62 Improved estimation of indirect social genetic effects in group-housed pigs by quantifying behavioral interactions

Abstract

Traditional social genetic effects modeling assumes uniform intensity of interaction between group members. Tree breeders proposed relaxing this assumption by incorporating estimates of intensity of competition between pairs of individuals. Here, we incorporated the quantification of aggressive interactions between pairs of animals in the estimation of indirect social genetic effects on skin lesions in the anterior part of the body in growing pigs. The data consisted of 491 pigs (215 barrows and 276 gilts, mean of 66 ±5 days of age). Animals were housed in 37 pens (11 to 15 pigs by pen) over 7 replicates. Trained scorers counted the number of skin lesions immediately before and 24 hours after mixing pigs. Animals were video-recorded for 9 hours post mixing and trained observers quantified the type and duration of aggressive interactions between pairs of pigs. The number of skin lesions in the frontal part of the body 24 hours post-mixing was the response variable and the number of seconds that pairs of animals spent engaged in reciprocal fights was used to quantify the intensity of interaction. We compared three different models: A direct genetic additive model (DGE), a traditional social genetic effect model (TSGE) assuming uniform interactions, and an improved social genetic effect model (ISGE) that used intensity of interaction to parameterize social genetic effects. All models included fixed effects of sex, replicate, lesion scorer, initial weight and pre-mixing lesion count; a random effect of pen; and a random direct genetic effect. The model ISGE recovered the most variance (smallest σe2) and resulted in the highest estimated h2 (P < 0.005). The model TSGE produced estimates that did not differ significantly from DGE (P = 1). Contrarily, incorporating the intensity of interaction into the modeling of ISGE allowed direct and indirect genetic effects to be estimated separately, even in a small dataset.