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Background: Identifying molecular mechanisms responsible for the response to heat stress is essential to increase production, reproduction, health, and welfare. This study aimed to identify early biological responses and potential biomarkers involved in the response to heat stress and animal's recovery in tropically adapted beef cattle through proteomic analysis of blood plasma. Methods: Blood samples were collected from 14 Caracu males during the heat stress peak (HSP) and 16 h after it (heat stress recovery-HSR) assessed based on wet bulb globe temperature index and rectal temperature. Proteome was investigated by liquid chromatography-tandem mass spectrometry from plasma samples, and the differentially regulated proteins were evaluated by functional enrichment analysis using DAVID tool. The protein-protein interaction network was evaluated by STRING tool. Results: A total of 1,550 proteins were detected in both time points, of which 84 and 65 were downregulated and upregulated during HSR, respectively. Among the differentially regulated proteins with the highest absolute log-fold change values, those encoded by the GABBR1, EPHA2, DUSP5, MUC2, DGCR8, MAP2K7, ADRA1A, CXADR, TOPBP1, and NEB genes were highlighted as potential biomarkers because of their roles in response to heat stress. The functional enrichment analysis revealed that 65 Gene Ontology terms and 34 pathways were significant (P < 0.05). We highlighted those that could be associated with the response to heat stress, such as those related to the immune system, complement system, hemostasis, calcium, ECM-receptor interaction, and PI3K-Akt and MAPK signaling pathways. In addition, the protein-protein interaction network analysis revealed several complement and coagulation proteins and acute-phase proteins as important nodes based on their centrality and edges. Conclusion: Identifying differentially regulated proteins and their relationship, as well as their roles in key pathways contribute to improve the knowledge of the mechanisms behind the response to heat stress in naturally adapted cattle breeds. In addition, proteins highlighted herein are potential biomarkers involved in the early response and recovery from heat stress in tropically adapted beef cattle.

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The exact accuracy of estimated breeding values can be calculated based on the prediction error variances obtained from the diagonal of the inverse of the left-hand side (LHS) of the mixed model equations (MME). However, inverting the LHS is not computationally feasible for large datasets, especially if genomic information is available. Thus, different algorithms have been proposed to approximate accuracies. This study aimed to: 1) compare the approximated accuracies from 2 algorithms implemented in the BLUPF90 suite of programs, 2) compare the approximated accuracies from the 2 algorithms against the exact accuracy based on the inversion of the LHS of MME, and 3) evaluate the impact of adding genotyped animals with and without phenotypes on the exact and approximated accuracies. Algorithm 1 approximates accuracies based on the diagonal of the genomic relationship matrix (G). In turn, algorithm 2 combines accuracies with and without genomic information through effective record contributions. The data were provided by the American Angus Association and included 3 datasets of growth, carcass, and marbling traits. The genotype file contained 1,235,930 animals, and the pedigree file contained 12,492,581 animals. For the genomic evaluation, a multi-trait model was applied to the datasets. To ensure the feasibility of inverting the LHS of the MME, a subset of data under single-trait models was used to compare approximated and exact accuracies. The correlations between exact and approximated accuracies from algorithms 1 and 2 of genotyped animals ranged from 0.87 to 0.90 and 0.98 to 0.99, respectively. The intercept and slope of the regression of exact on approximated accuracies from algorithm 2 ranged from 0.00 to 0.01 and 0.82 to 0.87, respectively. However, the intercept and the slope for algorithm 1 ranged from -0.10 to 0.05 and 0.98 to 1.10, respectively. In more than 80% of the traits, algorithm 2 exhibited a smaller mean square error than algorithm 1. The correlation between the approximated accuracies obtained from algorithms 1 and 2 ranged from 0.56 to 0.74, 0.38 to 0.71, and 0.71 to 0.97 in the groups of genotyped animals, genotyped animals without phenotype, and proven genotyped sires, respectively. The approximated accuracy from algorithm 2 showed a closer behavior to the exact accuracy when including genotyped animals in the analysis. According to the results, algorithm 2 is recommended for genetic evaluations since it proved more precise.

The genomic estimated breeding value (GEBV) represents an animal's genetic merit calculated using a combination of phenotypes, pedigree, and genomic information through a procedure known as single-step genomic best linear unbiased prediction (ssGBLUP). The accuracy of a GEBV reflects how closely it correlates with the true breeding value. However, calculating accuracies is not computationally feasible for large datasets with genomic information. In this context, methods for approximating accuracies have been proposed and implemented into genetic evaluations. This study aimed to compare 2 algorithms to approximate accuracies for ssGBLUP. In algorithm 1, genomic contributions are based on the diagonal of the genomic relationship matrix (G), combined with contributions from animal records and pedigrees. In turn, algorithm 2 combines accuracies with and without genomic information through effective record contributions. The data for this study were provided by the American Angus Association and included datasets of growth, carcass, and marbling traits. Genotypes were available for 1,235,930 animals, and the pedigree had 12,492,581 animals. We showed that algorithm 2 is better suited for approximating accuracies, as its approximations closely matched the exact accuracy values obtained from the inverse of the mixed model equations.

