Dataset Information

Estimation of indirect social genetic effects for skin lesion count in group-housed pigs by quantifying behavioral interactions1.

ABSTRACT: Mixing of pigs into new social groups commonly induces aggressive interactions that result in skin lesions on the body of the animals. The relationship between skin lesions and aggressive behavioral interactions in group-housed pigs can be analyzed within the framework of social genetic effects (SGE). This study incorporates the quantification of aggressive interactions between pairs of animals in the modeling of SGE for skin lesions in different regions of the body in growing pigs. The dataset included 792 pigs housed in 59 pens. Skin lesions in the anterior, central, and caudal regions of the body were counted 24 h after pig mixing. Animals were video-recorded for 9 h postmixing and trained observers recorded the type and duration of aggressive interactions between pairs of animals. The number of seconds that pairs of pigs spent engaged in reciprocal fights and unilateral attack behaviors were used to parametrize the intensity of social interactions (ISI). Three types of models were fitted: direct genetic additive model (DGE), traditional social genetic effect model (TSGE) assuming uniform interactions between dyads, and an intensity-based social genetic effect model (ISGE) that used ISI to parameterize SGE. All models included fixed effects of sex, replicate, lesion scorer, weight at mixing, premixing lesion count, and the total time that the animal spent engaged in aggressive interactions (reciprocal fights and unilateral attack behaviors) as a covariate; a random effect of pen; and a random direct genetic effect. The ISGE models recovered more direct genetic variance than DGE and TSGE, and the estimated heritabilities (h^D2) were highest for all traits (P < 0.01) for the ISGE with ISI parametrized with unilateral attack behavior. The TSGE produced estimates that did not differ significantly from DGE (P > 0.5). Incorporating the ISI into ISGE, even in a small dataset, allowed separate estimation of the genetic parameters for direct and SGE, as well as the genetic correlation between direct and SGE (r^ds), which was positive for all lesion traits. The estimates from ISGE suggest that if behavioral observations are available, selection incorporating SGE may reduce the consequences of aggressive behaviors after mixing pigs.

SUBMITTER: Angarita BK

PROVIDER: S-EPMC6735787 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Estimation of indirect social genetic effects for skin lesion count in group-housed pigs by quantifying behavioral interactions1.

Angarita Belcy K BK Cantet Rodolfo J C RJC Wurtz Kaitlin E KE O’Malley Carly I O CIO Siegford Janice M JM Ernst Catherine W CW Turner Simon P SP Steibel Juan P JP

Journal of animal science 20190901 9

Mixing of pigs into new social groups commonly induces aggressive interactions that result in skin lesions on the body of the animals. The relationship between skin lesions and aggressive behavioral interactions in group-housed pigs can be analyzed within the framework of social genetic effects (SGE). This study incorporates the quantification of aggressive interactions between pairs of animals in the modeling of SGE for skin lesions in different regions of the body in growing pigs. The dataset ...[more]

PMID: 31373628

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Project description:Poor health is a risk factor for damaging behaviors, but the mechanisms behind this link are unknown. Injection of pigs with lipopolysaccharide (LPS) can be used to model aspects of poor health. Recent studies have shown that LPS-injected pigs perform more tail- and ear-directed behavior compared to saline-injected pigs and suggest that pro-inflammatory cytokines may play a role in these behaviors. The aims of this study were to test the effect of LPS on the social behavior of pigs and the neurotransmitters and modulators in their brains and to test the effect of a nonsteroidal anti-inflammatory drug on the effects of LPS. Fifty-two female pigs (11-12 weeks) were allocated to four treatments comprising two injections: saline-saline (SS), saline-LPS (SL), ketoprofen-saline (KS), and ketoprofen-LPS (KL). Activity was scan-sampled every 5 min for 6 h after the last injection in the pen. Social behavior was observed continuously in 10 × 15-min bouts between 8 a.m. and 5 p.m. 1 day before (baseline) and 1 and 2 days after the injection. Saliva was analyzed for cortisol and plasma for tryptophan and kynurenine. The frontal cortex, hippocampus, hypothalamus, and brain stem were sampled 72 h after the injection and analyzed for cytokines and monoamines. LPS activated the HPA axis and decreased the activity within 6 h after the injection. Ketoprofen lowered the effect of LPS on cortisol release and attenuated the behavioral signs of sickness in challenged pigs. SL pigs manipulated the ears of their pen mates significantly longer than SS pigs 2 days after the injection. LPS had no observed effect on IFN-γ, TNF-α, and IL-18. At 72 h after the injection, plasma tryptophan was depleted in SL pigs, and tryptophan and kynurenine concentrations in the frontal cortex and brain stem of SL pigs were significantly lower compared to those in SS pigs. Dopamine concentrations in the hypothalamus of SL pigs were significantly lower compared to those in SS pigs. Serotonin concentrations in the hypothalamus and noradrenaline concentrations in the hippocampus of SL pigs were significantly lower compared to those in KL pigs. In conclusion, LPS influenced the different neurotransmitters and modulators in the brain that are hypothesized to play an important role in the regulation of mood and behavior.

Project description:Judgement bias testing has emerged as a potential tool for assessing affective states in animals. Researchers infer an animal's affective state based on an animal's response to an ambiguous stimulus that is intermediate to both the rewarded and punished conditioned stimuli. Animals can be classified as "optimistic" or having a positive affective state if the animal displays behaviors that suggest an increased expectation of reward in the face of ambiguous stimuli. Alternatively, animals can be classified "pessimistic" or having a negative affective state if the animal displays behaviors that suggest an increased expectation of punishment in the face of ambiguous stimuli. Recent reports in multiple species question what factors influence performance in judgement bias testing, and which may allow for erroneous conclusions regarding individual affective state. In order to better understand this concern, 25 female swine were subjected to behavioral assessments at critical rearing stages to determine response variability. These same individuals were then assessed for physical measures of welfare and judgement bias using the "go/no-go" task as breeding adults. Sows which were more aggressive approached the ambiguous, but not the positive, stimulus significantly faster than others. Both optimistic and pessimistic biases were observed despite all sows living in enriched housing, and, sows with more positive physical welfare measures (fewer skin lesions and healthy body condition) did not exhibit more optimistic judgement biases. Our data demonstrate that behavior traits, such as aggressiveness, can affect a sow's performance in a judgement bias test, while measures of physical health did not. We suggest that individual differences in behavior (e.g., bold-aggressive behavioral syndrome, or, proactive coping style) generate different emotional responses and can contribute to the animal's overall affective state more so than physical ailment. Our findings highlight the complexity of how different factors impact an animal's overall affective state and support the need for complementary measures in future JBT studies, including personality assessment.

Dataset Information

Estimation of indirect social genetic effects for skin lesion count in group-housed pigs by quantifying behavioral interactions1.

Publications

Estimation of indirect social genetic effects for skin lesion count in group-housed pigs by quantifying behavioral interactions1.

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