) of predicted total genetic values (GTV) for LMP, and GAD led to the highest (27.7%) for ADG. The most unbiased predictions of GTV were provided by GAD model.

The results indicate that dominance effects significantly contribute to the genetic variation of LMP and ADG in Danish Duroc pigs, but imprinting effects are not. Models including dominance effects can improve accuracy and reduce bias of genomic predictions.