|Title||Estimating genetic differences in hosts’ susceptibility and infectivity for infectious diseases|
|Author(s)||Biemans, Floor; Bijma, P.; Jong, M.C.M. de|
|Event||ISVEE14, Mérida, 2015-11-03/2015-11-07|
Quantitative Veterinary Epidemiology
Animal Breeding and Genetics
|Publication type||Abstract in scientific journal or proceedings|
|Abstract||Transmission of an infectious disease is affected by susceptibility and infectivity of the host individuals involved. Susceptibility, the relative probability that an individual gets infected when subjected to infectious individuals, is a trait affecting the disease status of the individual itself; it is measured as a direct genetic effect . Infectivity, the ability to infect other individuals, affects the diseases status of others; it is measured as an indirect genetic effect (IGE). An IGE is a heritable effect of an individual affecting the phenotype of another individual. When looking at genetic studies on infectious diseases, current focus is on susceptibility only. Infectivity, however, can have major effects on disease transmission. Identifying highly infective individuals can contribute to preventing disease outbreaks. Here we investigate methods to estimate host genetic effects on susceptibility and infectivity based on binary data on the disease status of individuals.
A simulation study was performed to simulate disease transmission over time. Genetic heterogeneity was modelled in a diploid host population with two unlinked loci, one for susceptibility and one for infectivity. Endemic disease transmission was simulated with a SIS-model. At several points in time the population state was determined by counting the number of susceptible and infectious individuals of each genotype. These data were analysed using a generalized linear model with a complementary log-log link function and relative gene effects for susceptibility and infectivity were estimated back. Genetic differences were estimated correctly for susceptibility and for infectivity when observation intervals were short and/or genetic differences were large. This model can be used in livestock genetic improvement by selecting animals with favourable genetic effects for susceptibility and infectivity. The model has been applied to field data on Digital Dermatitis (DD), an infectious claw disease in dairy cattle. By combining transmission data on DD with high density single-nucleotide polymorphism data of the individual cows, genomic regions affecting susceptibility and infectivity can be identified.