Behavioral observations are important in detecting illness, injury, and reproductive status as well as performance of normal behaviors. However, conducting live observations in extensive systems, such as pasture-based dairies, can be difficult and time consuming. Activity monitors, such as those created for use with automatic milking systems (AMS), have been developed to automatically and remotely collect individual behavioral data. Each cow wears a collar transponder for identification by the AMS, which can collect data on individual activity and rumination. The first aim of this study was to examine whether cow activity levels as reported by the AMS activity monitor (ACT) are accurate compared with live observations and previously validated pedometers [IceQube (IQ), IceRobotics, Edinburgh, UK]. The second aim of the study was to determine if the AMS rumination monitors (RUM) provide an accurate account of time spent ruminating compared with live observations. Fifteen lactating Holstein cows with pasture access were fitted with ACT, RUM, and IQ. Continuous focal observations (0600-2000 h) generated data on lying and active behaviors (standing and walking), as well as rumination. Activity recorded by live observation and IQ included walking and standing, whereas IQ steps measured cow movement (i.e., acceleration). Active behaviors were analyzed separately and in combination to ascertain exactly what behavioral components contributed to calculation of ACT "activity." Pearson correlations (rp) were computed between variables related to ACT, RUM, IQ, and live observations of behavior. A linear model was used to assess significance differences in the correlation coefficients of the 4 most relevant groups of variables. Significant but moderate correlations were found between ACT and observations of walking (r(p)=0.61), standing (r(p)=0.46), lying (r(p)=-0.57), and activity (r(p)=0.52), and between ACT and IQ steps (r(p)=0.75) and activity (r(p)=0.58) as well as between RUM and observations of rumination (rp=0.65). These data indicate that ACT and RUM do reflect cow walking and rumination, respectively, but not with a high degree of accuracy, and lying cannot be distinguished from standing.

We evaluated differences in gene expression in pigs from the Porcine Reproductive and Respiratory Syndrome (PRRS) Host Genetics Consortium initiative showing a range of responses to PRRS virus infection. Pigs were allocated into four phenotypic groups according to their serum viral level and weight gain. RNA obtained from blood at 0, 4, 7, 11, 14, 28, and 42 days post-infection (DPI) was hybridized to the 70-mer 20K Pigoligoarray. We used a blocked reference design for the microarray experiment. This allowed us to account for individual biological variation in gene expression, and to assess baseline effects before infection (0 DPI). Additionally, this design has the flexibility of incorporating future data for differential expression analysis. We focused on evaluating transcripts showing significant interaction of weight gain and serum viral level. We identified 491 significant comparisons [false discovery rate (FDR) = 10%] across all DPI and phenotypic groups. We corroborated the overall trend in direction and level of expression (measured as fold change) at 4 DPI using qPCR (r = 0.91, p ≤ 0.0007). At 4 and 7 DPI, network and functional analyses were performed to assess if immune related gene sets were enriched for genes differentially expressed (DE) across four phenotypic groups. We identified cell death function as being significantly associated (FDR ≤ 5%) with several networks enriched for DE transcripts. We found the genes interferon-alpha 1(IFNA1), major histocompatibility complex, class II, DQ alpha 1 (SLA-DQA1), and major histocompatibility complex, class II, DR alpha (SLA-DRA) to be DE (p ≤ 0.05) between phenotypic groups. Finally, we performed a power analysis to estimate sample size and sampling time-points for future experiments. We concluded the best scenario for investigation of early response to PRRSV infection consists of sampling at 0, 4, and 7 DPI using about 30 pigs per phenotypic group.

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BackgroundCalving difficulty or dystocia has a great economic impact in the US dairy industry. Reported risk factors associated with calving difficulty are feto-pelvic disproportion, gestation length and conformation. Different dairy cattle breeds have different incidence of calving difficulty, with Holstein having the highest dystocia rates and Jersey the lowest. Genomic selection becomes important especially for complex traits with low heritability, where the accuracy of conventional selection is lower. However, for complex traits where a large number of genes influence the phenotype, genome-wide association studies showed limitations. Biological networks could overcome some of these limitations and better capture the genetic architecture of complex traits. In this paper, we characterize Holstein, Brown Swiss and Jersey breed-specific dystocia networks and employ them in genomic predictions.ResultsMarker association analysis identified single nucleotide polymorphisms explaining the largest average proportion of genetic variance on BTA18 in Holstein, BTA25 in Brown Swiss, and BTA15 in Jersey. Gene networks derived from the genome-wide association included 1272 genes in Holstein, 1454 genes in Brown Swiss, and 1455 genes in Jersey. Furthermore, 256 genes in Holstein network, 275 genes in the Brown Swiss network, and 253 genes in the Jersey network were within previously reported dystocia quantitative trait loci. The across-breed network included 80 genes, with 9 genes being within previously reported dystocia quantitative trait loci. The gene-gene interactions in this network differed in the different breeds. Gene ontology enrichment analysis of genes in the networks showed Regulation of ARF GTPase was very significant (FDR ≤ 0.0098) on Holstein. Neuron morphogenesis and differentiation was the term most enriched (FDR ≤ 0.0539) on the across-breed network. Genomic prediction models enriched with network-derived relationship matrices did not outperform regular GBLUP models.ConclusionsRegions identified in the genome were in the proximity of previously described quantitative trait loci that would most likely affect calving difficulty by altering the feto-pelvic proportion. Inclusion of identified networks did not increase prediction accuracy. The approach used in this paper could be extended to any instance with asymmetric distribution of phenotypes, for example, resistance to disease data.Electronic supplementary materialThe online version of this article (10.1186/s12863-018-0606-y) contains supplementary material, which is available to authorized users.