Fine mapping by composite genome-wide association analysis.

Genome-wide association (GWA) studies play a key role in current genetics research, unravelling genomic regions linked to phenotypic traits of interest in multiple species. Nevertheless, the extent of linkage disequilibrium (LD) may provide confounding results when significant genetic markers span along several contiguous cM. In this study, we have adapted the composite interval mapping approach to the GWA framework (composite GWA), in order to evaluate the impact of including competing (possibly linked) genetic markers when testing for the additive allelic effect inherent to a given genetic marker. We tested model performance on simulated data sets under different scenarios (i.e., qualitative trait loci effects, LD between genetic markers and width of the genomic region involved in the analysis). Our results showed that the genomic region had a small impact on the number of competing single nucleotide polymorphisms (SNPs) as well as on the precision of the composite GWA analysis. A similar conclusion was derived from the preferable range of LD between the tested SNP and competing SNPs, although moderate-to-high LD seemed to attenuate the loss of statistical power. The composite GWA improved specificity and reduced the number of significant genetic markers. The composite GWA model contributes a novel point of view for GWA analyses where testing circumscribed to the genomic region flanking each SNP (delimited by the nearest competing SNPs) and conditioning on linked markers increases the precision to locate causal mutations, but possibly at the expense of power.

Proteomic analysis reveals oxidative stress response as the main adaptative physiological mechanism in cows under different production systems.

Three groups of cows representing three ranges of welfare in the production system were included in the study: two groups of Bruna dels Pirineus beef cattle maintained under different management systems (good and semiferal conditions) and a group of Alberes cows, a breed that lives in the mountains (hardest conditions). In order to identify new stress/welfare biomarkers, serum from Bruna cows living in both environments was subjected to DIGE labelling, two-dimensional electrophoresis and MALDI-MS or ion trap MS. Identification was achieved for 15 proteins, which mainly belonged to three biological functions, the oxidative stress pathway (glutathione peroxidase (GPx) and paraoxonase (PON-1)), the acute phase protein family (Heremans Schmid glycoprotein alpha2 (α2-HSG)) and the complement system. Biological validation included the Alberes breed. GPx and PON-1 were validated by an enzymatic assay and found to be higher and lower, respectively, in cows living in hard conditions. α2-HSG was validated by ELISA and found to be reduced in hard conditions. Other biomarkers of the redox status were also altered by living conditions: protein carbonyl content, superoxide dismutase (SOD) and glutathione reductase (GR). Our results show that changes in the redox system are the main adaptation of cows living in challenging environmental conditions.

Carcass conformation and fat cover scores in beef cattle: a comparison of threshold linear models vs grouped data models.

BACKGROUND: Beef carcass conformation and fat cover scores are measured by subjective grading performed by trained technicians. The discrete nature of these scores is taken into account in genetic evaluations using a threshold model, which assumes an underlying continuous distribution called liability that can be modelled by different methods. METHODS: Five threshold models were compared in this study: three threshold linear models, one including slaughterhouse and sex effects, along with other systematic effects, with homogeneous thresholds and two extensions with heterogeneous thresholds that vary across slaughterhouses and across slaughterhouse and sex and a generalised linear model with reverse extreme value errors. For this last model, the underlying variable followed a Weibull distribution and was both a log-linear model and a grouped data model. The fifth model was an extension of grouped data models with score-dependent effects in order to allow for heterogeneous thresholds that vary across slaughterhouse and sex. Goodness-of-fit of these models was tested using the bootstrap methodology. Field data included 2,539 carcasses of the Bruna dels Pirineus beef cattle breed. RESULTS: Differences in carcass conformation and fat cover scores among slaughterhouses could not be totally captured by a systematic slaughterhouse effect, as fitted in the threshold linear model with homogeneous thresholds, and different thresholds per slaughterhouse were estimated using a slaughterhouse-specific threshold model. This model fixed most of the deficiencies when stratification by slaughterhouse was done, but it still failed to correctly fit frequencies stratified by sex, especially for fat cover, as 5 of the 8 current percentages were not included within the bootstrap interval. This indicates that scoring varied with sex and a specific sex per slaughterhouse threshold linear model should be used in order to guarantee the goodness-of-fit of the genetic evaluation model. This was also observed in grouped data models that avoided fitting deficiencies when slaughterhouse and sex effects were score-dependent. CONCLUSIONS: Both threshold linear models and grouped data models can guarantee the goodness-of-fit of the genetic evaluation for carcass conformation and fat cover, but our results highlight the need for specific thresholds by sex and slaughterhouse in order to avoid fitting deficiencies.

Connectedness among herds of beef cattle bred under natural service.

BACKGROUND: A procedure to measure connectedness among herds was applied to a beef cattle population bred by natural service. It consists of two steps: (a) computing coefficients of determination (CDs) of comparisons among herds; and (b) building sets of connected herds. METHODS: The CDs of comparisons among herds were calculated using a sampling-based method that estimates empirical variances of true and predicted breeding values from a simulated n-sample. Once the CD matrix was estimated, a clustering method that can handle a large number of comparisons was applied to build compact clusters of connected herds of the Bruna dels Pirineus beef cattle. Since in this breed, natural service is predominant and there are almost no links with reference sires, to estimate CDs, an animal model was used taking into consideration all pedigree information and, especially, the connections with dams. A sensitivity analysis was performed to contrast single-trait sire and animal model evaluations with different heritabilities, multiple-trait animal model evaluations with different degrees of genetic correlations and models with maternal effects. RESULTS: Using a sire model, the percentage of connected herds was very low even for highly heritable traits whereas with an animal model, most of the herds of the breed were well connected and high CD values were obtained among them, especially for highly heritable traits (the mean of average CD per herd was 0.535 for a simulated heritability of 0.40). For the lowly heritable traits, the average CD increased from 0.310 in the single-trait evaluation to 0.319 and 0.354 in the multi-trait evaluation with moderate and high genetic correlations, respectively. In models with maternal effects, the average CD per herd for the direct effects was similar to that from single-trait evaluations. For the maternal effects, the average CD per herd increased if the maternal effects had a high genetic correlation with the direct effects, but the percentage of connected herds for maternal effects was very low, less than 12%. CONCLUSIONS: The degree of connectedness in a bovine population bred by natural service mating, such as Bruna del Pirineus beef cattle, measured as the CD of comparisons among herds, is high. It is possible to define a pool of animals for which estimated breeding values can be compared after an across-herds genetic evaluation, especially for highly heritable traits.

Evaluation of serum cortisol, metabolic parameters, acute phase proteins and faecal corticosterone as indicators of stress in cows.

To assess the validity of laboratory parameters in blood and faeces as indicators of stress in cows, concentrations of cortisol, non-esterified fatty acids (NEFAs), 3-hydroxybutyrate, glucose, triglycerides, cholesterol, serum amyloid A (SAA) and haptoglobin in serum, as well as corticosterone in faeces, were determined in two breeds of cattle (Alberes and Bruna dels Pirineus) under different systems of housing and feeding. Serum cortisol concentrations were markedly elevated in the Alberes group, probably because they were less habituated to human handling. Corticosterone concentrations in faeces were significantly increased in the Bruna dels Pirineus cattle on Alberes pastures. Concentrations of NEFAs and cholesterol were significantly elevated in the Alberes cows, indicating an adrenergic stimulus of lipolysis or the existence of nutritional stress. SAA concentrations were significantly higher in groups living in hardy conditions, whereas there were no significant differences in haptoglobin between the three groups.