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New Methods for capturing unidentified genetic Variation underlying Infectious Disease in Livestock Populations

Monday, August 18, 2014: 5:00 PM
Bayshore Grand Ballroom E-F (The Westin Bayshore)
Andrea Doeschl-Wilson , The Roslin Institute and R(D)SVS, University of Edinburgh, Midlothian, United Kingdom
Debby Lipschutz-Powell , The Roslin Institute and R(D)SVS, University of Edinburgh, Midlothian, United Kingdom
Osvaldo Anacleto , The Roslin Institute and R(D)SVS, University of Edinburgh, Midlothian, United Kingdom
Luis Alberto García-Cortés , SGIT - INIA, Ministerio de Ciencia e Innovación, Madrid, Spain
Graham Lough , The Roslin Institute and R(D)SVS, University of Edinburgh, Midlothian, United Kingdom
Andreas Lengeling , The Roslin Institute and R(D)SVS, University of Edinburgh, Midlothian, United Kingdom
Silke Bergmann , Helmholtz Centre for Infection Research, Braunschweig, Germany
John A. Woolliams , The Roslin Institute and R(D)SVS, University of Edinburgh, Midlothian, United Kingdom
Abstract Text:

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.      

Keywords:

Infectious disease

Resistance

Infectivity

Tolerance

Bayesian statistics

Trajectory