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BACKGROUND: The genotype-by-environment interaction (GxE) in beef cattle can be investigated using reaction norm models to assess environmental sensitivity and, combined with genome-wide association studies (GWAS), to map genomic regions related to animal adaptation. Including genetic markers from whole-genome sequencing in reaction norm (RN) models allows us to identify high-resolution candidate genes across environmental gradients through GWAS. Hence, we performed a GWAS via the RN approach using whole-genome sequencing data, focusing on mapping candidate genes associated with the expression of reproductive and growth traits in Nellore cattle. For this purpose, we used phenotypic data for age at first calving (AFC), scrotal circumference (SC), post-weaning weight gain (PWG), and yearling weight (YW). A total of 20,000 males and 7,159 females genotyped with 770k were imputed to the whole sequence (29 M). After quality control and linkage disequilibrium (LD) pruning, there remained ∼ 2.41 M SNPs for SC, PWG, and YW and ∼ 5.06 M SNPs for AFC. RESULTS: Significant SNPs were identified on Bos taurus autosomes (BTA) 10, 11, 14, 18, 19, 20, 21, 24, 25 and 27 for AFC and on BTA 4, 5 and 8 for SC. For growth traits, significant SNP markers were identified on BTA 3, 5 and 20 for YW and PWG. A total of 56 positional candidate genes were identified for AFC, 9 for SC, 3 for PWG, and 24 for YW. The significant SNPs detected for the reaction norm coefficients in Nellore cattle were found to be associated with growth, adaptative, and reproductive traits. These candidate genes are involved in biological mechanisms related to lipid metabolism, immune response, mitogen-activated protein kinase (MAPK) signaling pathway, and energy and phosphate metabolism. CONCLUSIONS: GWAS results highlighted differences in the physiological processes linked to lipid metabolism, immune response, MAPK signaling pathway, and energy and phosphate metabolism, providing insights into how different environmental conditions interact with specific genes affecting animal adaptation, productivity, and reproductive performance. The shared genomic regions between the intercept and slope are directly implicated in the regulation of growth and reproductive traits in Nellore cattle raised under different environmental conditions.

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Brazilian livestock breeding programmes strive to enhance the genetics of beef cattle, with a strong emphasis on the Nellore breed, which has an extensive database and has achieved significant genetic progress in the last years. There are other indicine breeds that are economically important in Brazil; however, these breeds have more modest sets of phenotypes, pedigree and genotypes, slowing down their genetic progress as their predictions are less accurate. Combining several breeds in a multi-breed evaluation could help enhance predictions for those breeds with less information available. This study aimed to evaluate the feasibility of multi-breed, single-step genomic best linear unbiased predictor genomic evaluations for Nellore, Brahman, Guzerat and Tabapua. Multi-breed evaluations were contrasted to the single-breed ones. Data were sourced from the National Association of Breeders and Researchers of Brazil and included pedigree (4,207,516), phenotypic (328,748), and genomic (63,492) information across all breeds. Phenotypes were available for adjusted weight at 210 and 450 days of age, and scrotal circumference at 365 days of age. Various scenarios were evaluated to ensure pedigree and genomic information compatibility when combining different breeds, including metafounders (MF) or building the genomic relationship matrix with breed-specific allele frequencies. Scenarios were compared using the linear regression method for bias, dispersion and accuracy. The results showed that using multi-breed evaluations significantly improved accuracy, especially for smaller breeds like Guzerat and Tabapua. The validation statistics indicated that the MF approach provided accurate predictions, albeit with some bias. While single-breed evaluations tended to have lower accuracy, merging all breeds in multi-breed evaluations increased accuracy and reduced dispersion. This study demonstrates that multi-breed genomic evaluations are proper for indicine beef cattle breeds. The MF approach may be particularly beneficial for less-represented breeds, addressing limitations related to small reference populations and incompatibilities between G and A22. By leveraging genomic information across breeds, breeders and producers can make more informed selection decisions, ultimately improving genetic gain in these cattle populations.

