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Title: Combining genetics and epidemiology : a model of footrot in sheep
Author: Russell, Vinca N. L.
ISNI:       0000 0004 2749 5488
Awarding Body: University of Warwick
Current Institution: University of Warwick
Date of Award: 2013
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The interaction between host genetics and epidemiological processes in ovine footrot was investigated using a combination of data analysis and simulation modelling. The study’s aims were to determine the potential for genetic selection to be used to reduce the prevalence of footrot in the UK and to assess different strategies for use of conventional epidemiological interventions. A stochastic simulation model was developed, incorporating host genetics for traits controlling footrot resistance, bacterial population dynamics, sheep population dynamics and epidemiological processes. Sensitivity analysis of the model showed survival time of Dichelobacter nodosus in the environment and infection rate were the key determinants of disease outcomes. Antibiotics were predicted to be the most effective conventional control method, reducing prevalence of footrot to 1-2% when administered promptly. Pasture rotation, selective culling and vaccination were all predicted to reduce prevalence but to a lower extent. Analysis of field data confirmed the likely role for some degree of host genetic control of footrot resistance, i.e. resistance appears to be lowly to moderately heritable. Using the simulation model it was then shown that genetic selection could be effective at reducing footrot prevalence. In combination with antibiotic treatment or pasture rotation elimination of footrot from an individual flock could be achieved. Genetic selection was predicted to be effective at reducing prevalence and improving resistance but the choice of selection criteria impacts the results seen. It is likely that progress would be slower in field situations because footrot traits would be diluted by simultaneous selection for other traits affecting profitability. Field studies are required to determine optimal combinations of interventions and genetic selection and to validate modelling outcomes. Combined data from longitudinal disease observations, genetic information and bacterial samples are necessary to address current knowledge gaps and to further advance understanding of host and disease processes in ovine footrot.
Supervisor: Not available Sponsor: Biotechnology and Biological Sciences Research Council (Great Britain) (BBSRC) ; Pfizer Inc.
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: QR180 Immunology ; SF Animal culture