The Contribution of Genetic Diversity to the Spread of Infectious Diseases in Livestock Populations

This article uses stochastic simulations with a compartmental epidemic model to quantify the impact of genetic diversity within animal populations on the transmission of infectious disease. Genetic diversity is defined by the number of distinct genotypes in the population conferring resistance to microparasitic (e.g., viral or bacterial) infections. Scenarios include homogeneous populations and populations composed of few (finite-locus model) or many (infinitesimal model) genotypes. Genetic heterogeneity has no impact upon the expected value of the basic reproductive ratio (the primary description of the transmission of infection) but affects the variability of this parameter. Consequently, increasing genetic heterogeneity is associated with an increased probability of minor epidemics and decreased probabilities of both major (catastrophic) epidemics and no epidemics. Additionally, heterogeneity per se is associated with a breakdown in the expected relationship between the basic reproductive ratio and epidemic severity, which has been developed for homogeneous populations, with increasing heterogeneity generally resulting in fewer infected animals than expected. Furthermore, increased heterogeneity is associated with decreased disease-dependent mortality in major epidemics and a complex trend toward decreased duration of these epidemics. In summary, more heterogeneous populations are not expected to suffer fewer epidemics on average, but are less likely to suffer catastrophic epidemics.

Estimation of additive, dominance and epistatic variance components using finite locus models implemented with a single-site Gibbs and a descent graph sampler

In a previous contribution, we implemented a finite locus model (FLM) for estimating additive and dominance genetic variances via a Bayesian method and a single-site Gibbs sampler. We observed a dependency of dominance variance estimates on locus number in the analysis FLM. Here, we extended the FLM to include two-locus epistasis, and implemented the analysis with two genotype samplers (Gibbs and descent graph) and three different priors for genetic effects (uniform and variable across loci, uniform and constant across loci, and normal). Phenotypic data were simulated for two pedigrees with 6300 and 12300 individuals in closed populations, using several different, non-additive genetic models. Replications of these data were analysed with FLMs differing in the number of loci. Simulation results indicate that the dependency of non-additive genetic variance estimates on locus number persisted in all implementation strategies we investigated. However, this dependency was considerably diminished with normal priors for genetic effects as compared with uniform priors (constant or variable across loci). Descent graph sampling of genotypes modestly improved variance components estimation compared with Gibbs sampling. Moreover, a larger pedigree produced considerably better variance components estimation, suggesting this dependency might originate from data insufficiency. As the FLM represents an appealing alternative to the infinitesimal model for genetic parameter estimation and for inclusion of polygenic background variation in QTL mapping analyses, further improvements are warranted and might be achieved via improvement of the sampler or treatment of the number of loci as an unknown.