Genetic indicators for disease resilience in pigs

Infectious swine diseases have the potential to decimate the health and productivity of swine farms. One of the most economically concerning diseases is caused by the Porcine Reproductive and Respiratory Syndrome (PRRS) virus. While swine producers can implement vaccines, medications, or antibiotics and antiviral drugs, many infectious pathogens such as the PRRS virus have shown these strategies to be ineffective. One complimentary strategy would be to select pigs for increased disease resistance or resilience, where disease resilience is defined as an animal’s ability to maintain performance when infected. However, the elite populations that are used for genetic improvement are typically kept in high health conditions, making it difficult and impractical for swine breeders to use phenotypic selection in an environment with exposure to disease to select for increased disease resilience. Previous research has shown that host response to PRRS virus infection has a sizable genetic component and revealed a Quantitative Trait Locus (QTL) for host response to PRRS virus infection on Sus Scrofa Chromosome (SSC) 4. A putative causative mutation in the GBP5 gene was identified for this QTL. This mutation was determined to be in complete linkage disequilibrium with the single nucleotide polymorphism (SNP) WUR10000125 (WUR) that was included on commercial SNP panels. However, this was based on data from one genetic source. The overall objective of this thesis was to determine if the WUR SNP and phenotypes obtained from in-vitro mitogen stimulation assays (MSA) of peripheral blood mononuclear cells (PBMCs) from young healthy nursery pigs can be used as genetic indicators to select for disease resilience. The two requirements for a genetic indicator are that it must be heritable and have a sizeable genetic correlation with the trait of interest, in this case, disease resilience. Data from experimental PRRS virus infection trials from the PRRS Host Genetics Consortium (PHGC) and a polymicrobial Natural Disease Challenge Model (NDCM) of grow-finish pigs were used to address these objectives. Data and SNP genotypes, including for the WUR SNP and the putative causative mutation in the GBP5 gene, were available on 1414 pigs from eight PHGC trials of ~200 commercial crossbred nursery pigs per trial from six unrelated populations. Results showed that the WUR and GBP5 SNPs were not in complete linkage disequilibrium (r2 = 0.94). Discordant genotypes were determined to be the result of recombination, rather than genotyping errors. Although it was previously speculated that the GBP5 gene is a major gene responsible for host response to PRRS, there were small but non-significant differences between the effect of GBP5 and WUR on PRRS viral load and weight gain post-infection. These results indicate that either GBP5 or the WUR SNP can be used for marker-assisted selection to increase resistance to PRRS. In the NDCM, data from 3139 crossbred nursery barrows that were genotyped using a 650 K SNP Panel (Affymetrix) were used. The 650 K panel included the WUR SNP but not the GBP5 SNP. In the NDCM, pigs were entered through a batch system of 60 or 75 pigs per batch into a facility that was seeded with multiple infectious pathogens, including PRRS, to maximize the expression of disease resilience. Disease resilience traits, including growth, feed intake, and treatment and mortality rates were recorded. Based on these data, it was determined that the favorable G allele for the WUR SNP was significantly associated with greater average daily gain (p=0.02) and lower numbers of treatments in the challenge nursery (p=0.05) and across the challenge nursery and finisher (p=0.01), establishing the effect of the SSC4 QTL on resilience to a polymicrobial disease challenge. For the MSAs, PBMCs were isolated from blood samples of 882 pigs from 19 batches of the NDCM, taken at 27 or 35 days of age and prior to their entry in the disease challenge. For the MSAs, PBMCs were stimulated with five unique mitogens: Concanavalin A (Con A), Phytohemagglutinin (PHA), Poke Weed Mitogen (PWM), Lipopolysaccharide (LPS), and Phorbol Myristate Acetate (PMA), and evaluated for counts of proliferated cells after 48, 72, and 96 hours compared to unstimulated samples (restcount). Proliferated cell counts were adjusted for restcount in two ways: 1) by dividing the average cell count of the stimulated wells by the average cell count of the non-stimulated wells, to compute a Blastogenic Index Score (BIS), and 2) by including the average cell count of the non-stimulated wells as a covariate in the model for analysis of the average cell count of the stimulated wells. Data on BIS and stimulated means at each time point were analyzed separately for each mitogen. For pigs that had data at all three time points for a mitogen, data across these time points were incorporated into a single phenotype called the Area Under the Curve (AUC). Differences between pairs of time points for a given mitogen (delta = 72 – 48 hrs, 96 – 72 hrs, and 96 - 48 hrs) were also analyzed as phenotypes. Genetic parameters (heritabilities and genetic correlations) were estimated for the MSA phenotypes. In general, MSA phenotypes based on BIS versus stimulated means adjusted for restcount had similar estimates of genetic parameters. Heritability estimates for the Con A, PHA, and PMA MSA phenotypes were moderate, ranging from 0.13 +0.09 to 0.37 +0.10 for Con A, from 0.10 +0.07 to 0.34 +0.09 for PHA, and from 0.05 +0.06 to 0.30 +0.10 for PMA. Heritability estimates for the PWM and LPS MSA phenotypes were low, ranging from 0.00 +0.00 to 0.15 +0.09. Disease-related phenotypes collected on these same pigs in the NDCM were then used to estimate genetic correlations of the MSA phenotypes with disease resilience phenotypes. Phenotypic correlations between MSA and disease resilience phenotypes were low. Phenotypes derived from the Con A, PHA, and PMA MSAs, however, had moderately high estimates of genetic correlations with several disease resilience traits, although none were significantly different from zero due to large standard errors. However, genetic correlation estimates were generally in the expected direction, with pigs with higher MSA response having better resilience at the genetic level. Overall, Con A presented itself as the most promising mitogen to use as a genetic indicator for disease resilience, although further studies are recommended to validate its potential and to determine the ideal time point or MSA phenotype to use. In conclusion, the use of a genetic indicator to indirectly select for increased disease resilience in swine is a viable approach. The two indicators investigated in this thesis, i.e. genotype at an SNP on chromosome 4 and results of an in vitro mitogen stimulation assay on immune cells derived from the blood of young healthy piglets, are suitable genetic indicators for disease resilience to a polymicrobial disease challenge