Transmission of infectious diseases is determined by susceptibility and infectivity of the individuals involved. An individual’s genes for susceptibility affect the disease status of the individual itself, and thus represent a direct genetic effect. An individual’s genes for infectivity, on the other hand, affect the disease status of other individuals, and thus represent a so-called indirect genetic effect (IGE). An IGE is a genetic effect of an individual on the phenotype of another individual. IGEs have been studied extensively in evolutionary biology, and can have profound effects on the rate and direction of evolution by natural selection. In genetic studies on infectious diseases, the current focus is largely on susceptibility, whereas genetics of infectivity can have major effects on disease transmission. However, little is known about the genetic background of infectivity. We show how genetic effects on susceptibility and infectivity can be estimated simultaneously from time-series data on disease status of individuals. An endemic disease was simulated, and the disease status (0/1) and genotype of individuals were recorded at several points in time. These data were analysed using a generalized linear model (GLM) with a complementary log-log link function. The model included two genetic terms: i) the genotype of the focal individual, representing susceptibility, and ii) the average genotype of its infected social partners (contacts), representing infectivity. First results showed that estimated genetic effects were almost unbiased. This work, therefore, provides a tool for genome-wide association studies aiming to identify genomic regions affecting susceptibility and infectivity of individuals to endemic diseases.
|Publication status||Published - 29 Aug 2014|
|Event||IGES 2014 (International Genetic Epidemiology Society) - Vienna, Austria|
Duration: 28 Aug 2014 → 30 Aug 2014
|Conference||IGES 2014 (International Genetic Epidemiology Society)|
|Period||28/08/14 → 30/08/14|