The purpose of this thesis is to quantify the effect of selection for disease resistance on the epidemiology of microparasitic infection in pigs, combining quantitative genetics and epidemiology using computer simulation. Two methods of selection are investigated: continuous selection at an arbitrary rate of progress and resistance gene introgression Selection is simulated on a 500 sow farrow-to-finish farm with selection taking place in the sires used to provide semen. A discrete-time model is introduced which expands that of De Jong (1994) allowing reduced susceptibility to infection to be included. The benefits of selection for disease resistance are measured by the effect on the basic reproductive ratio, R<sub>0</sub>, which describes the expected number of secondary cases from a single infected individual. Two functions of R<sub>0</sub>, the maximum and total proportions of pigs infected during simulated epidemics, are used to demonstrate the benefits of the different selection implementations. The model shows that although the reduction, under selection, in R<sub>0</sub> is linear, the reduction in the proportions of infected animals is not. For a highly infectious pathogen, it may take many years of selection before the benefits are seen on the farm. This is not the case for the gene by the time it takes to introgress the resistance alleles. The gene introgression model provides an indication of the proportion of a population that need to be resistant for that population to be protected from epidemics. Unless the pathogen is extremely infectious it is not necessary that the whole population is resistant. For example, when R<sub>0</sub>=5.0 for the population prior to selection, the proportion that need to be resistant to protect the population is 0.82 and when R<sub>0 </sub>= 10.0, the proportion is 0.95.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:654317 |
| Date | January 1999 |
| Creators | MacKenzie, Katrin |
| Publisher | University of Edinburgh |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/1842/12516 |
Page generated in 0.0019 seconds