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Feed efficiency plays a major role in the overall profitability and sustainability of the beef cattle industry, as it is directly related to the reduction of the animal demand for input and methane emissions. Traditionally, the average daily feed intake and weight gain are used to calculate feed efficiency traits. However, feed efficiency traits can be analysed longitudinally using random regression models (RRMs), which allow fitting random genetic and environmental effects over time by considering the covariance pattern between the daily records. Therefore, the objectives of this study were to: (1) propose genomic evaluations for dry matter intake (DMI), body weight gain (BWG), residual feed intake (RFI) and residual weight gain (RWG) data collected during an 84-day feedlot test period via RRMs; (2) compare the goodness-of-fit of RRM using Legendre polynomials (LP) and B-spline functions; (3) evaluate the genetic parameters behaviour for feed efficiency traits and their implication for new selection strategies. The datasets were provided by the EMBRAPA-GENEPLUS beef cattle breeding program and included 2920 records for DMI, 2696 records for BWG and 4675 genotyped animals. Genetic parameters and genomic breeding values (GEBVs) were estimated by RRMs under ssGBLUP for Nellore cattle using orthogonal LPs and B-spline. Models were compared based on the deviance information criterion (DIC). The ranking of the average GEBV of each test week and the overall GEBV average were compared by the percentage of individuals in common and the Spearman correlation coefficient (top 1%, 5%, 10% and 100%). The highest goodness-of-fit was obtained with linear B-Spline function considering heterogeneous residual variance. The heritability estimates across the test period for DMI, BWG, RFI and RWG ranged from 0.06 to 0.21, 0.11 to 0.30, 0.03 to 0.26 and 0.07 to 0.27, respectively. DMI and RFI presented within-trait genetic correlations ranging from low to high magnitude across different performance test-day. In contrast, BWG and RWG presented negative genetic correlations between the first 3 weeks and the other days of performance tests. DMI and RFI presented a high-ranking similarity between the GEBV average of week eight and the overall GEBV average, with Spearman correlations and percentages of individuals selected in common ranging from 0.95 to 1.00 and 93 to 100, respectively. Week 11 presented the highest Spearman correlations (ranging from 0.94 to 0.98) and percentages of individuals selected in common (ranging from 85 to 94) of BWG and RWG with the average GEBV of the entire period of the test. In conclusion, the RRM using linear B-splines is a feasible alternative for the genomic evaluation of feed efficiency. Heritability estimates of DMI, RFI, BWG and RWG indicate enough additive genetic variance to achieve a moderate response to selection. A new selection strategy can be adopted by reducing the performance test to 56 days for DMI and RFI selection and 77 days for BWG and RWG selection.

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Abstract Objective: To evaluate idiopathic musculoskeletal pain, musculoskeletal pain syndromes, and use of electronic devices in adolescents with asthma and healthy controls. Methods: Cross-sectional study was conducted on 150 asthmatic adolescents and 300 controls. Adolescents completed a self-administered questionnaire regarding painful symptoms, use of electronic devices, and physical activity. Seven musculoskeletal pain syndromes were evaluated, and Asthma Control Test (ACT) was assessed. Results: Musculoskeletal pain (42% vs. 61%, p = 0.0002) and musculoskeletal pain syndromes (2.7% vs. 15.7%, p = 0.0006) were significantly lower in asthmatic adolescents than in controls. The frequency of pain in the hands and wrists was reduced in asthmatic than in controls (12.6% vs. 31.1%, p = 0.004), in addition to cell phone use (80% vs. 93%, p < 0.0001), simultaneous use of at least two electronic media (47% vs. 91%, p < 0.0001), myofascial syndrome (0% vs. 7.1%, p = 0.043), and tendinitis (0% vs. 9.2%, p = 0.008). Logistic regression analysis, including asthma with musculoskeletal pain as the dependent variable, and female sex, ACT > 20, simultaneous use of at least two electronic devices, cell phone use, and weekends and weekdays of cell phone use, as independent variables, showed that female sex (odds ratio [OR], 2.06; 95% confidence interval [CI], 1.929-6.316; p = 0.0009) and ACT ≥ 20 (OR, 0.194; 95% CI, 0.039-0.967; p = 0.045) were associated with asthma and musculoskeletal pain (Nagelkerke R2 = 0.206). Conclusion: Musculoskeletal pain and musculoskeletal pain syndromes were lower in adolescents with asthma. Female sex was associated with musculoskeletal pain in asthmatic, whereas patients with asthma symptoms and well-controlled disease reported a lower prevalence of musculoskeletal pain.

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Efficient computing techniques allow the estimation of variance components for virtually any traditional dataset. When genomic information is available, variance components can be estimated using genomic REML (GREML). If only a portion of the animals have genotypes, single-step GREML (ssGREML) is the method of choice. The genomic relationship matrix (G) used in both cases is dense, limiting computations depending on the number of genotyped animals. The algorithm for proven and young (APY) can be used to create a sparse inverse of G (GAPY~-1) with close to linear memory and computing requirements. In ssGREML, the inverse of the realized relationship matrix (H-1) also includes the inverse of the pedigree relationship matrix, which can be dense with a long pedigree, but sparser with short. The main purpose of this study was to investigate whether costs of ssGREML can be reduced using APY with truncated pedigree and phenotypes. We also investigated the impact of truncation on variance components estimation when different numbers of core animals are used in APY. Simulations included 150K animals from 10 generations, with selection. Phenotypes (h2 = 0.3) were available for all animals in generations 1-9. A total of 30K animals in generations 8 and 9, and 15K validation animals in generation 10 were genotyped for 52,890 SNP. Average information REML and ssGREML with G-1 and GAPY~-1 using 1K, 5K, 9K, and 14K core animals were compared. Variance components are impacted when the core group in APY represents the number of eigenvalues explaining a small fraction of the total variation in G. The most time-consuming operation was the inversion of G, with more than 50% of the total time. Next, numerical factorization consumed nearly 30% of the total computing time. On average, a 7% decrease in the computing time for ordering was observed by removing each generation of data. APY can be successfully applied to create the inverse of the genomic relationship matrix used in ssGREML for estimating variance components. To ensure reliable variance component estimation, it is important to use a core size that corresponds to the number of largest eigenvalues explaining around 98% of total variation in G. When APY is used, pedigrees can be truncated to increase the sparsity of H and slightly reduce computing time for ordering and symbolic factorization, with no impact on the estimates.

