Porcine Reproductive and Respiratory Syndrome (PRRS) is, globally, one of the costliest of diseases to the pig industry. Despite enormous efforts, methods such as vaccination strategies and herd management have failed to fully control the disease. Exploiting the genetic variation in host response could be included as part of a multifaceted approach to mitigate the devastating impact of this disease. Establishing the presence of genetic variation and its underlying genetic architecture are key to implementing genomic selection, which is considered a viable and safe long-term disease control strategy. This thesis explores the effect of natural PRRSV outbreaks on the reproductive performance of sows, and the underlying genetic influences on it. Litter records were available from two farms, where Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) outbreaks had been confirmed using ELISA. One farm had full pedigree information, but for both farms 60K SNP genotypes were available. In both farms, performance records could be partitioned into an epidemic and non-epidemic phase using a previously established threshold method. The partitioning also identified a period of high reproductive failure not coinciding with a diagnosed PRRSV outbreak on one farm. This period was isolated and analysed separately. Linear mixed models were used to explore both genetic and non-genetic factors contributing to differences in reproductive performance associated with the two phases. This analysis identified five disease indicator traits identified showing significant differences (>95% CI) in least squares means between the epidemic and non-epidemic phase. These were the number of mummified, stillborn, dead and alive piglets per litter and the fraction of the total born dead. Alternative statistical models that accounted for differences in the severity of the individual PRRSV outbreaks were also considered throughout. Despite differences in the estimates associated with different models and farms, in general very low heritability estimates were obtained for these disease indicator traits during the non-epidemic phase, whereas the traits were found moderately heritable during the epidemic phase. Two genome wide association analyses methods were used to explore the distribution of the genetic effects throughout the genome: Family-based Score Test for Association (FASTA) and Genome-wide Rapid Analysis using Mixed Model and Regression (GRAMMAR). In addition, regional associations were studied using Regional Heritability Mapping (RHM). Associations were then further characterised using Measured Genotype (MG) analyses. Genome-wide significant associations were identified for five SNPs and one region. The regional association spans the region previously identified in an experimental challenge experiment of growing pigs, in association with viral load and weight gain. Different patterns of linkage disequilibrium (LD) are observed which may explain why this study and others failed to find single SNP effects at this location. One genome wide significant SNP on SSC15 was found between two previously identified SNPs associated with PRRSV mortality. Five further putative SNP associations are indicated by RHM and subsequent measured genotype analysis, two of which flank previously reported associations and indicate an epistatic effect, observed in several traits. In summary, this study showed that reproductive performance of sow is considerably reduced during PRRSV outbreaks and the genetics of the sow significantly affects variance in survival and mortality. Several novel genomic regions associated with the reproductive performance of sows in the absence and during PRRSV outbreaks have been identified in this study. In addition to these, the results suggest the region on SSC4 previously associated with PRRSV viral load and weight gain may also affect foetal mortality. These results demonstrate the potential for genomic selection to be used to mitigate PRRSV related reproductive losses, the greatest financial exposure faced by the pig industry. In addition, RHM is directly shown to capture genetic variance, where single SNP methods fail to identify an effect, highlighting the usefulness of this tool as a method to identify genomic regions with significant effect on production traits.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:756580 |
| Date | January 2018 |
| Creators | Orrett, Christopher Mark |
| Contributors | Archibald, Alan |
| Publisher | University of Edinburgh |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/1842/31281 |
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