Estimating the host genetic contribution to the epidemiology of infectious diseases

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

Indirect Genetic Effects and the Spread of Infectious Disease: Are We Capturing the Full Heritable Variation Underlying Disease Prevalence?

Reducing disease prevalence through selection for host resistance offers a desirable alternative to chemical treatment. Selection for host resistance has proven difficult, however, due to low heritability estimates. These low estimates may be caused by a failure to capture all the relevant genetic variance in disease resistance, as genetic analysis currently is not taylored to estimate genetic variation in infectivity. Host infectivity is the propensity of transmitting infection upon contact with a susceptible individual, and can be regarded as an indirect effect to disease status. It may be caused by a combination of physiological and behavioural traits. Though genetic variation in infectivity is difficult to measure directly, Indirect Genetic Effect (IGE) models, also referred to as associative effects or social interaction models, allow the estimation of this variance from more readily available binary disease data (infected/non-infected). We therefore generated binary disease data from simulated populations with known amounts of variation in susceptibility and infectivity to test the adequacy of traditional and IGE models. Our results show that a conventional model fails to capture the genetic variation in infectivity inherent in populations with simulated infectivity. An IGE model, on the other hand, does capture some of the variation in infectivity. Comparison with expected genetic variance suggests that there is scope for further methodological improvement, and that potential responses to selection may be greater than values presented here. Nonetheless, selection using an index of estimated direct and indirect breeding values was shown to have a greater genetic selection differential and reduced future disease risk than traditional selection for resistance only. These findings suggest that if genetic variation in infectivity substantially contributes to disease transmission, then breeding designs which explicitly incorporate IGEs might help reduce disease prevalence

A unifying theory for genetic epidemiological analysis of binary disease data

BACKGROUND: Genetic selection for host resistance offers a desirable complement to chemical treatment to control infectious disease in livestock. Quantitative genetics disease data frequently originate from field studies and are often binary. However, current methods to analyse binary disease data fail to take infection dynamics into account. Moreover, genetic analyses tend to focus on host susceptibility, ignoring potential variation in infectiousness, i.e. the ability of a host to transmit the infection. This stands in contrast to epidemiological studies, which reveal that variation in infectiousness plays an important role in the progression and severity of epidemics. In this study, we aim at filling this gap by deriving an expression for the probability of becoming infected that incorporates infection dynamics and is an explicit function of both host susceptibility and infectiousness. We then validate this expression according to epidemiological theory and by simulating epidemiological scenarios, and explore implications of integrating this expression into genetic analyses. RESULTS: Our simulations show that the derived expression is valid for a range of stochastic genetic-epidemiological scenarios. In the particular case of variation in susceptibility only, the expression can be incorporated into conventional quantitative genetic analyses using a complementary log-log link function (rather than probit or logit). Similarly, if there is moderate variation in both susceptibility and infectiousness, it is possible to use a logarithmic link function, combined with an indirect genetic effects model. However, in the presence of highly infectious individuals, i.e. super-spreaders, the use of any model that is linear in susceptibility and infectiousness causes biased estimates. Thus, in order to identify super-spreaders, novel analytical methods using our derived expression are required. CONCLUSIONS: We have derived a genetic-epidemiological function for quantitative genetic analyses of binary infectious disease data, which, unlike current approaches, takes infection dynamics into account and allows for variation in host susceptibility and infectiousness

Risk factors and variations in detection of new bovine tuberculosis breakdowns via slaughterhouse surveillance in Great Britain.

Slaughterhouse surveillance through post-mortem meat inspection provides an important mechanism for detecting bovine tuberculosis (bTB) infections in cattle herds in Great Britain (GB), complementary to the live animal skin test based programme. We explore patterns in the numbers of herd breakdowns detected through slaughterhouse surveillance and develop a Bayesian hierarchical regression model to assess the associations of animal-level factors with the odds of an infected animal being detected in the slaughterhouse, allowing us to highlight slaughterhouses that show atypical patterns of detection. The analyses demonstrate that the numbers and proportions of breakdowns detected in slaughterhouses increased in GB over the period of study (1998-2013). The odds of an animal being a slaughterhouse case was strongly associated with the region of the country that the animal spent most of its life, with animals living in high-frequency testing areas of England having on average 21 times the odds of detection compared to animals living in Scotland. There was also a strong effect of age, with animals slaughtered at > 60 months of age having 5.3 times the odds of detection compared to animals slaughtered between 0-18 months of age. Smaller effects were observed for cattle having spent time on farms with a history of bTB, quarter of the year that the animal was slaughtered, movement and test history. Over-and-above these risks, the odds of detection increased by a factor of 1.1 for each year of the study. After adjustment for these variables, there were additional variations in risk between slaughterhouses and breed. Our framework has been adopted into the routine annual surveillance reporting carried out by the Animal Plant Health Agency and may be used to target more detailed investigation of meat inspection practices.Defra Project SE3133. (Department of Environment and Rural Affairs, UK Government

