PSI-16 Exploring complete blood count as a predictor of resilience in pigs using a natural disease challenge model

Abstract

Abstract Disease resilience is defined as an animal’s ability to maintain a relatively undepressed performance in the face of infections, due to optimal resource allocation between immunity and productivity. Extending breeding goals with resilience is a pragmatic way to improve herd health and reduce economic losses related to infectious diseases. To make genetic improvement for resilience, there is a need for predictors that can be obtained in high health nucleus herds where the selection of breeding animals takes place. Therefore, our study aimed to determine whether a clinical measure, CBC, is a useful indicator trait for resilience. Least square means and variance component analyses were conducted using CBC and 660K SNP genotype data of 2593 pigs that went through a nursery-to-finish natural disease challenge model and exhibited divergent responses in terms of growth and individual medication. CBC taken from healthy pigs before challenge did not show differences between resilient and susceptible pigs. However, resilient animals showed a significantly greater increase of lymphocytes at the early stages of infection and hemoglobin at the late stage. Neutrophils in resilient animals showed a tendency for a reduction during the late stage of infection. These results suggest that CBC traits could provide an indication of a change in resource allocation in response to infection. Resilient animals are expected to allocate more resources towards immunity during the early stages of infection to help limit infection so as to resolve inflammation and recover earlier in order to maintain high rates of production. CBC traits were heritable and genetically correlated with growth and treatment, which may indicate the potential to develop CBC as a predictor for the selection of resilience among breeding animals. Further studies are underway to test if CBC traits have value as an indicator of resilience in combination with genome-wide association studies and genomic prediction.