Abstract
BackgroundGenetic selection of livestock against infectious diseases can complement existing interventions to control infectious diseases. Most genetic approaches that aim at reducing disease prevalence assume that individual disease status (infected/not-infected) is solely a function of its susceptibility to a particular pathogen. However, individual infectivity also affects the risk and prevalence of an infection in a population. Variation in susceptibility and infectivity between hosts affects transmission of an infection in the population, which is usually measured by the value of the basic reproduction ratio R0. R0 is an important epidemiological parameter that determines the risk and prevalence of infectious diseases. An individual’s breeding value for R0 is a function of its genes that influence both susceptibility and infectivity. Thus, to estimate the effects of genes on R0, we need to estimate the effects of genes on individual susceptibility and infectivity. To that end, we developed a generalized linear model (GLM) to estimate relative effects of genes for susceptibility and infectivity. A simulation was performed to investigate bias and precision of the estimates, the effect of R0, the size of the effects of genes for susceptibility and infectivity, and relatedness among group mates on bias and precision. We considered two bi-allelic loci that affect, respectively, the individuals’ susceptibility only and individuals’ infectivity only.ResultsA GLM with complementary log–log link function can be used to estimate the relative effects of genes on the individual’s susceptibility and infectivity. The model was developed from an equation that describes the probability of an individual to become infected as a function of its own susceptibility genotype and infectivity genotypes of all its infected group mates. Results show that bias is smaller when R0 ranges approximately from 1.8 to 3.1 and relatedness among group mates is higher. With larger effects, both absolute and relative standard deviations become clearly smaller, but the relative bias remains the same.ConclusionsWe developed a GLM to estimate the relative effect of genes that affect individual susceptibility and infectivity. This model can be used in genome-wide association studies that aim at identifying genes that influence the prevalence of infectious diseases.
Highlights
Genetic selection of livestock against infectious diseases can complement existing interventions to control infectious diseases
The classical quantitative genetic approach of disease analysis based on individual disease status will capture only part of the heritable variation that is present in the host population and affects the dynamics of infectious diseases [7]
The black straight line in all figures represents the true difference between γg and γG and between φf and φF, and the bars indicate the standard deviation of these estimates among replicates
Summary
Genetic selection of livestock against infectious diseases can complement existing interventions to control infectious diseases. Most genetic approaches that aim at reducing the prevalence of an infection assume that an individual’s disease status (infected/not-infected) is solely a function of its own genes and of non-genetic factors [2]. These methods capture only the genetic variation in susceptibility or tolerance (strictly, this latter statement is restricted to the measurement of disease occurrence in groups of unrelated individuals [5]). The classical quantitative genetic approach of disease analysis based on individual disease status will capture only part of the heritable variation that is present in the host population and affects the dynamics of infectious diseases [7]
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