title: A Novel Statistical Model to Estimate Host Genetic Effects Affecting Disease Transmission.
abstract: There is increasing recognition that genetic diversity can affect the spread of diseases, potentially affecting plant and livestock disease control as well as the emergence of human disease outbreaks. Nevertheless, even though computational tools can guide the control of infectious diseases, few epidemiological models can simultaneously accommodate the inherent individual heterogeneity in multiple infectious disease traits influencing disease transmission, such as the frequently modeled propensity to become infected and infectivity, which describes the host ability to transmit the infection to susceptible individuals. Furthermore, current quantitative genetic models fail to fully capture the heritable variation in host infectivity, mainly because they cannot accommodate the nonlinear infection dynamics underlying epidemiological data. We present in this article a novel statistical model and an inference method to estimate genetic parameters associated with both host susceptibility and infectivity. Our methodology combines quantitative genetic models of social interactions with stochastic processes to model the random, nonlinear, and dynamic nature of infections and uses adaptive Bayesian computational techniques to estimate the model parameters. Results using simulated epidemic data show that our model can accurately estimate heritabilities and genetic risks not only of susceptibility but also of infectivity, therefore exploring a trait whose heritable variation is currently ignored in disease genetics and can greatly influence the spread of infectious diseases. Our proposed methodology offers potential impacts in areas such as livestock disease control through selective breeding and also in predicting and controlling the emergence of disease outbreaks in human populations.
authors.affiliation: The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Roslin, Midlothian EH25 9PS, United Kingdom osvaldo.anacleto@roslin.ed.ac.uk.
authors.forename: Osvaldo
authors.initials: O
authors.lastname: Anacleto
authors.affiliation: Departamento de Mejora Genética, Instituto Nacional de Investigación Agraria, Ctra. de La Coruña km. 7.5, Madrid 28040, Spain.
authors.forename: Luis Alberto
authors.initials: LA
authors.lastname: Garcia-Cortés
authors.affiliation: Department of Veterinary Medicine, University of Cambridge, Cambridge CB3 0ES, United Kingdom.
authors.forename: Debby
authors.initials: D
authors.lastname: Lipschutz-Powell
authors.affiliation: The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Roslin, Midlothian EH25 9PS, United Kingdom.
authors.forename: John A
authors.initials: JA
authors.lastname: Woolliams
authors.affiliation: The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Roslin, Midlothian EH25 9PS, United Kingdom.
authors.forename: Andrea B
authors.initials: AB
authors.lastname: Doeschl-Wilson
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D003198:Computer Simulation; D004196:Disease Outbreaks / Q000662:veterinary; D018562:Disease Transmission, Infectious; D020022:Genetic Predisposition to Disease; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D020412:Multifactorial Inheritance
keywords: Bayesian statistics; complex traits; disease resistance; infectivity; quantitative genetics