Genetic analyses of infectious disease data usually focus on disease resistance, but recent developments point towards additional traits that may influence animal health and performance, 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. Here we propose two methods for incorporating infection dynamics into genetic analyses. 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 trajectory sequences representing resistance-tolerance co-expression patterns. Applying the methods to simulated and real data, respectively, shows that it is possible to determine the genetic footprint underlying infectious disease dynamics in livestock populations if appropriate data exist.
Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume Methods and Tools: Statistical methods - linear and nonlinear models, , 203, 2014
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