Genetic analyses of infectious disease data usually focus on disease resistance, but recent developments point towards additional host traits that may influence the risk and severity of infectious disease outbreaks, namely susceptibility, infectivity and tolerance. Estimating genetic parameters for these traits has proven difficult, because current quantitative genetics methods fail to account for the complex dynamic dependence structure between the traits. In this talk I will present two novel methods for quantifying the host genetic signal underlying infectious disease data. The first method uses a hierarchical Bayesian framework for estimating genetic parameters for host susceptibility and infectivity from epidemiological data. The second method uses tools from mathematical dynamical systems theory to construct and analyse individual health trajectories. I conclude by demonstrating applications of these methods to examples of infectious diseases in livestock


Andrea Doeschl-Wilson

Organisms of agronomic interest e-session

Photos by : Tyssul Patel