The estimation of variance components is computationally expensive under large-scale genetic evaluations due to several inversions of the coefficient matrix. Variance components are used as parameters for estimating breeding values in mixed model equations (MME). However, resulting breeding values are not Best Linear Unbiased Predictions (BLUP) unless the variance components approach the true parameters. The increasing availability of genomic data requires the development of new methods for improving the efficiency of variance component estimations. Therefore, this study aimed to reduce the costs of single-step genomic REML (ssGREML) with the Algorithm for Proven and Young (APY) for estimating variance components with truncated pedigree and phenotypes using simulated data. In addition, we investigated the influence of truncation on variance components and genetic parameter estimates. Under APY, the size of the core group influences the similarity of breeding values and their reliability compared to the full genomic matrix. In this study, we found that to ensure reliable variance component estimation, it is required to consider a core size that corresponds to the number of largest eigenvalues explaining around 98% of the total variation in G to avoid biased parameters. In terms of costs, the use of APY slightly decreased the time for ordering and symbolic factorization with no impact on estimations.

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Genomic prediction has become the new standard for genetic improvement programs, and currently, there is a desire to implement this technology for the evaluation of Angus cattle in Brazil. Thus, the main objective of this study was to assess the feasibility of evaluating young Brazilian Angus (BA) bulls and heifers for 12 routinely recorded traits using single-step genomic BLUP (ssGBLUP) with and without genotypes from American Angus (AA) sires. The second objective was to obtain estimates of effective population size (Ne) and linkage disequilibrium (LD) in the Brazilian Angus population. The dataset contained phenotypic information for up to 277,661 animals belonging to the Promebo breeding program, pedigree for 362,900, of which 1,386 were genotyped for 50k, 77k, and 150k single nucleotide polymorphism (SNP) panels. After imputation and quality control, 61,666 SNPs were available for the analyses. In addition, genotypes from 332 American Angus (AA) sires widely used in Brazil were retrieved from the AA Association database to be used for genomic predictions. Bivariate animal models were used to estimate variance components, traditional EBV, and genomic EBV (GEBV). Validation was carried out with the linear regression method (LR) using young-genotyped animals born between 2013 and 2015 without phenotypes in the reduced dataset and with records in the complete dataset. Validation animals were further split into progeny of BA and AA sires to evaluate if their progenies would benefit by including genotypes from AA sires. The Ne was 254 based on pedigree and 197 based on LD, and the average LD (±SD) and distance between adjacent single nucleotide polymorphisms (SNPs) across all chromosomes were 0.27 (±0.27) and 40743.68 bp, respectively. Prediction accuracies with ssGBLUP outperformed BLUP for all traits, improving accuracies by, on average, 16% for BA young bulls and heifers. The GEBV prediction accuracies ranged from 0.37 (total maternal for weaning weight and tick count) to 0.54 (yearling precocity) across all traits, and dispersion (LR coefficients) fluctuated between 0.92 and 1.06. Inclusion of genotyped sires from the AA improved GEBV accuracies by 2%, on average, compared to using only the BA reference population. Our study indicated that genomic information could help us to improve GEBV accuracies and hence genetic progress in the Brazilian Angus population. The inclusion of genotypes from American Angus sires heavily used in Brazil just marginally increased the GEBV accuracies for selection candidates.

There was a desire to implement genomic selection for Angus cattle in Brazil since the technology has been proved to increase genetic gain in animal breeding programs. Single-step genomic best linear unbiased prediction (ssGBLUP), which simultaneously combines pedigree and genomic information, was used to estimate individuals' genomic breeding values (GEBV) or genetic merit. Genomic selection can accelerate genetic progress by increasing accuracy, especially in young animals without progeny. The accuracy of GEBV can also be improved by combing data from other countries to increase the reference population (i.e., genotyped and phenotyped animals) in small, genotyped populations. Thus, the main objective of this study was to evaluate the accuracy of GEBV for young Brazilian Angus (BA) bulls and heifers with ssGBLUP, including or not the genotypes from American Angus sires. The accuracies with ssGBLUP were higher than those from traditional BLUP (EBV calculated from pedigree), improving accuracies by, on average, 16% for young bulls and heifers. Including genotypes from American Angus sires heavily used in Brazil just marginally increased the GEBV accuracies for selection candidates.

