Improvement in genetic evaluation of quantitative traits in sheep by enriching genetic model with dominance effects

Abstract

Although dominance effects play a major role in quantitative genetics, most studies on quantitative traits have often neglected dominance effects, assuming alleles act additively. Therefore, the aim followed here was to quantify the proportion of variation in the early growth of Baluchi sheep that was attributed to dominance effects. Data collected over a 28-year period at the Baluchi sheep breeding station was used in this study. Traits evaluated were birth weight (BW), weaning weight (WW) and average daily gain (ADG). Each trait was analyzed with a series of twelve animal models which included different combinations of additive genetic, dominance genetic, maternal genetic and maternal permanent environmental effects. The Akaike's information criterion (AIC) was used to rank models. The predictive ability of models was measured using the mean squared error of prediction (MSE) and Pearson's correlation coefficient between the real and predicted values of records (r([Formula: see text],[Formula: see text])). Correlations between traits due to additive and dominance effects were estimated using bivariate analyses. For all traits studied, including dominance effects improved the likelihood of the fitting model. In addition, models that included dominance effects had the better predictive ability as provided higher r([Formula: see text],[Formula: see text]) and lower MSE. However, accounting for dominance effects significantly increased the computing burden evidenced by considerably longer computing time and a huge amount of memory required. By including dominance effects in the model, additive genetic variance did not change, but residual variance decreased significantly up to 41%, which indicated that the dominance component distangelled from residual variance. For BW, WW and ADG, dominance genetic variance was 6.61, 1.91, and 2.73 times greater than additive genetic variance and contributed 87%, 65% and 73% to the total genetic variance, respectively. Estimates of dominance heritability ([Formula: see text]), were 0.29 ± 0.06, 0.15 ± 0.07 and 0.20 ± 0.07 for BW, WW and ADG, respectively. Additive heritability ([Formula: see text]), was 0.05 ± 0.01 for BW, 0.08 ± 0.02 for WW and 0.07 ± 0.02 for ADG, respectively. By including dominance effects in the model, the accuracy of additive breeding values increased by 8%, 8% and 11% for BW, WW and ADG, respectively. Correlation between additive breeding values obtained from the best model and the best model without dominance effects were close to unity for all traits studied, indicating negligible changes in the additive breeding values and little chance for re-ranking of top animals across models. While additive genetic correlations were all positive and high, the dominance genetic correlation between WW and ADG was positively high (0.99), and between other pairs of traits was negative. Although the inclusion of dominance effects in the model did not change the ranking of top animals and had high computational requirements, it improved the predictive performance of the model and led to a significantly better data fit and an increase in the accuracy of additive breeding values. Therefore, including dominance effects in the model for genetic evaluation of the early growth of Baluchi lambs can be a reasonable recommendation.

Keywords: Animal model; Breeding values; Dominance effects; Heritability; Sheep.

Fig. 1

Computing time for analyses with and without dominance effects.

Fig. 2

Memory requirement for analyses with and without dominance effects.