Abstract
BackgroundSeveral studies have found that the growth rate of a pig is influenced by the genetics of the group members (indirect genetic effects). Accounting for these indirect genetic effects in a selection program may increase genetic progress for growth rate. However, indirect genetic effects are small and difficult to predict accurately. Genomic information may increase the ability to predict indirect genetic effects. Thus, the objective of this study was to test whether including indirect genetic effects in the animal model increases the predictive performance when genetic effects are predicted with genomic relationships. In total, 11,255 pigs were phenotyped for average daily gain between 30 and 94 kg, and 10,995 of these pigs were genotyped. Two relationship matrices were used: a numerator relationship matrix ({mathbf{A}}) and a combined pedigree and genomic relationship matrix ({mathbf{H}}); and two different animal models were used: an animal model with only direct genetic effects and an animal model with both direct and indirect genetic effects. The predictive performance of the models was defined as the Pearson correlation between corrected phenotypes and predicted genetic levels. The predicted genetic level of a pig was either its direct genetic effect or the sum of its direct genetic effect and the indirect genetic effects of its group members (total genetic effect).ResultsThe highest predictive performance was achieved when total genetic effects were predicted with genomic information (21.2 vs. 14.7%). In general, the predictive performance was greater for total genetic effects than for direct genetic effects (0.1 to 0.5% greater; not statistically significant). Both types of genetic effects had greater predictive performance when they were predicted with {mathbf{H}} rather than {mathbf{A}} (5.9 to 6.3%). The difference between predictive performances of total genetic effects and direct genetic effects was smaller when {mathbf{H}} was used rather than {mathbf{A}}.ConclusionsThis study provides evidence that: (1) corrected phenotypes are better predicted with total genetic effects than with direct genetic effects only; (2) both direct genetic effects and indirect genetic effects are better predicted with {mathbf{H}} than {mathbf{A}}; (3) using {mathbf{H}} rather than {mathbf{A}} primarily improves the predictive performance of direct genetic effects.
Highlights
Several studies have found that the growth rate of a pig is influenced by the genetics of the group members
Some studies on growth rate in pigs show that including indirect genetic effects in the animal model improved its goodness-of-fit and/or its predictive performance [4, 9, 10], whereas other studies found neither of these results [3, 11, 12]
We hypothesize that: (1) the combined predictive performance of indirect and direct genetic effects is superior to the predictive performance of only direct effects; and (2) both prediction with the combination of indirect and direct genetic effects and prediction with direct genetic effects only is more accurate with genomic relationships than with pedigree relationships
Summary
Several studies have found that the growth rate of a pig is influenced by the genetics of the group members (indirect genetic effects). Some studies on growth rate in pigs show that including indirect genetic effects in the animal model improved its goodness-of-fit and/or its predictive performance [4, 9, 10], whereas other studies found neither of these results [3, 11, 12]. Some of these studies may have been challenged by both the complex nature of interactions between pigs and the fact that indirect genetic models are more sensitive to experimental design/data structure than classical animal models [9, 13,14,15,16,17]. A major challenge with predicting indirect genetic effects is that they are often small and thereby require more information for accurate prediction than direct genetic effects
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