Quantification of differences in resistance to gastrointestinal nematode infections in sheep using a multivariate...

Breeding for resistance to gastrointestinal nematodes (GIN) in sheep relies largely on the use of worm egg counts (WEC) to identify animals that are able to resist infection. As an alternative to such measures of parasite load we aimed to develop a method to identify animals showing resistance to GIN infection based on the impact of the infection on blood parameters. We hypothesized that blood parameters may provide a measure of infection level with a blood-feeding parasite through perturbation of red blood cell parameters due to feeding behaviour of the parasite, and white blood cell parameters through the mounting of an immune response in the host animal. We measured a set of blood parameters in 390 sheep that had been exposed to an artificial regime of repeated challenges with Trichostrongylus colubriformis followed by Haemonchus contortus. A simple analysis revealed strong relationships between single blood parameters and WECs with correlation coefficients -0.54 to -0.60. We then used more complex multi-variate methods based on supervised classifier models (including Bayesian Network) as well as regression models (Lasso and Elastic Net) to study the relationships between WECs and blood parameters, and derived algorithms describing the relationships. The ability of these algorithms to classify sheep GIN resistance status was tested using the WEC and blood parameters collected from a different group of 418 sheep that had acquired natural infections of H. contortus from pasture. We identified the most resistant and most susceptible animals (10% percentiles) of this group based on WECs, and then compared the identities of these animals to the identities of animals that were predicted to be most resistant and most susceptible by our algorithms. The models showed varying abilities to predict susceptible and resistant sheep, with up to 65% of the most susceptible animals and 30% of the most resistant animals identified by the Elastic Net model algorithms. The prediction algorithms derived from female sheep data performed better than those for male sheep in some cases, with the predicted animals accounting for up to 50-60% of the actual resistant and susceptible female animals. Heritability values were calculated for blood parameters and the aggregate trait descriptions defined by the novel prediction algorithms. The aggregate trait descriptions were moderately heritable and may therefore be suitable for use in genetic selection strategies. The present study indicates that multivariate models based on blood parameter data showed some ability to predict the resistance status of sheep to infection with H. contortus.