Quantifying Resilience from Individual Feed Intake Data in a Natural Disease Challenge Model for Growing Pigs

• Published in: Journal of animal science 2018-04, Vol.96, p.18-18
• Main Authors: Putz, A M, Harding, J C S, Dyck, M K, Canada, P, tin, F, Plastow, G S, Dekkers, J C M
• Format: Article
• Language: English
• Subjects: Animal sciences; Animals; Correlation analysis; Genetics; Genomes; Heritability; Hogs; Mortality; Physiological effects; Regression analysis; Resilience; Root-mean-square errors; Swine
• ISSN: 0021-8812; 1525-3163

Quantifying resilience in a health challenged environment could be beneficial to add information in commercial crossbred testing systems beyond mortality. Mortality and treatment records can be biased due to the subjective nature of euthanizing or treating individuals. Mortality may also capture other problems (e.g. ruptures) that are not linked to disease resilience. Objective measures would help quantify resilience to disease and other stressors. Feed intake is sensitive to disease due to the physiological effects of illness on feed intake. To study resilience, a natural challenge model was set up in Quebec, Canada. Every three weeks, batches of ~60-75 F1 (Large White x Landrace) healthy weaned barrows were sent to a high health quarantine nursery to take pre-challenge samples for potential predictors of resilience and then sent to the challenge facility after ~3 weeks. From the first 1341 animals, two separate measures of resilience were calculated using individual daily feed intake. The first involved regressing daily feed intake (FI) or duration at the feeder (DUR) on age and extracting the root mean square error (RMSE) within individual (RMSEFI and RMSEDUR, respectively). The second measure was computed as the percentage of negative residuals for an animal from quantile regression of FI on age using the 0.05 quantile across animals (FIQR05), which were classified as sick days. Mortality (0/1) and treatment rate per day times 180 days (TRT180) were used to validate the resilience measures. Heritability estimates for RMSEFI, RMSEDUR, and FIQR05 were 0.22 (±0.07), 0.25 (±0.08), and 0.17 (±0.06), respectively. Heritability estimates for mortality and TRT180 were 0.13 (0.05) and 0.29 (0.07). The genetic correlation between mortality and TRT180 was 0.93 (±0.14). RMSEFI and RMSEDUR had a genetic correlation with mortality of 0.54 (±0.36) and 0.65 (±0.35), respectively. RMSEFI and RMSEDUR had genetic correlations with TRT180 of 0.56 (±0.20) and 0.64 (±0.14), respectively. FIQR05 showed positive genetic correlations of 0.40 (±0.40) and 0.87 (±0.10) with mortality and TRT180, respectively. FIQR05 also showed moderate to strong negative genetic correlations of -0.70 (±0.18) and -0.76 (±0.19) with finishing average daily gain (ADG) and average daily feed intake (ADFI). RMSE measures showed lower genetic correlations with ADG and ADFI (-0.31 ± 0.27 and -0.19 ± 0.26 for RMSEFI and RMSEDUR, respectively). This research demonstrates that resilience measures that are