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Genomic prediction of piglet response to infection with one of two porcine reproductive and respiratory syndrome virus isolates
- Waide, Emily H., Tuggle, Christopher K., Serão, Nick V. L., Schroyen, Martine, Hess, Andrew, Rowland, Raymond R. R., Lunney, Joan K., Plastow, Graham, Dekkers, Jack C. M.
- Genetics, selection, evolution 2018 v.50 no.1 pp. 3
- Porcine reproductive and respiratory syndrome virus, chromosomes, data collection, genetic background, genome, genome-wide association study, genomics, genotyping, linkage disequilibrium, marker-assisted selection, phenotype, piglets, porcine reproductive and respiratory syndrome, pork industry, prediction, quantitative trait loci, single nucleotide polymorphism, viral load, virulence, viruses, weight gain
- BACKGROUND: Genomic prediction of the pig’s response to the porcine reproductive and respiratory syndrome (PRRS) virus (PRRSV) would be a useful tool in the swine industry. This study investigated the accuracy of genomic prediction based on porcine SNP60 Beadchip data using training and validation datasets from populations with different genetic backgrounds that were challenged with different PRRSV isolates. RESULTS: Genomic prediction accuracy averaged 0.34 for viral load (VL) and 0.23 for weight gain (WG) following experimental PRRSV challenge, which demonstrates that genomic selection could be used to improve response to PRRSV infection. Training on WG data during infection with a less virulent PRRSV, KS06, resulted in poor accuracy of prediction for WG during infection with a more virulent PRRSV, NVSL. Inclusion of single nucleotide polymorphisms (SNPs) that are in linkage disequilibrium with a major quantitative trait locus (QTL) on chromosome 4 was vital for accurate prediction of VL. Overall, SNPs that were significantly associated with either trait in single SNP genome-wide association analysis were unable to predict the phenotypes with an accuracy as high as that obtained by using all genotyped SNPs across the genome. Inclusion of data from close relatives into the training population increased whole genome prediction accuracy by 33% for VL and by 37% for WG but did not affect the accuracy of prediction when using only SNPs in the major QTL region. CONCLUSIONS: Results show that genomic prediction of response to PRRSV infection is moderately accurate and, when using all SNPs on the porcine SNP60 Beadchip, is not very sensitive to differences in virulence of the PRRSV in training and validation populations. Including close relatives in the training population increased prediction accuracy when using the whole genome or SNPs other than those near a major QTL.