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OBJECTIVE: To evaluate idiopathic musculoskeletal pain, musculoskeletal pain syndromes, and use of electronic devices in adolescents with asthma and healthy controls. METHODS: Cross-sectional study was conducted on 150 asthmatic adolescents and 300 controls. Adolescents completed a self-administered questionnaire regarding painful symptoms, use of electronic devices, and physical activity. Seven musculoskeletal pain syndromes were evaluated, and Asthma Control Test (ACT) was assessed. RESULTS: Musculoskeletal pain (42% vs. 61%, p = 0.0002) and musculoskeletal pain syndromes (2.7% vs. 15.7%, p = 0.0006) were significantly lower in asthmatic adolescents than in controls. The frequency of pain in the hands and wrists was reduced in asthmatic than in controls (12.6% vs. 31.1%, p = 0.004), in addition to cell phone use (80% vs. 93%, p < 0.0001), simultaneous use of at least two electronic media (47% vs. 91%, p < 0.0001), myofascial syndrome (0% vs. 7.1%, p = 0.043), and tendinitis (0% vs. 9.2%, p = 0.008). Logistic regression analysis, including asthma with musculoskeletal pain as the dependent variable, and female sex, ACT > 20, simultaneous use of at least two electronic devices, cell phone use, and weekends and weekdays of cell phone use, as independent variables, showed that female sex (odds ratio [OR], 2.06; 95% confidence interval [CI], 1.929-6.316; p = 0.0009) and ACT ≥ 20 (OR, 0.194; 95% CI, 0.039-0.967; p = 0.045) were associated with asthma and musculoskeletal pain (Nagelkerke R2 = 0.206). CONCLUSIONS: Musculoskeletal pain and musculoskeletal pain syndromes were lower in adolescents with asthma. Female sex was associated with musculoskeletal pain in asthmatic, whereas patients with asthma symptoms and well-controlled disease reported a lower prevalence of musculoskeletal pain.

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The level of genetic diversity in a population is inversely proportional to the linkage disequilibrium (LD) between individual single nucleotide polymorphisms (SNPs) and quantitative trait loci (QTLs), leading to lower predictive ability of genomic breeding values (GEBVs) in high genetically diverse populations. Haplotype-based predictions could outperform individual SNP predictions by better capturing the LD between SNP and QTL. Therefore, we aimed to evaluate the accuracy and bias of individual-SNP- and haplotype-based genomic predictions under the single-step-genomic best linear unbiased prediction (ssGBLUP) approach in genetically diverse populations. We simulated purebred and composite sheep populations using literature parameters for moderate and low heritability traits. The haplotypes were created based on LD thresholds of 0.1, 0.3, and 0.6. Pseudo-SNPs from unique haplotype alleles were used to create the genomic relationship matrix ( G ) in the ssGBLUP analyses. Alternative scenarios were compared in which the pseudo-SNPs were combined with non-LD clustered SNPs, only pseudo-SNPs, or haplotypes fitted in a second G (two relationship matrices). The GEBV accuracies for the moderate heritability-trait scenarios fitting individual SNPs ranged from 0.41 to 0.55 and with haplotypes from 0.17 to 0.54 in the most (Ne ≅ 450) and less (Ne < 200) genetically diverse populations, respectively, and the bias fitting individual SNPs or haplotypes ranged between -0.14 and -0.08 and from -0.62 to -0.08, respectively. For the low heritability-trait scenarios, the GEBV accuracies fitting individual SNPs ranged from 0.24 to 0.32, and for fitting haplotypes, it ranged from 0.11 to 0.32 in the more (Ne   ≅ 250) and less (Ne   ≅ 100) genetically diverse populations, respectively, and the bias ranged between -0.36 and -0.32 and from -0.78 to -0.33 fitting individual SNPs or haplotypes, respectively. The lowest accuracies and largest biases were observed fitting only pseudo-SNPs from blocks constructed with an LD threshold of 0.3 (p < 0.05), whereas the best results were obtained using only SNPs or the combination of independent SNPs and pseudo-SNPs in one or two G matrices, in both heritability levels and all populations regardless of the level of genetic diversity. In summary, haplotype-based models did not improve the performance of genomic predictions in genetically diverse populations.

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Metafounders are pseudo-individuals that act as proxies for animals in base populations. When metafounders are used, individuals from different breeds can be related through pedigree, improving the compatibility between genomic and pedigree relationships. The aim of this study was to investigate the use of metafounders and unknown parent groups (UPGs) for the genomic evaluation of a composite beef cattle population. Phenotypes were available for scrotal circumference at 14 months of age (SC14), post weaning gain (PWG), weaning weight (WW), and birth weight (BW). The pedigree included 680,551 animals, of which 1,899 were genotyped for or imputed to around 30,000 single-nucleotide polymorphisms (SNPs). Evaluations were performed based on pedigree (BLUP), pedigree with UPGs (BLUP_UPG), pedigree with metafounders (BLUP_MF), single-step genomic BLUP (ssGBLUP), ssGBLUP with UPGs for genomic and pedigree relationship matrices (ssGBLUP_UPG) or only for the pedigree relationship matrix (ssGBLUP_UPGA), and ssGBLUP with metafounders (ssGBLUP_MF). Each evaluation considered either four or 10 groups that were assigned based on breed of founders and intermediate crosses. To evaluate model performance, we used a validation method based on linear regression statistics to obtain accuracy, stability, dispersion, and bias of (genomic) estimated breeding value [(G)EBV]. Overall, relationships within and among metafounders were stronger in the scenario with 10 metafounders. Accuracy was greater for models with genomic information than for BLUP. Also, the stability of (G)EBVs was greater when genomic information was taken into account. Overall, pedigree-based methods showed lower inflation/deflation (regression coefficients close to 1.0) for SC14, WWM, and BWD traits. The level of inflation/deflation for genomic models was small and trait-dependent. Compared with regular ssGBLUP, ssGBLUP_MF4 displayed regression coefficient closer to one SC14, PWG, WWM, and BWD. Genomic models with metafounders seemed to be slightly more stable than models with UPGs based on higher similarity of results with different numbers of groups. Further, metafounders can help to reduce bias in genomic evaluations of composite beef cattle populations without reducing the stability of GEBVs.

