Selection for disease resistance in swine

Abstract Text:

Infectious disease is a major problem for swine production around the world despite successes in terms of biosecurity and vaccination. Perhaps the best known examples of disease resistance pertain to infection with E. coli F4 (K88) or F18 which cause scours in young pigs and can result in significant morbidity and mortality. In other cases, there is variation in susceptibility but all pigs become sick. Porcine respiratory and reproduction syndrome (PRRS) is an example of a disease where all pigs are infected by PRRSV but the impact on the host varies. For example, the amount of virus produced and the growth of the infected pigs varies significantly between individuals. Genome wide association studies identified a relatively large effect on chromosome 4 (SSC4) and Boddicker et al (2012 J. Anim Sci 90:1733) reported that this region explained 15.7% of the genetic variance for viral load and 11.2% for gain. Subsequently a putative functional variant in GBP5 was identified as the likely causative mutation (Koltes et al. 2015 BMC Genomics 16:412). Two regions of the genome were identified as explaining a similar amount of variation in PCV2 susceptibility (Engle et al 2014 Proc. 10th WCGALP). Although these findings offer potential tools to reduce the impact of these two major viruses through genomic assisted selection, it would be very difficult to combine such tools for all of the important diseases. One option may be to select for animals that respond better to infection, maintaining growth and/or rapidly recovering. This is described as resilience or robustness. A new study is underway to investigate resilience using a “natural challenge” model consisting of a number of different agents including PRRSV as well as Swine Influenza Virus, Haemophilus parasuis and Streptococcus suis. A number of approaches are being used to try to identify biomarkers that will predict resilience in high health status pigs to reduce the impact of infection and contribute to reducing the use of antimicrobials in production. For example, Kommadath et al (2015 BMC Genomics 15: 452) used RNAseq to identify gene expression patterns prior to infection with Salmonella that predict different outcomes in terms of carriage of the pathogen. A second option in the future may be to use gene editing to manipulate the early stages of infection and develop novel resistance as was recently demonstrated so remarkably for PRRSV by Prather and colleagues (Whitworth et al 2016 Nature Biotechnology 34:20).

Keywords:

Health, Pigs, Resilience