A Bayesian Modeling Framework to Integrate Genetics and Epidemiology in Field Disease Data

Abstract Text:

Data on disease occurrences at field level are valuable resources for quantifying host genetic variation in disease resistance. However, they are often inaccurate due to incomplete information describing exposure, disease prevalence and imperfect diagnostic tests. Quantitative genetic models of disease occurrence data do not typically account for these factors leading to underestimation of the true extent of genetic variation. We propose a framework that integrates genetics and epidemiology including genetic relationships between animals, observed disease state, prevalence of the disease and sensitivity and specificity of diagnostic tests. Bayesian inference allows quantification of host genetic variation accounting for the complexities inherent in field disease data. Prior information, as elicited by expert opinion, is incorporated. Application to simulated data shows this novel approach provides reliable inferences on genetic and epidemiological parameters that are of practical relevance to animal breeders.

Keywords:

disease genetics

disease diagnosis

Markov chain Monte Carlo (MCMC)

heritability