Porcine reproductive and respiratory syndrome (PRRS) is a devastating disease in the swine industry. Identification of host genetic factors that enable selection for improved performance during PRRS virus (PRRSV) infection would reduce the impact of this disease on animal welfare and production efficiency. We conducted genomewide association study (GWAS) analyses of data from 13 trials of approximately 200 commercial crossbred nursery-age piglets that were experimentally infected with 1 of 2 type 2 isolates of PRRSV (NVSL 97-7985 [NVSL] and KS2006-72109 [KS06]). Phenotypes analyzed were viral load (VL) in blood during the first 21 d after infection (dpi) and weight gain (WG) from 0 to 42 dpi. We accounted for the previously identified QTL in the region on SSC4 in our models to increase power to identify additional regions. Many regions identified by single-SNP analyses were not identified using Bayes-B, but both analyses identified the same regions on SSC3 and SSC5 to be associated with VL in the KS06 trials and on SSC6 in the NVSL trials ( < 5 × 10); for WG, regions on SSC5 and SSC17 were associated in the NVSL trials ( < 3 × 10). No regions were identified with either method for WG in the KS06 trials. Except for the region on SSC4, which was associated with VL for both isolates (but only with WG for NVSL), identified regions did not overlap between the 2 PRRSV isolate data sets, despite high estimates of the genetic correlation between isolates for traits based on these data. We also identified genomic regions whose associations with VL or WG interacted with either PRRSV isolate or with genotype at the SSC4 QTL. Gene ontology (GO) annotation terms for genes located near moderately associated SNP ( < 0.003) were enriched for multiple immunologically (VL) and metabolism- (WG) related GO terms. The biological relevance of these regions suggests that, although it may increase the number of false positives, the use of single-SNP analyses and a relaxed threshold also increased the identification of true positives. In conclusion, although only the SSC4 QTL was associated with response to both PRRSV isolates, genes near associated SNP were enriched for the same GO terms across PRRSV isolates, suggesting that host responses to these 2 isolates are affected by the actions of many genes that function together in similar biological processes.

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High fat diets have been reported to negatively affect the microbiota in both mice and humans. However, there is a lack of studies in canine models. The variation among the gastrointestinal (GI) tract anatomy/physiology and typical diet compositions of these animal species may lead to vastly different results. Due to the large inclusion rate of dietary fat in pet food, it is critical to understand its effects in a canine model. Therefore, the study objective was to report the effects of high fat, low carbohydrate diets on the fecal microbiota in healthy adult dogs. Eight adult beagles were randomly assigned to one of four dietary treatments within each 15-day period of a replicated 4x4 Latin Square design. Diets contained 32% (T1), 37% (T2), 42% (T3), and 47% (T4) fat. T2, T3, and T4 were created by adding increasing levels of canola oil to T1, a commercially manufactured canned canine diet, which served as the control diet. Fresh fecal samples were collected during the last 5 days of each period for microbial analysis. DNA was extracted from fecal samples and paired-end 16S rRNA gene amplicon sequencing was performed using the Illumina MiSeq platform. When comparing whole microbial communities using PERMANOVA, no significant differences were observed among treatments (P = 0.735). Individual OTUs were analyzed using the GLIMMIX procedure of SAS with fixed effects of diet and room, and the random effects of period and animal. Out of the 100 most abundant individual OTUs, 36 showed significant differences in abundance based on treatment (q &lt; 0.05). Overall, OTUs assigned to genera related to fat digestion increased while OTUs assigned to genera involved in carbohydrate digestion decreased. In conclusion, the microbial community adapted to dietary intervention without jeopardizing the health of the animals, evaluated by body condition score, fecal characteristics, and blood parameters.

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