Disease & heat resistance

Validation of Genomic Prediction for Host Response to Multifactorial PRRS Challenge

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Abstract

Porcine reproductive and respiratory syndrome (PRRS) is a global disease affecting pigs across all stages of production and causing major economic losses. Previous research by our group has demonstrated considerable genetic variation in host response to PRRS virus (PRRSv), indicating that genetic selection can be used to breed pigs for enhanced natural resilience to PRRSv infection. Since 2018, Topigs Norsvin has collected data from crossbred pigs under PRRS challenge conditions. Prior analyses showed that host response to PRRS is heritable, whether trials were analyzed individually or combined. The objective of this study was to evaluate the accuracy of genomic prediction for host response to PRRS challenge using data collected across genetic lines and diverse challenge conditions. Genomic prediction accuracy was assessed using the linear regression (LR) method, which compares predictions derived from a whole dataset containing all phenotypes with those from a partial dataset in which phenotypes from the most recent challenge trial are excluded. Phenotypic data were available from eight disease challenge trials. Pigs for each trial were produced at a commercial sow farm and transported to a wean-to-finish facility at weaning. In Trials 1-3, pigs were vaccinated post-arrival using a commercially available modified live PRRS vaccine, whereas pigs in Trials 4-8 were not vaccinated. Between three and seven weeks post-placement, depending on the trial, pigs were experimentally inoculated with a standardized dose of PRRSv via intramuscular injection and exposed to additional pathogens before and after inoculation. Individual-level data were recorded from placement through marketing. Genotypes for 20,856 genomic markers (post-quality control) were available for all animals. Genetic parameters were estimated using a single-trait animal model that accounted for trial, crossbred type (two-way or three-way), challenge treatment, body weight at inoculation, and age at inoculation. Average daily gain (ADG) from 0 to 28 days post-inoculation (dpi) was analyzed for 5,033 animals, including 725 animals in the partial dataset. Mortality, defined as survival status between 0 and 28 dpi, was analyzed as a binary trait using data from 12,071 animals, with 1,015 animals in the partial dataset. Mean ADG was 321.36 g in the whole dataset and 299.68 g in the partial dataset, while mortality rates were 17.2% and 25.7%, respectively. LR validation results showed a prediction accuracy of 0.48 for ADG, with upward bias (2.30) but no evidence of dispersion (0.92), indicating the need for caution when comparing predictions across trials. For mortality, prediction accuracy was 0.64, with no evidence of bias (-0.06) or dispersion (0.97). These results demonstrate that genomic data collected across genetic lines and challenge conditions can be used to predict ADG and mortality following PRRS exposure, supporting the use of genetic selection to improve resilience to multifactorial PRRS challenges.

Keywords: 2026

How to Cite:

Sevillano, C., Dunkelberger, J. & Grindflek, E., (2026) “Validation of Genomic Prediction for Host Response to Multifactorial PRRS Challenge”, World Congress on Genetics Applied to Livestock Production Digital Archive 2026(1): 2286343. doi: https://doi.org/10.31274/wcgalp.23940

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