Abstract
The purpose of this study was to investigate the origin of the genetic variation in the prevalence of bovine digital dermatitis (DD) by comparing a genetic analysis of infection events to a genetic analysis of disease status. DD is an important endemic infectious disease affecting the claws of cattle. For disease status, we analysed binary data on individual disease status (0,1; indicating being free versus infected), whereas for infections, we analysed binary data on disease transmission events (1,0; indicating becoming infected or not). The analyses of the two traits were compared using cross‐validation. The analysis of disease status captures a combination of genetic variation in disease susceptibility and the ability of individuals to recover, whereas the analysis of infections captures genetic variation in susceptibility only. Estimated genetic variances for both traits indicated substantial genetic variation. The GEBV for disease status and infections correlated with only 0.60, indicating that both models indeed capture distinct information. Together, these results suggest the presence of genetic variation not only in disease susceptibility, but also in the ability of individuals to recover from DD. We argue that the presence of genetic variation in recovery implies that breeders should distinguish between infected individuals versus infectious individuals. This is because epidemiological theory shows that selection for recovery is effective only when it targets recovery from being infectious.
Highlights
Infectious diseases reduce the productivity of farm animals, and cause considerable losses related to disease control and cure measures (Ifende et al, 2014)
Heritability was estimated to be 0.16. This value is difficult to compare with literature results, since we are not aware of any other estimates for the heritability of susceptibility based on a infection model (IM) with a complementary log-log link function
The above comparison of the Disease status model (DSM) and the IM shows that the DSM captures both the disease susceptibility of an individual and its ability to recover from being infected, whereas the IM captures susceptibility
Summary
Infectious diseases reduce the productivity of farm animals, and cause considerable losses related to disease control and cure measures (Ifende et al, 2014). To disease control measures such as vaccination and treatment, genetic selection of the host population can be used to combat infectious diseases in livestock (Davies et al, 2009; Deb et al, 2012; Jovanović et al, 2009). In addition to measures such as vaccination and treatment, genetic selection is an important tool to combat infectious diseases in livestock (and plants). Disease status data are often analysed using simple linear mixed models, where the binary record of the individual is linearly related to its breeding value. Generalized linear mixed models, such as threshold models, are used and are statistically much more appropriate (Gianola, 1982), but benefits over the simpler linear models are often found to be small in practice especially without adequate Bayesian priors (Hadfield & Nakagawa, 2010; Sorensen & Gianola, 2007)
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