Bjarke G Poulsen1,2, Birgitte Ask3, Hanne M Nielsen4,3, Tage Ostersen3, Ole F Christensen4. 1. Center for Quantitative Genetics and Genomics, Blichers Allé 20, 8830, Tjele, Denmark. bgp@qgg.au.dk. 2. SEGES, Danish Pig Research Centre, Danish Agriculture and Food Council F.m.b.A., Axelborg, Axeltorv 3, 1609, Copenhagen V, Denmark. bgp@qgg.au.dk. 3. SEGES, Danish Pig Research Centre, Danish Agriculture and Food Council F.m.b.A., Axelborg, Axeltorv 3, 1609, Copenhagen V, Denmark. 4. Center for Quantitative Genetics and Genomics, Blichers Allé 20, 8830, Tjele, Denmark.
Abstract
BACKGROUND: Several 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 ([Formula: see text]) and a combined pedigree and genomic relationship matrix ([Formula: see text]); 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). RESULTS: The 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 [Formula: see text] rather than [Formula: see text] (5.9 to 6.3%). The difference between predictive performances of total genetic effects and direct genetic effects was smaller when [Formula: see text] was used rather than [Formula: see text]. CONCLUSIONS: This 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 [Formula: see text] than [Formula: see text]; (3) using [Formula: see text] rather than [Formula: see text] primarily improves the predictive performance of direct genetic effects.
BACKGROUND: Several 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 ([Formula: see text]) and a combined pedigree and genomic relationship matrix ([Formula: see text]); 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). RESULTS: The 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 [Formula: see text] rather than [Formula: see text] (5.9 to 6.3%). The difference between predictive performances of total genetic effects and direct genetic effects was smaller when [Formula: see text] was used rather than [Formula: see text]. CONCLUSIONS: This 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 [Formula: see text] than [Formula: see text]; (3) using [Formula: see text] rather than [Formula: see text] primarily improves the predictive performance of direct genetic effects.
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