Development of breeding values for susceptibility to virulent footrot in sheep: A strategy to accommodate variable disease progression at time of scoring

•Ovine footrot results from invasion of epidermal tissue of the hooves by bacteria.•Disease data from industry-recorded outbreaks will have diverse levels of expression.•The transition matrix models progression of footrot through clinical stages.•Transition matrix provides the opportunity to account for variation in prevalence.•Transitioning low incidence data instead of re-scoring will expedite treatment. Genetic evaluations utilising footrot scores from industry flocks in their essence, incorporate data from a wide range of challenge environments, resulting in potentially large differences in means, variances and distribution of scores across challenges. The date that commencement of infection occurs is generally unknown, and progression of the infection varies with the prevailing environmental and management conditions, virulence of the bacterium Dichelobacter nodosus, as well as the genetic potential and (permanent) environmental ability of animals to resist footrot. In practice, animals are unlikely to be repeatedly scored to identify the best time for comparison, or monitor development of disease progression. Furthermore, field challenges are limited by the need to treat animals before their welfare is compromised. Therefore, the duration and intensity of infection varies and this affects comparisons between animals for their susceptibility. Diseases such as footrot are characterised by multiple categorical scores reflecting clinical stages that describe the progression and relative impact of the disease. This provides the opportunity for the transformation of the data to a standardised prevalence. Scoring events from multiple footrot field challenges under a standardised protocol were used to establish a series of transition matrices to describe disease progression between scores over time. These transition matrices were used to standardise challenge events to the more severe scoring events, observed later in the challenge. The accuracy of the transition technique was tested by comparing the ranking of animals and sires against the observed scores. Transitioning the data from low disease prevalence to the higher prevalence at the subsequent scoring event improved the correlations between the scoring events, at the animal level, by upwards of 0.10 across challenges. The utilisation of a transition matrix to transform low prevalence disease challenges by taking into account the natural biological rate of progression through the clinical stages of the