Nicholas James Boddicker

Evidence for a major QTL associated with host response to Porcine Reproductive and Respiratory Syndrome Virus challenge

Porcine reproductive and respiratory syndrome (PRRS) causes decreased reproductive performance in breeding animals and increased respiratory problems in growing animals, which result in significant economic losses in the swine industry. Vaccination has generally not been effective in the prevention of PRRS, partially because of the rapid mutation rate and evolution of the virus. The objective of the current study was to discover the genetic basis of host resistance or susceptibility to the PRRS virus through a genome-wide association study using data from the PRRS Host Genetics Consortium PRRS-CAP project. Three groups of approximately 190 commercial crossbred pigs from 1 breeding company were infected with PRRS virus between 18 and 28 d of age. Blood samples and BW were collected up to 42 d post infection (DPI). Pigs were genotyped with the Illumina Porcine 60k Beadchip. Whole-genome analysis focused on viremia at each day blood was collected and BW gains from 0 to 21 DPI (WG21) or 42 DPI (WG42). Viral load (VL) was quantified as area under the curve from 0 to 21 DPI. Heritabilities for WG42 and VL were moderate at 0.30 and litter accounted for an additional 14% of phenotypic variation. Genomic regions associated with VL were found on chromosomes 4 and X and on 1, 4, 7, and 17 for WG42. The 1-Mb region identified on chromosome 4 influenced both WG and VL, exhibited strong linkage disequilibrium, and explained 15.7% of the genetic variance for VL and 11.2% for WG42. Despite a genetic correlation of -0.46 between VL and WG42, genomic EBV for this region were favorably and nearly perfectly correlated. The favorable allele for the most significant SNP in this region had a frequency of 0.16 and estimated allele substitution effects were significant (P < 0.01) for each group when the SNP was fitted as a fixed covariate in a model that included random polygenic effects with overall estimates of -4.1 units for VL (phenotypic SD = 6.9) and 2.0 kg (phenotypic SD = 3 kg) for WG42. Candidate genes in this region on SSC4 include the interferon induced guanylate-binding protein gene family. In conclusion, host response to experimental PRRS virus challenge has a strong genetic component, and a QTL on chromosome 4 explains a substantial proportion of the genetic variance in the studied population. These results could have a major impact in the swine industry by enabling marker-assisted selection to reduce the impact of PRRS but need to be validated in additional populations.

Effects of selection for decreased residual feed intake on composition and quality of fresh pork

The objectives of this study were to determine the extent to which selection for decreased residual feed intake (RFI) affects pork composition and quality. Pigs from the fifth generation of selection for decreased RFI (select) and a randomly selected line (control) were utilized. Two experiments were conducted. In Exp. 1, barrows (22.6 ± 3.9 kg) from select and control lines were paired based on age and BW. The test was conducted in 8 replicates of pairs for the test period of 6 wk. Calpastatin activity and myosin isoforms profile were determined on samples from the LM. Control barrows were heavier (59.1 vs. 55.0 kg; P < 0.01) at the end of the test period. Calpastatin activity was greater (P < 0.01) in LM of select barrows than control barrows. In Exp. 2, composition and quality of gilts (114 kg) from control and select lines were determined. The model included fixed effects of line, slaughter date, melanocortin-4 receptor (MC4R) genotype, barn group, line × slaughter date, genotype × line interactions, a covariate of off-test BW, and sire, pen, and litter fitted as random effects. The select line (n = 80) had 0.043 kg less (P < 0.05) RFI per day than the control line (n = 89). Loin quality and composition were determined at 2 d postmortem. Desmin degradation was measured at 2 and 7 d postmortem. Purge, cook loss, sensory traits, and star probe texture were measured at 7 to 10 d postmortem on cooked chops. Residual correlations between RFI and composition and quality traits were calculated. Compared with the control line, carcasses from the select line tended to have less (P = 0.09) backfat, greater (P < 0.05) loin depth, and greater (P < 0.05) fat free lean. Loin chops from the select line had less (P < 0.01) intramuscular lipid content than loin chops from control line. Significant residual correlations between RFI and both tenderness (r = 0.24, P < 0.01) and star probe (r = -0.26, P < 0.01) were identified. Selection for decreased RFI has the potential to improve carcass composition with few effects on pH and water-holding capacity. However, decreased RFI could negatively affect tenderness and texture because of decreased lipid content and decreased postmortem protein degradation.

