Variation among sows in response to porcine reproductive and respiratory syndrome1

Abstract

Porcine reproductive and respiratory syndrome (PRRS) is a viral disease with negative impacts on reproduction of sows. Genetic selection to improve the response of sows to PRRS could be an approach to control the disease. Determining sow response to PRRS requires knowing pathogen burden and sow performance. In practice, though, records of pathogen burden are unavailable. We develop a statistical method to distinguish healthy and disease phases and to develop a method to quantify sows' responses to PRRS without having individual pathogen burden. We analyzed 10,910 sows with 57,135 repeated records of reproduction performance. Disease phases were recognized as strong deviation of herd-year-week estimates for reproduction traits using two methods: Method 1 used raw weekly averages of the herd; Method 2 used a linear model with fixed effects for seasonality, parity, and year, and random effects for herd-year-week and sow. The variation of sows in response to PRRS was quantified using 2 models on the traits number of piglets born alive (NBA) and number of piglets born dead (LOSS): 1) bivariate model considering the trait in healthy and disease phases as different traits, and 2) reaction norm model modeling the response of sows as a linear regression of the trait on herd-year-week estimates of NBA. The linear model for NBA had the highest sensitivity (78%) for disease phases. Residual variances of both were more than doubled in the disease phase compared with the healthy phase. Trait correlations between healthy and disease phases deviated from unity (0.57 ± 0.13 - 0.87 ± 0.18). In the bivariate model, repeatabilities were lower in disease phase compared with healthy phase (0.07 ± 0.027 and 0.16 ± 0.005 for NBA; 0.07 ± 0.027 and 0.09 ± 0.004 for LOSS). The reaction norm model fitted the data better than the bivariate model based on Akaike's information criterion, and had also higher predictive ability in disease phase based on cross validation. Our results show that the linear model is a practical method to distinguish between healthy and disease phases in farm data. We showed that there is variation among sows in response to PRRS, implying possibilities for selection, and the reaction norm model is a good model to study the response of animals toward diseases.