Genet. Sel. Evol. 37 (2005) 437-453
DOI: 10.1051/gse:2005010

Genetic management of infectious diseases: a heterogeneous epidemio-genetic model illustrated with S. aureus mastitis

Johann C. Detilleux

Quantitative Genetics Group, Department of Animal Production, Faculty of Veterinary Medicine, University of Liège, Liège, Belgium

(Received 7 October 2004; accepted 27 January 2005)

Abstract - Given that individuals are genetically heterogeneous in their degree of resistance to infection, a model is proposed to formulate appropriate choices that will limit the spread of an infectious disease. The model is illustrated with data on S. aureus mastitis and is based on parameters characterizing the spread of the disease (contact rate, probability of infection after contact, and rate of recovery after infection), the demography (replacement and culling rates) and the genetic composition (degree of relationship and heritability of the disease trait) of the animal population. To decrease infection pressure, it is possible to apply non-genetic procedures that increase the culling (e.g., culling of chronically infected cows) and recovery (e.g., antibiotic therapy) rates of infected cows. But the contribution of the paper is to show that genetic management of infectious disease is also theoretically possible as a control measure complementary to non-genetic actions. Indeed, the probability for an uninfected individual to become infected after contact with an infected one is partially related to their degree of kinship: the more closely they are related, the more likely they are to share identical genes like those associated to the non-resistance to infection. Different prospective genetic management procedures are proposed to decrease the contact rate between infected and uninfected relatives and keep the number of secondary cases generated by one infected animal below 1.

Key words: infectious disease resistance / heterogeneous SIS model / genetic management / mastitis

Correspondence and reprints: jdetilleux@ulg.ac.be

© INRA, EDP Sciences 2005