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The multiple sire system (MSS) is a common mating scheme in extensive beef production systems. However, MSS does not allow paternity identification and lead to inaccurate genetic predictions. The objective of this study was to investigate the implementation of single-step genomic BLUP (ssGBLUP) in different scenarios of uncertain paternity in the evaluation for 450-day adjusted liveweight (W450) and age at first calving (AFC) in a Nellore cattle population. To estimate the variance components using BLUP and ssGBLUP, the relationship matrix (A) with different proportions of animals with missing sires (MS) (scenarios 0, 25, 50, 75, and 100% of MS) was created. The genotyped animals with MS were randomly chosen, and ten replicates were performed for each scenario and trait. Five groups of animals were evaluated in each scenario: PHE, all animals with phenotypic records in the population; SIR, proven sires; GEN, genotyped animals; YNG, young animals without phenotypes and progeny; and YNGEN, young genotyped animals. The additive genetic variance decreased for both traits as the proportion of MS increased in the population when using the regular REML. When using the ssGBLUP, accuracies ranged from 0.13 to 0.47 for W450 and from 0.10 to 0.25 for AFC. For both traits, the prediction ability of the direct genomic value (DGV) decreased as the percentage of MS increased. These results emphasize that indirect prediction via DGV of young animals is more accurate when the SNP effects are derived from ssGBLUP with a reference population with known sires. The ssGBLUP could be applied in situations of uncertain paternity, especially when selecting young animals. This methodology is shown to be accurate, mainly in scenarios with a high percentage of MS.

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OBJECTIVE: To assess overall adrenal mineralocorticoid/glucocorticoid/androgen steroidogenesis in childhood-onset systemic lupus erythematosus (cSLE) patients and the possible effect of prednisone on adrenal hormones and ovarian reserve. METHODS: Fifty-one adult cSLE (ACR criteria) patients and 23 healthy controls were evaluated for adrenal steroidogenesis including mineralocorticoid (progesterone, deoxycorticosterone, aldosterone), glucocorticoid (17-OHprogesterone, 11-desoxycortisol, cortisol), and androgen (dehydroepiandrosterone-sulfate, androstenedione, total testosterone, and dihydrotestosterone) hormones. Ovarian reserve assessment included follicle-stimulating hormone (FSH), estradiol, anti-Müllerian hormone, ovarian volumes, and antral follicle count. RESULTS: The median of current age [29.11 (19-39.8) vs. 30.8 (19.6-42.1) years, p = 0.502] was similar in adult cSLE and controls. Regarding mineralocorticoid/glucocorticoid, the median of progesterone (p = 0.003), 17-OH progesterone (p < 0.001), and 11-desoxycortisol (p = 0.036) were significantly lower in patients compared to controls. All androgen steroidogenesis hormones were reduced in the former group [dehydroepiandrosterone-sulfate (p < 0.001), androstenedione (p = 0.001), total testosterone (p = 0.005), and dihydrotestosterone (p < 0.001)]. Further comparison of patients with and without current use of prednisone and controls revealed a predominant impact on adrenal glucocorticoid and androgen steroidogenesis with reduced levels of 17-OH progesterone [0.17 (0-0.5) vs. 0.27 (0.1-2.9) vs. 0.33 (0.1-0.8) ng/mL, p < 0.001], dehydroepiandrosterone-sulfate [0.155 (0-0.6) vs. 0.49 (0.1-1.6) vs. 1.11 (0.1-2.6) µg/mL, p < 0.001], androstenedione [0.56 (0.2-4.4) vs. 1.7 (0.5-4.5) vs. 2.33 (0.3-3.8) ng/mL, p < 0.001], total testosterone [12 (12-167) vs. 16 (12-28) vs. (16.5 (0-50) ng/d, p = 0.002], and dihydrotestosterone [92.68 (11.8-198.5) vs. 160.62 (37.9-842.1) vs. 188.3 (71.3-543.9) pg/ml, p < 0.001] in patients under this drug. In addition, patients with this therapy had reduced median ovarian volumes [4.14 (2-12) vs. 7.13 (2-25.7) vs. 5.18 (2.4-17.3) cm3, p = 0.028) that was not associated with cyclophosphamide cumulative dose (p > 0.05). The median prednisone dose was 15/mg/day (2.5-40). CONCLUSIONS: We provided novel evidence that cSLE patients have an overall androgen/glucocorticoid/mineralocorticoid adrenal suppression. Furthermore, low/moderate prednisone use seems to underlie these abnormalities and may also adversely affect ovarian reserve, independently of immunosuppressants. Key Points • cSLE patients have an overall androgen/glucocorticoid/mineralocorticoid adrenal suppression. • Low/moderate prednisone use may affect ovarian reserve, independently of immunosuppressants.

