1 research outputs found
Quantitative genetic and genomic analyses of the effect of Porcine Reproductive and Respiratory Syndrome (PRRS) outbreaks on the reproductive performance of sows
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