Genomic Prediction Accuracies for Growth and Carcass Traits in a Brangus Heifer Population.

The predictive abilities and accuracies of genomic best linear unbiased prediction (GBLUP) and the Bayesian (BayesA, BayesB, BayesC and Lasso) genomic selection (GS) methods for economically important growth (birth, weaning, and yearling weights) and carcass (depth of rib fat, apercent intramuscular fat and longissimus muscle area) traits were characterized by estimating the linkage disequilibrium (LD) structure in Brangus heifers using single nucleotide polymorphisms (SNP) markers. Sharp declines in LD were observed as distance among SNP markers increased. The application of the GBLUP and the Bayesian methods to obtain the GEBV for growth and carcass traits within k-means and random clusters showed that k-means and random clustering had quite similar heritability estimates, but the Bayesian methods resulted in the lower estimates of heritability between 0.06 and 0.21 for growth and carcass traits compared with those between 0.21 and 0.35 from the GBLUP methodologies. Although the prediction ability of the GBLUP and the Bayesian methods were quite similar for growth and carcass traits, the Bayesian methods overestimated the accuracies of GEBV because of the lower estimates of heritability of growth and carcass traits. However, GBLUP resulted in accuracy of GEBV for growth and carcass traits that parallels previous reports.

Assessment of the relationship between the postpartum diseases susceptibility and the bovine monocyte subsets via Bayesian logistic regression, under various prior distributions.

Postpartum diseases (PD) in dairy cows cause serious concerns about economic losses worldwide. This study intended to investigate the relationship between PD susceptibility and counts of monocyte subgroup cells (MCC), in the blood samples taken from 27 German Holstein cows 42 and 14 days before the expected calving by adopting the Bayesian approach. The paper also aimed to discuss the prior selection problem in the Bayesian approach and to reveal the parameter estimation difference based on the available data. The parameters were estimated according to the models established at two different time points with eight different prior distributions. As a result of the study, all the models revealed strong evidence that cows with PD, compared to healthy cows, had a higher increase in MCC counts on Day 14. There was no difference between the models according to their WAIC and LOO values. In terms of the parameter estimates, the models produced identical results; however, the models with noninformative priors presented strong evidence for the absence of effects by Bayes factor but, provided evidence for the existence of the effect according to the credible interval. The models with weakly informative and shrinkage priors provided strong evidence for the presence of the effect. The findings suggest that MCC can be considered to serve as a prospective indicator for early detection of PD.

Ambient Air Quality and General Health Outcomes in Nigde (Turkey) between 2011 and 2017.

BACKGROUND: We aimed to provide information for health practitioners and other related people about the association between ambient air quality and adverse health outcomes in the general population of Nigde, a central Turkish city, within the context of current health data epidemiological evidence. METHODS: The present study highlights the connection between health problems and time series of particulate matter (PM10) and sulphur dioxide (SO2) in Nigde, Turkey between 2011 and 2017. Significant morbidity is linked to ambient air pollution, resulting in a significant economic cost to society. RESULTS: We found that the required funds to treat cancers and chronic obstructive pulmonary disease triggered by ambient air pollution in Nigde, exceed 9 million US dollars per year, even when only the city center is taken into account. CONCLUSION: As Turkish cities grow and urban population density increases, air pollution issues need to be given priority in order to protect the health of the public and support sustainable development for future generations. It is recommended that particulate matter concentration in this urban center should be significantly reduced to minimize health problems.

Use of linear mixed models for genetic evaluation of gestation length and birth weight allowing for heavy-tailed residual effects.

BACKGROUND: The distribution of residual effects in linear mixed models in animal breeding applications is typically assumed normal, which makes inferences vulnerable to outlier observations. In order to mute the impact of outliers, one option is to fit models with residuals having a heavy-tailed distribution. Here, a Student's-t model was considered for the distribution of the residuals with the degrees of freedom treated as unknown. Bayesian inference was used to investigate a bivariate Student's-t (BSt) model using Markov chain Monte Carlo methods in a simulation study and analysing field data for gestation length and birth weight permitted to study the practical implications of fitting heavy-tailed distributions for residuals in linear mixed models. METHODS: In the simulation study, bivariate residuals were generated using Student's-t distribution with 4 or 12 degrees of freedom, or a normal distribution. Sire models with bivariate Student's-t or normal residuals were fitted to each simulated dataset using a hierarchical Bayesian approach. For the field data, consisting of gestation length and birth weight records on 7,883 Italian Piemontese cattle, a sire-maternal grandsire model including fixed effects of sex-age of dam and uncorrelated random herd-year-season effects were fitted using a hierarchical Bayesian approach. Residuals were defined to follow bivariate normal or Student's-t distributions with unknown degrees of freedom. RESULTS: Posterior mean estimates of degrees of freedom parameters seemed to be accurate and unbiased in the simulation study. Estimates of sire and herd variances were similar, if not identical, across fitted models. In the field data, there was strong support based on predictive log-likelihood values for the Student's-t error model. Most of the posterior density for degrees of freedom was below 4. Posterior means of direct and maternal heritabilities for birth weight were smaller in the Student's-t model than those in the normal model. Re-rankings of sires were observed between heavy-tailed and normal models. CONCLUSIONS: Reliable estimates of degrees of freedom were obtained in all simulated heavy-tailed and normal datasets. The predictive log-likelihood was able to distinguish the correct model among the models fitted to heavy-tailed datasets. There was no disadvantage of fitting a heavy-tailed model when the true model was normal. Predictive log-likelihood values indicated that heavy-tailed models with low degrees of freedom values fitted gestation length and birth weight data better than a model with normally distributed residuals.Heavy-tailed and normal models resulted in different estimates of direct and maternal heritabilities, and different sire rankings. Heavy-tailed models may be more appropriate for reliable estimation of genetic parameters from field data.