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We have previously reported that ß-(1→3,1→6)-ᴅ-glucans produced by endophytes Diaporthe sp. G27-60 and G65-65 (GenBank accession codes JF766998 and JF767007, respectively) are promising anti-proliferation agents against human breast carcinoma (MCF-7) and hepatocellular carcinoma (HepG2-C3A) cells. However, the literature fails to describe the effects of Diaporthe exopolysaccharides (EPS) on eukaryotic healthy cells. The fungus Metarhiziumanisopliae has been employed as model-system to evaluate the toxicity of pharmaceutical and agricultural-interest substances, taking into account, among other parameters, the speed of conidia germination. Current study verified the effect of different concentrations of Diaporthe ß-glucans on the germination speed of M. anisopliae. Conidia were incubated with ß-glucans treatments (50, 200 and 400 µg/mL) at 28ºC, sampled during 24 h and analyzed by light microscopy. At the end of a 24-h incubation, the amount of germinated conidia reached ≈99% for controls and ranged between 97.7 and 98.6% for treatments. Bayesian analysis indicated that Diaporthe glucans had no toxicity on M. anisopliaeand the curve of germination occurred as expected for this fungal strain. Considering the validity of filamentous fungi as model-systems, results are important data on the toxicity of endophytic EPS on healthy cells and may be associated with our previous results obtained for these polymers against tumor cells.

Anteriormente, um estudo mostrou que ß-(1→3,1→6)-ᴅ-glucanas produzidas pelos endófitos Diaporthe sp. G27-60 e G65-65 (códigos de acesso no GenBank JF766998 e JF767007, respectivamente) são agentes promissores com ação antiproliferativa contra células HepG2-C3A (hepatoma humano) e MCF-7 (adenocarcinoma mamário humano). No entanto, os efeitos de exopolissacarídeos (EPS) produzidos por fungos do gênero Diaporthe em células eucarióticas sadias não estão descritos na literatura atual. O fungo Metarhiziumanisopliae tem sido utilizado como sistema-modelo para avaliar a toxicidade de substâncias de interesse farmacêutico e agronômico, considerando, entre outros parâmetros, a velocidade de germinação de conídios. O presente estudo teve como objetivo verificar os efeitos de diferentes concentrações de ß-glucanas produzidas por Diaporthe sp. sobre a velocidade de germinação de M. anisopliae. Os conídios foram incubados com os tratamentos de ß-glucanas (50, 200 e 400 µg/mL) a 28 ºC, com amostras coletadas ao longo de 24 h, e analisados por microscopia de luz. Ao final das 24 h de incubação, o total de conídios germinados nos controles foi de ≈99%, e variou entre 97,7 e 98,6% para os tratamentos. A análise bayesiana indicou que as glucanas de Diaporthe sp. não apresentaram toxicidade sobre M. anisopliae, e a curva de germinação atendeu ao esperado para essa linhagem fúngica. Considerando a validade dos fungos filamentosos como sistemas-modelo, esses resultados representam dados importantes sobre a toxicidade dos EPS de endófitos sobre células sadias e podem ser associados aos resultados anteriormente obtidos para esses polímeros em testes contra células tumorais.

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Pedigree information is incomplete by nature and commonly not well-established because many of the genetic ties are not known a priori or can be wrong. The genomic era brought new opportunities to assess relationships between individuals. However, when pedigree and genomic information are used simultaneously, which is the case of single-step genomic BLUP (ssGBLUP), defining the genetic base is still a challenge. One alternative to overcome this challenge is to use metafounders, which are pseudo-individuals that describe the genetic relationship between the base population individuals. The purpose of this study was to evaluate the impact of metafounders on the estimation of breeding values for tick resistance under ssGBLUP for a multibreed population composed by Hereford, Braford, and Zebu animals. Three different scenarios were studied: pedigree-based model (BLUP), ssGBLUP, and ssGBLUP with metafounders (ssGBLUPm). In ssGBLUPm, a total of four different metafounders based on breed of origin (i.e., Hereford, Braford, Zebu, and unknown) were included for the animals with missing parents. The relationship coefficient between metafounders was in average 0.54 (ranging from 0.34 to 0.96) suggesting an overlap between ancestor populations. The estimates of metafounder relationships indicate that Hereford and Zebu breeds have a possible common ancestral relationship. Inbreeding coefficients calculated following the metafounder approach had less negative values, suggesting that ancestral populations were large enough and that gametes inherited from the historical population were not identical. Variance components were estimated based on ssGBLUPm, ssGBLUP, and BLUP, but the values from ssGBLUPm were scaled to provide a fair comparison with estimates from the other two models. In general, additive, residual, and phenotypic variance components in the Hereford population were smaller than in Braford across different models. The addition of genomic information increased heritability for Hereford, possibly because of improved genetic relationships. As expected, genomic models had greater predictive ability, with an additional gain for ssGBLUPm over ssGBLUP. The increase in predictive ability was greater for Herefords. Our results show the potential of using metafounders to increase accuracy of GEBV, and therefore, the rate of genetic gain in beef cattle populations with partial levels of missing pedigree information.

