Dominance genetic effect is rarely included in pedigree-based genetic evaluation, because large-scale datasets including a high proportion of full sibs are needed to estimate dominance effects accurately. With the availability of SNP markers and the development of genomic evaluation, estimates of dominance effects have become feasible. Usually, studies involving additive and dominance effects have ignored the relationships between them. However, mating in an inbred population will produce deviations from Hardy-Weinberg equilibrium, and these deviations generate correlations between breeding values and dominant deviations. Also, it has been often suggested that the magnitude of additive and dominance effects at the QTLs are related, but there is no existing applicable approach accounting for such correlation. Wellmann and Bennewitz showed that magnitudes of additive and dominant genetic effects in quantitative trait loci are related by the dominance coefficients δ=d/|a|, and they put forwarded two ways of directional relationships between additive and dominance effects, such as cor(|a|,δ)=0 (BayesD2) and cor(|a|,δ)>0 (BayesD3). Nevertheless, these relationships cannot be fitted in individual scales in the animal model and they are not compatible with standard animal breeding software. In this study, we present a simple way of fitting the correlation between genotypic additive and dominant effects in individual scales by using a combined additive and dominance relationship matrix computed from marker genotypes. Then through a simulation study, we show that such correlations can easily be estimated by animal breeding software and accuracy and unbiasedness of prediction for genetic values would be significantly improved if such correlations are used in GBLUP.