Fatty acids (FA) have been related to effects on human health, sensory quality and shelf life of dairy products, cow's health and methane emission. However, despite their importance, they are not regularly measured in all dairy herds yet, which can affect the accuracy of estimated breeding values (EBV) for these traits. In this case, an alternative is to use genomic selection. Thus, the aim was to assess the use of genomic information in the genetic evaluation for milk traits in a tropical Holstein population.Monthly records (n = 36,457) of milk FA percentage, daily milk yield and quality traits from 4,203 cows as well as the genotypes of 755 of these cows for 57,368 single nucleotide polymorphisms (SNP) were used. Polygenic and genomic-polygenic models were applied for EBV prediction, and both models were compared through the EBV accuracy calculated from the prediction error and Spearman's correlation among EBV rankings. Prediction accuracy was assessed by using cross-validation.In this case, the accuracy was the correlation between the genomic breeding values (GEBV) obtained as the sum of SNP effects and the EBV obtained in the polygenic model in each validation group. For all traits, the use of the genomic-polygenic model did not alter the animals' ranking, with correlations higher than 0.87. Nevertheless, through this model, the accuracy increased from 1.5% to 6.8% compared to the polygenic model. The correlations between GEBV and EBV varied from 0.52 to 0.68. Therefore, the use of a small group of genotyped cows in the genetic evaluation can increase the accuracy of EBV for milk FA and other traditional milk traits.

We estimated the impact of the COVID-19 pandemic on mortality in Brazil for 2020 and 2021 years. We used mortality data (2015-2021) from the Health Ministry, Brazil government, to fit linear mixed models for forecasting baseline deaths under non-pandemic conditions. An advantage of the linear mixed model is the flexibility to capture year-trend while dealing with the correlations among death counts over time. Following a specified model building strategy, estimation of all-cause excess deaths at the country level and stratified by sex, age, ethnicity and region of residence, from March 2020 to August 2021. We also considered the estimation of excess deaths by specific causes. Estimated all-cause excess deaths was 199,108 (95% PI: 171,007; 227,209, P-Score=17.3%) for weeks 10-53, 2020, and 417,167 (95% PI: 372,075; 462,259, P-Score=50.1%) for weeks 1-32, 2021. P-scores ranged from 5.4% (RS, South) to 36.2% (AM, North) in 2020 and from 29.3% (AL, Northeast) to 94.9%$ (RO, North) in 2021. Differences among men (18.9%) and women  (14.2%) appeared in 2020 only, and the P-scores were about 51% for both sexes in 2021. Except for youngsters (<20 years old), all adult age groups were badly hit, especially those from 40 to 79 years old. In 2020, the Indigenous+East Asian population had the highest P-score (27%), and the Black population suffered the greatest impact (61.9%)  in 2021. The pandemic impact had enormous regional heterogeneity and substantial differences according to socio-demographic factors, mainly during the first wave, showing some population strata benefits from the social distancing measures when able to adhere to them. In the second wave, the burden was very high for all but extremely high for some, highlighting our society needs to tackle the health inequalities experienced by groups of different socio-demographic and economic status.

We estimated the impact of the COVID-19 pandemic on mortality in Brazil for 2020 and 2021 years. We used mortality data (2015–2021) from the Brazilian Health Ministry for forecasting baseline deaths under non-pandemic conditions and to estimate all-cause excess deaths at the country level and stratified by sex, age, ethnicity and region of residence, from March 2020 to December 2021. We also considered the estimation of excess deaths due to specific causes. The estimated all-cause excess deaths were 187 842 (95% PI: 164 122; 211 562, P-Score = 16.1%) for weeks 10-53, 2020, and 441 048 (95% PI: 411 740; 470 356, P-Score = 31.9%) for weeks 1-52, 2021. P-Score values ranged from 1.4% (RS, South) to 38.1% (AM, North) in 2020 and from 21.2% (AL and BA, Northeast) to 66.1% (RO, North) in 2021. Differences among men (18.4%) and women (13.4%) appeared in 2020 only, and the P-Score values were about 30% for both sexes in 2021. Except for youngsters (< 20 years old), all adult age groups were badly hit, especially those from 40 to 79 years old. In 2020, the Indigenous, Black and East Asian descendants had the highest P-Score (26.2 to 28.6%). In 2021, Black (34.7%) and East Asian descendants (42.5%) suffered the greatest impact. The pandemic impact had enormous regional heterogeneity and substantial differences according to socio-demographic factors, mainly during the first wave, showing that some population strata benefited from the social distancing measures when they could adhere to them. In the second wave, the burden was very high for all but extremely high for some, highlighting that our society must tackle the health inequalities experienced by groups of different socio-demographic statuses.

Context The economic efficiency of a dairy system is associated with the animal’s productive and reproductive abilities. Therefore, selection criteria should include milk production and quality traits as well as traits related to health and fertility. Since such phenotypes can present non-normal distributions, the use of threshold models is appropriate to study the genetic variation of such traits. Aim To estimate variance components for dairy production and functional traits in a Brazilian Holstein cattle population using linear and threshold models under a Bayesian approach. Methods Data comprised 64 657 test-day records for milk yield (MY, kg/day), casein percentage (CP, % of milk) and subclinical mastitis incidence (SCM), along with 4460 records for sexual precocity (PREC) from 5439 cows. Both SCM and PREC were defined as binary traits. Genetic analyses were based on linear (for MY and CP) and threshold (for SCM and PREC) models using Bayesian estimation. Non-informative and informative priors were considered for variance components, and these models were compared using the deviance information criterion (DIC) and the absolute difference between DIC (Δ). Key results Posterior means of heritability for MY, CP, SCM and PREC were 0.14, 0.39, 0.13 and 0.38 (based on non-informative priors) and 0.13, 0.27, 0.13 and 0.44 (considering informative priors), respectively. The model based on non-informative priors was better (lower DIC) for CP, whereas for PREC, the best model used informative priors. No differences between priors (Δ < 5) were observed for MY and SCM. Conclusions Threshold models were adequate for the analysis of non-normally distributed traits. The use of informative priors can be beneficial if specification is based on results from similar databases and models. Due to their high genetic variation, CP and PREC can be considered as selection criteria in animal breeding programs. In turn, accurate genetic evaluation for MY and SCM will depend on the pedigree and the information from genetically correlated traits. Implications Our study contributes to the understanding of the variation under important dairy production traits in a tropical Holstein population and provides information on the use of Bayesian threshold models as an appropriate method for the evaluation of non-normally distributed phenotypes.