Bias, accuracy, and impact of indirect genetic effects in infectious diseases

Selection for improved host response to infectious disease offers a desirable alternative to chemical treatment but has proven difficult in practice, due to low heritability estimates of disease traits. Disease data from field studies is often binary, indicating whether an individual has become infected or not following exposure to an infectious disease. Numerous studies have shown that from this data one can infer genetic variation in individuals’ underlying susceptibility. In a previous study, we showed that with an indirect genetic effect (IGE) model it is possible to capture some genetic variation in infectivity, if present, as well as in susceptibility. Infectivity is the propensity of transmitting infection upon contact with a susceptible individual. It is an important factor determining the severity of an epidemic. However, there are severe shortcomings with the Standard IGE models as they do not accommodate the dynamic nature of disease data. Here we adjust the Standard IGE model to (1) make expression of infectivity dependent on the individuals’ disease status (Case Model) and (2) to include timing of infection (Case-ordered Model). The models are evaluated by comparing impact of selection, bias, and accuracy of each model using simulated binary disease data. These were generated for populations with known variation in susceptibility and infectivity thus allowing comparisons between estimated and true breeding values. Overall the Case Model provided better estimates for host genetic susceptibility and infectivity compared to the Standard Model in terms of bias, impact, and accuracy. Furthermore, these estimates were strongly influenced by epidemiological characteristics. However, surprisingly, the Case-Ordered model performed considerably worse than the Standard and the Case Models, pointing toward limitations in incorporating disease dynamics into conventional variance component estimation methodology and software used in animal breeding

A Comparison of Expected and Observed Variance Components for the Skewed ‘Multiple Alleles’ and ‘Two Alleles’ Architectures When Genetic Variance Is Introduced INTO Infectivity, or Susceptibility, or Both.

<p>Observed components are taken from results of analyses of data with either a conventional model (Eqn 2) or IGE model (Eqn 3), whilst expected components are obtained from the true simulated values and Eqn 5. ‘#’ means not significantly different from zero (P>0.05), values scaled by 10³.</p

Estimated Genetic Variance in Disease Presence (Binary), in Populations with a Skewed Bi-Allelic Genetic Architecture Underlying Susceptibility/Infectivity, Using the Indirect Genetic Effects Model.

<p>Values scaled by 10³, ‘#’ means not significantly different from zero (P>0.05). Values along the rows are directly comparable to each other where mean presence is the same. Estimates averaged over ten iterations. Parameter values as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0039551#pone-0039551-t002" target="_blank">Table 2</a>, 10000 groups of size 10. The log-likelihood P-value refers to the significance of the indirect genetic effect.</p

Estimated Genetic Variance in Disease Presence (Binary) Using a Conventional Animal Model.

<p>All parameters as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0039551#pone-0039551-t002" target="_blank">table 2</a>. 10000 groups of size 10, ‘#’ means not significantly different from zero (P>0.05), values scaled by 10³.</p

Parameters for Breeding Values Generation.

<p>MAF applied to the alleles with a large effect (F2, G2).</p

Estimated Genetic Variance in Disease Presence (Binary), in Populations with a Skewed Multiple Alleles Genetic Architecture Underlying Susceptibility/Infectivity, Using the Indirect Genetic Effects Model.

<p>Values scaled by10³, ‘#’ means not significantly different from zero (P>0.05). Values along the rows are directly comparable to each other where mean presence is the same. Estimates averaged over ten replicates. Parameters as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0039551#pone-0039551-t002" target="_blank">Table 2</a>, 10000 groups of size 10. The log-likelihood P-value refers to the significance of the indirect genetic effect.</p