Uses and Implications of Field Disease Data for Livestock Genomic and Genetics Studies

This paper identifies issues associated with field disease data and their implications on the interpretation of estimated genetic parameters and experimental designs. The main focus is on concepts relating to the impacts of diagnostic test properties and exposure to infection, and how exposure to infection is intricately related to within-herd epidemic dynamics. The following are raised challenges: (i) to more fully understand and describe the dynamic impacts of disease epidemics on genetic interpretations; (ii) to develop statistical methods to jointly estimate epidemiological and genetic parameters from complex epidemiological data; (iii) to develop and explore optimal experimental designs for case-control studies, exploiting field disease data. Solving these problems would add insight to both disease genetic and epidemiological studies, as well as enabling us to better select animals for increased disease resistance

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

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 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

Nitrogen excretion at different stages of growth and its association with production traits in growing pigs

The objectives of this study were to determine nitrogen loss at different stages of growth and during the entire growing period and to investigate the associations between nitrogen excretion and production traits in growing pigs. Data from 315 pigs of an F-2 population which originated from crossing Pietrain sires with a commercial dam line were used. Nitrogen retention was derived from protein retention as measured using the deuterium dilution technique during different stages of growth (60 to 90 kg, 90 to 120 kg, and 120 to 140 kg). Pigs were fed ad libitum with 2 pelleted diets containing 17% (60 to 90 kg) and 16.5% (90 to 120 and 120 to 140 kg) CP. Average daily nitrogen excretion (ADNE) within each stage of growth was calculated on the basis of the accumulated difference between average daily nitrogen intake (ADNI) and average daily nitrogen retention (ADNR). Least ADNE, nitrogen excretion per BW gain (NEWG) and total nitrogen excretion (TNE) were observed during growth from 60 to 90 kg. In contrast, the greatest ADNE, NEWG, and TNE were found during growth from 120 to 140 kg. Statistical analyses indicated that gender, housing type, the ryanodine receptor 1 (RYR1) gene, and batch influenced nitrogen excretion (P <0.05), but the degree and direction of influences differed between growth stages. Gender differences showed that gilts excreted less nitrogen than barrows (P <0.05), which was associated with decreased feed conversion ratio (FCR; feed: gain) and lipid: protein gain ratio. Single-housed pigs showed reduced nitrogen excretion compared with group-housed pigs (P <0.05). In comparison to other genotypes, pigs carrying genotype NN (homozygous normal) at the RYR1 locus had the least nitrogen excretion (P <0.05) at all stages of growth except from 60 to 90 kg. The residual correlations indicated that NEWG and TNE have large positive correlations with FCR (r = 0.99 and 0.91, respectively) and moderate negative correlations with ADG (r = -0.53 and -0.48, respectively), for the entire growing period. Improvement in FCR, increase in ADG and reduction in lipid: protein gain ratio by 1 phenotypic SD reduced TNE per pig by 709 g, 307 g, and 211 g, respectively, over the entire growing period. The results indicate that nitrogen excretion changes substantially during growth, and it can be reduced most effectively by improvement of feed efficiency and to a lesser extent through the improvement of BW gain or body composition or both

Health trajectories reveal the dynamic contributions of host genetic resistance and tolerance to infection outcome

Resistance and tolerance are two alternative strategies hosts can adopt to survive infections. Both strategies may be genetically controlled. To date, the relative contribution of resistance and tolerance to infection outcome is poorly understood. Here, we use a bioluminescent Listeria monocytogenes (Lm) infection challenge model to study the genetic determination and dynamic contributions of host resistance and tolerance to listeriosis in four genetically diverse mouse strains. Using conventional statistical analyses, we detect significant genetic variation in both resistance and tolerance, but cannot capture the time-dependent relative importance of either host strategy. We overcome these limitations through the development of novel statistical tools to analyse individual infection trajectories portraying simultaneous changes in infection severity and health. Based on these tools, early expression of resistance followed by expression of tolerance emerge as important hallmarks for surviving Lm infections. Our trajectory analysis further reveals that survivors and non-survivors follow distinct infection paths (which are also genetically determined) and provides new survival thresholds as objective endpoints in infection experiments. Future studies may use trajectories as novel traits for mapping and identifying genes that control infection dynamics and outcome. A Matlab script for user-friendly trajectory analysis is provided