Genome Wide Association Studies (GWAS) Identify QTL on SSC2 and SSC17 Affecting Loin Peak Shear Force in Crossbred Commercial Pigs.

Of all the meat quality traits, tenderness is considered the most important with regard to eating quality and market value. In this study we have utilised genome wide association studies (GWAS) for peak shear force (PSF) of loin muscle as a measure of tenderness for 1,976 crossbred commercial pigs, genotyped for 42,721 informative SNPs using the Illumina PorcineSNP60 Beadchip. Four 1 Mb genomic regions, three on SSC2 (at 4 Mb, 5 Mb and 109 Mb) and one on SSC17 (at 20 Mb), were detected which collectively explained about 15.30% and 3.07% of the total genetic and phenotypic variance for PSF respectively. Markers ASGA0008566, ASGA0008695, DRGA0003285 and ASGA0075615 in the four regions were strongly associated with the effects. Analysis of the reference genome sequence in the region with the most important SNPs for SSC2_5 identified FRMD8, SLC25A45 and LTBP3 as potential candidate genes for meat tenderness on the basis of functional annotation of these genes. The region SSC2_109 was close to a previously reported candidate gene CAST; however, the very weak LD between DRGA0003285 (the best marker representing region SSC2_109) and CAST indicated the potential for additional genes which are distinct from, or interact with, CAST to affect meat tenderness. Limited information of known genes in regions SSC2_109 and SSC17_20 restricts further analysis. Re-sequencing of these regions for informative animals may help to resolve the molecular architecture and identify new candidate genes and causative mutations affecting this trait. These findings contribute significantly to our knowledge of the genomic regions affecting pork shear force and will potentially lead to new insights into the molecular mechanisms regulating meat tenderness.

Genomic evaluation of feed efficiency component traits in Duroc pigs using 80K, 650K and whole-genome sequence variants

BackgroundIncreasing marker density was proposed to have potential to improve the accuracy of genomic prediction for quantitative traits; whole-sequence data is expected to give the best accuracy of prediction, since all causal mutations that underlie a trait are expected to be included. However, in cattle and chicken, this assumption is not supported by empirical studies. Our objective was to compare the accuracy of genomic prediction of feed efficiency component traits in Duroc pigs using single nucleotide polymorphism (SNP) panels of 80K, imputed 650K, and whole-genome sequence variants using GBLUP, BayesB and BayesRC methods, with the ultimate purpose to determine the optimal method to increase genetic gain for feed efficiency in pigs.ResultsPhenotypes of average daily feed intake (ADFI), average daily gain (ADG), ultrasound backfat depth (FAT), and loin muscle depth (LMD) were available for 1363 Duroc boars from a commercial breeding program. Genotype imputation accuracies reached 92.1% from 80K to 650K and 85.6% from 650K to whole-genome sequence variants. Average accuracies across methods and marker densities of genomic prediction of ADFI, FAT, LMD and ADG were 0.40, 0.65, 0.30 and 0.15, respectively. For ADFI and FAT, BayesB outperformed GBLUP, but increasing marker density had little advantage for genomic prediction. For ADG and LMD, GBLUP outperformed BayesB, while BayesRC based on whole-genome sequence data gave the best accuracies and reached up to 0.35 for LMD and 0.25 for ADG.ConclusionsUse of genomic information was beneficial for prediction of ADFI and FAT but not for that of ADG and LMD compared to pedigree-based estimates. BayesB based on 80K SNPs gave the best genomic prediction accuracy for ADFI and FAT, while BayesRC based on whole-genome sequence data performed best for ADG and LMD. We suggest that these differences between traits in the effect of marker density and method on accuracy of genomic prediction are mainly due to the underlying genetic architecture of the traits.