SIRE 2.0: a novel method for estimating polygenic host effects underlying infectious disease transmission, and analytical expressions for prediction accuracies.

BACKGROUND

Genetic selection of individuals that are less susceptible to infection, less infectious once infected, and recover faster, offers an effective and long-lasting solution to reduce the incidence and impact of infectious diseases in farmed animals. However, computational methods for simultaneously estimating genetic parameters for host susceptibility, infectivity and recoverability from real-word data have been lacking. Our previously developed methodology and software tool SIRE 1.0 (Susceptibility, Infectivity and Recoverability Estimator) allows estimation of host genetic effects of a single nucleotide polymorphism (SNP), or other fixed effects (e.g. breed, vaccination status), for these three host traits using individual disease data typically available from field studies and challenge experiments. SIRE 1.0, however, lacks the capability to estimate genetic parameters for these traits in the likely case of underlying polygenic control.

RESULTS

This paper introduces novel Bayesian methodology and a new software tool SIRE 2.0 for estimating polygenic contributions (i.e. variance components and additive genetic effects) for host susceptibility, infectivity and recoverability from temporal epidemic data, assuming that pedigree or genomic relationships are known. Analytical expressions for prediction accuracies (PAs) for these traits are derived for simplified scenarios, revealing their dependence on genetic and phenotypic variances, and the distribution of related individuals within and between contact groups. PAs for infectivity are found to be critically dependent on the size of contact groups. Validation of the methodology with data from simulated epidemics demonstrates good agreement between numerically generated PAs and analytical predictions. Genetic correlations between infectivity and other traits substantially increase trait PAs. Incomplete data (e.g. time censored or infrequent sampling) generally yield only small reductions in PAs, except for when infection times are completely unknown, which results in a substantial reduction.

CONCLUSIONS

The method presented can estimate genetic parameters for host susceptibility, infectivity and recoverability from individual disease records. The freely available SIRE 2.0 software provides a valuable extension to SIRE 1.0 for estimating host polygenic effects underlying infectious disease transmission. This tool will open up new possibilities for analysis and quantification of genetic determinates of disease dynamics.