title: Genetic analysis of infectious diseases: estimating gene effects for susceptibility and infectivity.
abstract: BACKGROUND
  Genetic 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 R 0 . R 0 is an important epidemiological parameter that determines the risk and prevalence of infectious diseases. An individual's breeding value for R 0 is a function of its genes that influence both susceptibility and infectivity. Thus, to estimate the effects of genes on R 0 , 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 R 0 , 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.
  
  
  RESULTS
  A 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 R 0 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.
  
  
  CONCLUSIONS
  We 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.
authors.affiliation: Animal Breeding and Genomics Centre, Wageningen University, 6700 AH, Wageningen, The Netherlands. mahlet.anche@wur.nl.|Quantitative Veterinary Epidemiology Group, Wageningen University, 6700 AH, Wageningen, The Netherlands. mahlet.anche@wur.nl.
authors.forename: Mahlet T
authors.initials: MT
authors.lastname: Anche
authors.affiliation: Animal Breeding and Genomics Centre, Wageningen University, 6700 AH, Wageningen, The Netherlands. piter.bijma@wur.nl.
authors.forename: P
authors.initials: P
authors.lastname: Bijma
authors.affiliation: Quantitative Veterinary Epidemiology Group, Wageningen University, 6700 AH, Wageningen, The Netherlands. mart.dejong@wur.nl.
authors.forename: Mart C M
authors.initials: MC
authors.lastname: De Jong
mesh_terms: D000465:Algorithms; D000818:Animals; D003141:Communicable Diseases / Q000453:epidemiology / Q000235:genetics*; D003198:Computer Simulation; D018740:Genetic Heterogeneity; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study / Q000379:methods; D054884:Host-Pathogen Interactions; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D012641:Selection, Genetic