Genomic prediction for tuberculosis resistance in dairy cattle

Background: The increasing prevalence of bovine tuberculosis (bTB) in the UK and the limitations of the currently available diagnostic and control methods require the development of complementary approaches to assist in the sustainable control of the disease. One potential approach is the identification of animals that are genetically more resistant to bTB, to enable breeding of animals with enhanced resistance. This paper focuses on prediction of resistance to bTB. We explore estimation of direct genomic estimated breeding values (DGVs) for bTB resistance in UK dairy cattle, using dense SNP chip data, and test these genomic predictions for situations when disease phenotypes are not available on selection candidates. Methodology/Principal Findings: We estimated DGVs using genomic best linear unbiased prediction methodology, and assessed their predictive accuracies with a cross validation procedure and receiver operator characteristic (ROC) curves. Furthermore, these results were compared with theoretical expectations for prediction accuracy and area-under-the-ROC- curve (AUC). The dataset comprised 1151 Holstein-Friesian cows (bTB cases or controls). All individuals (592 cases and 559 controls) were genotyped for 727,252 loci (Illumina Bead Chip). The estimated observed heritability of bTB resistance was 0.23±0.06 (0.34 on the liability scale) and five-fold cross validation, replicated six times, provided a prediction accuracy of 0.33 (95% C.I.: 0.26, 0.40). ROC curves, and the resulting AUC, gave a probability of 0.58, averaged across six replicates, of correctly classifying cows as diseased or as healthy based on SNP chip genotype alone using these data. Conclusions/Significance: These results provide a first step in the investigation of the potential feasibility of genomic selection for bTB resistance using SNP data. Specifically, they demonstrate that genomic selection is possible, even in populations with no pedigree data and on animals lacking bTB phenotypes. However, a larger training population will be required to improve prediction accuracies. © 2014 Tsairidou et al.

Proteomic identification of plasma proteins as markers of growth promoter abuse in cattle

Growth-promoting agents are continually misused for increasing animal growth and fraudulent gain in the meat industry, yet detection rates from conventional targeted testing for drug residues do not reflect this. This is because testing currently relies on direct detection of drugs or related metabolites and administrators of such compounds can take adaptive measures to avoid detection through the use of endogenous or unknown drugs, and low dose or combined mixtures. New detection methods are needed which focus on the screening of biological responses of an animal to such growth-promoting agents as it has been demonstrated that genomic, proteomic and metabolomics profiles are altered by xenobiotic intake. Therefore, an untargeted proteomics approach using comparative two-dimensional gel electrophoresis (2DE) was carried out to identify putative proteins altered in plasma after treatment with oestradiol, dexamethasone or prednisolone. Twenty-four male cattle were randomly assigned to four groups (n = 6) for experimental treatment over 40 days, namely a control group of non-treated cattle, and three groups administered 17β-oestradiol-3-benzoate (0.01 mg/kg, intramuscular), dexamethasone sodium phosphate (0.7 mg/day, per os) or prednisolone acetate (15 mg/day, per os), respectively. Plasma collected from each animal at day 25 post study initiation was subjected to proteomic analysis by 2DE for comparison of protein expression between treated and untreated animals. Analysis of acquired gel images revealed 22 plasma proteins which differed in expression by more than 50 % (p < 0.05) in treated animals compared to untreated animals. Proteins of interest underwent identification by LC–MS/MS analysis and were found to have associated roles in transport, blood coagulation, immune response and metabolism pathways. In this way, seven proteins are highlighted as novel biomarker candidates including transthyretin which is shown to be significantly increased in all treatment groups compared to control animals and potentially may find use as global markers of suspect anabolic practice.