Genetic and genomic basis of antibody response to porcine reproductive and respiratory syndrome (PRRS) in gilts and sows

Authors

Nick V. Serão, North Carolina State UniversityFollow
Robert A. Kemp, Genesus Inc., Oakville, MB
Benny Mote, University of Nebraska-LincolnFollow
Philip Willson, University of Saskatchewan
John C.S. Harding, University of SaskatchewanFollow
Stephen C. Bishop, University of Edinburgh
Graham S. Plastow, University of AlbertaFollow
Jack C.M. Dekkers, Iowa State UniversityFollow

Date of this Version

2016

Citation

Serão et al. Genetics Selection Evolution (2016) 48:51
DOI 10.1186/s12711-016-0230-0

Comments

© 2016 The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 International License

Abstract

Background: Our recent research showed that antibody response to porcine reproductive and respiratory syndrome (PRRS), measured as sample-to-positive (S/P) ratio, is highly heritable and has a high genetic correlation with reproductive performance during a PRRS outbreak. Two major quantitative trait loci (QTL) on Sus scrofa chromosome 7 (SSC7; QTL_MHC and QTL₁₃₀) accounted for ~40 % of the genetic variance for S/P. Objectives of this study were to estimate genetic parameters for PRRS S/P in gilts during acclimation, identify regions associated with S/P, and evaluate the accuracy of genomic prediction of S/P across populations with different prevalences of PRRS and using different single nucleotide polymorphism (SNP) sets.

Methods: Phenotypes and high-density SNP genotypes of female pigs from two datasets were used. The outbreak dataset included 607 animals from one multiplier herd, whereas the gilt acclimation (GA) dataset included data on 2364 replacement gilts from seven breeding companies placed on health-challenged farms. Genomic prediction was evaluated using GA for training and validation, and using GA for training and outbreak for validation. Predictions were based on SNPs across the genome (SNP_All), SNPs in one (SNP_MHC and SNP₁₃₀) or both (SNP_SSC7) QTL, or SNPs outside the QTL (SNP_Rest).

Results: Heritability of S/P in the GA dataset increased with the proportion of PRRS-positive animals in the herd (from 0.28 to 0.47). Genomic prediction accuracies ranged from low to moderate. Average accuracies were highest when using only the 269 SNPs in both QTL regions (SNP_SSC7, with accuracies of 0.39 and 0.31 for outbreak and GA validation datasets, respectively. Average accuracies for SNP_ALL, SNP_MHC, SNP₁₃₀, and SNP_Rest were, respectively, 0.26, 0.39, 0.21, and 0.05 for the outbreak, and 0.28, 0.25, 0.22, and 0.12, for the GA validation datasets.

Conclusions: Moderate genomic prediction accuracies can be obtained for PRRS antibody response using SNPs located within two major QTL on SSC7, while the rest of the genome showed limited predictive ability. Results were obtained using data from multiple genetic sources and farms, which further strengthens these findings. Further research is needed to validate the use of S/P ratio as an indicator trait for reproductive performance during PRRS outbreaks.