Estimating the host genetic contribution to the epidemiology of infectious diseases
View/ Open
Lipschutz-Powell2014.docx (3.823Mb)
Date
28/11/2014Author
Lipschutz-Powell, Debby
Metadata
Abstract
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.