Establishing a Model for Evaluating Chicken Coccidiosis Resistance Based on Principal Component Analysis

Abstract

Simple Summary

Avian coccidiosis, an infectious disease caused by seven species of Eimeria that can infect a bird’s digestive tract and significantly retard its growth, is a serious economic disease for chickens. Many studies have demonstrated that host resistance to coccidiosis related to genetic variations can be improved by selective breeding. The parameters for evaluation of resistance to coccidiosis could be objective indicators, such as body weight gain and cecal lesion score, or biochemical indices, such as immune factors or cytokines in the plasma or serum. The aim of the study is to establish an optimal comprehensive evaluation model including a resistance index that can be detected in live chickens (slaughter traits cannot be selected in breeding) based on principal component analysis. The value of individual chickens calculated with the optimal evaluation model is associated with the cecum lesion score; the larger the value, the stronger the resistance to coccidiosis. This illustrated that the optimal model is effective in coccidiosis resistance selection.

Abstract

To establish a coccidiosis resistance evaluation model for chicken selection, the different parameters were compared between infected and control Jinghai yellow chickens. Validation parameters were selected for principal component analysis (PCA), and an optimal comprehensive evaluation model was selected based on the significance of a correlation coefficient between coccidiosis resistance parameters and principal component functions. The following six different parameters were identified: body weight gain 3–5 days post infection and catalase (CAT), superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), malondialdehyde (MDA) and γ-interferon (IFN-γ) concentrations on the eight day post inoculation. Six principal components and one accumulated contribution of up to 80% of the evaluation models were established by PCA. The results showed that the first model was significantly or highly significantly related to nine resistance parameters ( p < 0.01 or p < 0.05), especially to cecal lesions ( p < 0.01). The remaining models were related to only 2–3 parameters ( p < 0.01 or p < 0.05) and not to cecal lesions ( p > 0.05). The values calculated by the optimal model (first model) were significantly negatively correlated with cecal lesion performance; the larger the value, the more resistant to coccidiosis. The model fi1 = −0.636 zxi1 + 0.311 zxi2 + 0.801 zxi3 − 0.046 zxi4 − 0.076 zxi5 + 0.588 zxi6 might be the best comprehensive selection index model for chicken coccidiosis resistance selection.