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Background: High resistance (the ability of the host to reduce pathogen load) and tolerance (the ability to maintain high performance at a given pathogen load) are two desirable host traits for producing animals that are resilient to infections. For Porcine Reproductive and Respiratory Syndrome (PRRS), one of the most devastating swine diseases worldwide, studies have identified substantial genetic variation in resistance of pigs, but evidence for genetic variation in tolerance has so far been inconclusive. Resistance and tolerance are usually considered as static traits. In this study, we used longitudinal viremia measurements of PRRS virus infected pigs to define discrete stages of infection based on viremia profile characteristics. These were used to investigate host genetic effects on viral load (VL) and growth at different stages of infection, to quantify genetic variation in tolerance at these stages and throughout the entire 42-day observation period, and to assess whether the single nucleotide polymorphism (SNP) WUR10000125 (WUR) with known large effects on resistance confers significant differences in tolerance., Results: Genetic correlations between resistance and growth changed considerably over time. Individuals that expressed high genetic resistance early in infection tended to grow slower during that time-period, but were more likely to experience lower VL and recovery in growth by the later stage. The WUR genotype was most strongly associated with VL at early- to mid-stages of infection, and with growth at mid- to late-stages of infection. Both, single-stage and repeated measurements random regression models identified significant genetic variation in tolerance. The WUR SNP was significantly associated only with the overall tolerance slope fitted through all stages of infection, with the genetically more resistant AB pigs for the WUR SNP being also more tolerant to PRRS., Conclusions: The results suggest that genetic selection for improved tolerance of pigs to PRRS is possible in principle, but may be feasible only with genomic selection, requiring intense recording schemes that involve repeated measurements to reliably estimate genetic effects. In the absence of such records, consideration of the WUR genotype in current selection schemes appears to be a promising strategy to improve simultaneously resistance and tolerance of growing pigs to PRRS.

Response to infection in animals has 2 main mechanisms: resistance (ability to control pathogen burden) and tolerance (ability to maintain performance given the pathogen burden). Selection on disease resistance and tolerance to infections seems a promising avenue to increase productivity of animals in the presence of disease infections, but it is hampered by a lack of records of pathogen burden of infected animals. Selection on resilience (ability to maintain performance regardless of pathogen burden) may, therefore, be an alternative pragmatic approach, because it does not need records of pathogen burden. Therefore, the aim of this study was to assess response to selection in resistance and tolerance when selecting on resilience compared with direct selection on resistance and tolerance. Monte Carlo simulation was used combined with selection index theory to predict responses to selection. Using EBV for resilience in the absence of records for pathogen burden resulted in favorable responses in resistance and tolerance to infections, with higher responses in tolerance than in resistance. If resistance and tolerance were unfavorably correlated, lower selection responses were obtained, especially in resistance. When the genetic correlation was very unfavorable, the selection response in tolerance became negative. Results showed that lower selection responses in resistance and tolerance were obtained when the frequency of disease outbreaks was 10% rather than 50% of the contemporary groups. The efficiency of selection on EBV for resilience compared with selection on EBV for resistance and tolerance was, however, not affected by the frequency of disease outbreaks. When records on pathogen burden were available, selection responses in resistance, tolerance, and the total breeding goal were 3 to 28%, 66 to 398%, and 2 to 11% higher, respectively, than when using the EBV for resilience, showing a clear benefit of recording pathogen burden. This study shows that selection on resilience is a pragmatic way of increasing disease resistance and tolerance to infections in the absence of records on pathogen burden, but recording pathogen burden would yield higher selection responses in resistance and tolerance.

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. Detennining 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 bom alive (NBA) and number of piglets bom 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 com-pared 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 pre-dictive ability in disease phase based on cross validation. Our results show that the linear model is a practical meth-od 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. [ABSTRACT FROM AUTHOR]