Using novel partitioning methodologies to enable genetic parameter estimation for reproductive traits affected by porcine reproductive and respiratory syndrome virus (PRRSV) in pigs

Abstract

Predictive models have previously been applied to viral diseases such as porcine reproductive and respiratory syndrome virus (PRRSV) and have demonstrated that selection for resistance can reduce the likelihood of epidemics or the impact of disease on infected animals (Bishop and MacKenzie, 2003). Key components of selection programs for disease resistance are the characterisation of genetic variation and the identification of genetic markers or QTL associated with resistance to, or tolerance of, the pathogen. The growing evidence for genetic variation in host susceptibility to PRRSV has been described in the review by Lewis et al. (2007). This study seeks to develop data partitioning methodologies for describing the impacts of disease on pig performance, and estimate heritabilities traits affected by PRRSV.