Reducing disease prevalence through selection for host resistance offers a desirable alternative to chemical treatment which is a potential environmental concern due to run-off, and sometimes only offers limited protection due to pathogen resistance for example (Chen et al., 2010). Genetic analyses require large sample sizes and hence disease phenotypes often need to be obtained from field data. Disease data from field studies is often binary, indicating whether an individual became infected or not following exposure to infectious pathogens. In genetic analyses of binary disease data, however, exposure is often considered as an environmental constant and thus potential variation in host infectivity is ignored. Host infectivity is the propensity of an infected individual to infect others. The lack of attention to genetic variation in infectivity stands in contrast to its important role in epidemiology. The theory of indirect genetic effects (IGE), also known as associative or social genetic effects, provides a promising framework to account for genetic variation in infectivity as it investigates heritable effects of an individual on the trait value of another individual. Chapter 2 examines to what extent genetic variance in infectivity/susceptibility is captured by a conventional model versus an IGE model. The results show that, unlike a conventional model, which does not capture the variation in infectivity when it is present in the data, a model which takes IGEs into account captures some, though not all, of the inherent genetic variation in infectivity. The results also show that genetic evaluations that incorporate variation in infectivity can increase response to selection and reduce future disease risk. However, the results of this study also reveal severe shortcomings in using the standard IGE model to estimate genetic variance in infectivity caused by ignoring dynamic aspects of disease transmission. Chapter 3 explores to what extent the standard IGE model could be adapted for use with binary infectious disease data taking account of dynamic properties within the remit of a conventional quantitative genetics mixed model framework and software. The effect of including disease dynamics in this way was assessed by comparing the accuracy, bias and impact for estimates obtained for simulated binary disease data with two such adjusted IGE models, with the Standard IGE model. In the first adjusted model, the Case model, it was assumed that only infected individuals have an indirect effect on their group mates. In the second adjusted IGE model, the Case-ordered model, it was assumed that only infected individuals exert an indirect effect on susceptible group mates only. The results show that taking the disease status of individuals into account, by using the Case model, considerably improves the bias, accuracy and impact of genetic infectivity estimates from binary disease data compared to the Standard IGE model. However, although heuristically one would assume that the Case-ordered model would provide the best estimates, as it takes the disease dynamics into account, in fact it provides the worst. Moreover, the results suggest that further improvements would be necessary in order to achieve sufficiently reliable infectivity estimates, and point to inadequacy of the statistical model. In order to derive an appropriate relationship between the observed binary disease trait and underlying susceptibility and infectivity, epidemiological theory was combined with quantitative genetics theory to expand the existing framework in Chapter 4. This involved the derivation of a genetic-epidemiological function which takes dynamic expression of susceptibility and infectivity into account. When used to predict the outcome of simulated data it proved to be a good fit for the probability of an individual to become infected given its own susceptibility and the infectivity of its group mates. Using the derived function it was demonstrated that the use of a linear IGE model would result in biased estimates of susceptibility and infectivity as observed in Chapters 2 & 3. Following the results of Chapter 4, the derived expression was used to develop a Markov Chain Monte Carlo (MCMC) algorithm in order to estimate breeding values in susceptibility and infectivity in Chapter 5. The MCMC algorithm was evaluated with simulated disease data. Prior to implementing this algorithm with real disease data an adequate experimental design must be determined. The results suggest that there is a trade-off for the ability to estimate susceptibility and infectivity with regards to group size; this is in line with findings for IGE models. A possible compromise would be to place relatives in both larger and smaller groups. The general discussion addresses such questions regarding experimental design and possible areas for improvement of the algorithm. In conclusion, the thesis advances and develops a novel approach to the analysis of binary infectious disease data, which makes it possible to capture genetic variation in both host susceptibility and infectivity. This approach has been refined to make those estimates increasingly accurate. These breeding values will provide novel opportunities for genome wide association studies and may lead to novel genetic disease control strategies tackling not only host resistance but also the ability to transmit infectious agents.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:764159 |
| Date | January 2014 |
| Creators | Lipschutz-Powell, Debby |
| Contributors | Woolliams, John ; Wilson, Andrea |
| Publisher | University of Edinburgh |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/1842/10037 |
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