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Estimates of dominance variance for growth traits in beef cattle based on pedigree data vary considerably across studies, and the proportion of genetic variance explained by dominance deviations remains largely unknown. The potential benefits of including nonadditive genetic effects in the genomic model combined with the increasing availability of large genomic data sets have recently renewed the interest in including nonadditive genetic effects in genomic evaluation models. The availability of genomic information enables the computation of covariance matrices of dominant genomic relationships among animals, similar to matrices of additive genomic relationships, and in a more straightforward manner than the pedigree-based dominance relationship matrix. Data from 19,357 genotyped American Angus males were used to estimate additive and dominant variance components for 3 growth traits: birth weight, weaning weight, and postweaning gain, and to evaluate the benefit of including dominance effects in beef cattle genomic evaluations. Variance components were estimated using 2 models: the first one included only additive effects (MG) and the second one included both additive and dominance effects (MGD). The dominance deviation variance ranged from 3% to 8% of the additive variance for all 3 traits. Gibbs sampling and REML estimates showed good concordance. Goodness of fit of the models was assessed by a likelihood ratio test. For all traits, MG fitted the data as well as MGD as assessed either by the likelihood ratio test or by the Akaike information criterion. Predictive ability of both models was assessed by cross-validation and did not improve when including dominance effects in the model. There was little evidence of nonadditive genetic variation for growth traits in the American Angus male population as only a small proportion of genetic variation was explained by nonadditive effects. A genomic model including the dominance effect did not improve the model fit. Consequently, including nonadditive effects in the genomic evaluation model is not beneficial for growth traits in the American Angus male population.

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ABSTRACT Objective: To evaluate the effect of educational strategies on sleep quality and its relation to diabetes-related distress and glycemic control in people with type 2 diabetes mellitus (DM2). Materials and methods: Randomized clinical trial involving two groups. Group 1 (G1, n = 45) received verbal guidance and leaflets on sleep hygiene strategies and group 2 (G2, n = 46) received usual health care guidelines on self-care with the feet. Sleep was assessed by the Pittsburgh Sleep Quality Inventory and diabetes-related distress by the Diabetes Distress Scale. Linear mixed-effects models and linear regression model were used for the statistical analysis. Results: At the end of the follow-up, sleep quality improvement (p = 0.02) was verified in G1. Low diabetes-related distress score (p = 0.03), being male (p = 0.02), belonging to G1 (p = 0.002), and age (p = 0.04) contributed to better sleep quality. Conclusion: Educational guidelines on sleep hygiene in patients with DM2 were effective in improving sleep quality, measured by the PSQI instrument and emotional stress related to diabetes as assessed by the Diabetes Distress Scale.

RESUMEN Objetivo: evaluar el efecto de estrategias educativas en la calidad del sueño y su relación con el estrés emocional asociado con diabetes en personas con DM2. Materiales y método: ensayo clínico aleatorizado con dos grupos. El grupo 1 (G1, n = 45) tuvo orientaciones verbales y folletos acerca de estrategias de higiene del sueño; el grupo 2 (G2, n = 46), orientaciones usuales de las unidades de salud sobre autocuidado con los pies. El sueño se evaluó por el Pittsburgh Sleep Quality Inventory (PSQI-BR), y el estrés emocional, por el Diabetes Distress Scale (B-DDS). Se emplearon modelos lineales de efectos mixtos y modelo de regresión lineal para análisis estadísticas. Resultados: al término del seguimiento, se encontró mejoría de la calidad del sueño (p = 0,02) en el G1. Bajo índice de estrés emocional (p = 0,03), ser del sexo masculino (p = 0,02), pertenecer al G1 (p = 0,002) y edad (p = 0,04) contribuyeron a mejor calidad del sueño. Conclusión: orientaciones educativas acerca de la higiene del sueño en pacientes con DM2 fueron efectivas en la mejoría de la puntuación de la calidad del sueño, mensurada por el instrumento PSQI-BR, y del estrés emocional relacionado con diabetes, evaluado por el instrumento del B-DDS.

RESUMO Objetivo: avaliar o efeito de estratégias educativas sobre a qualidade do sono e sua relação com o estresse emocional associado com o diabetes em pessoas com DM2. Materiais e método: ensaio clínico randomizado com dois grupos. O grupo 1 (G1, n = 45) recebeu orientações verbais e folhetos sobre estratégias de higiene do sono; o grupo 2 (G2, n = 46), orientações usuais das unidades de saúde sobre autocuidado com os pés. O sono foi avaliado pelo Pittsburgh Sleep Quality Inventory (PSQI-BR), e o estresse emocional, pelo Diabetes Distress Scale (B-DDS). Utilizaram-se modelos lineares de efeitos mistos e modelo de regressão linear para as análises estatísticas. Resultados: no final do seguimento, verificou-se melhora da qualidade do sono (p = 0,02) no G1. Baixo escore de estresse emocional (p = 0,03), ser do sexo masculino (p = 0,02), pertencer ao G1 (p = 0,002) e idade (p = 0,04) contribuíram para melhor qualidade do sono. Conclusão: orientações educativas sobre a higiene do sono em pacientes com DM2 foram efetivas na melhora da pontuação da qualidade do sono, mensurada pelo instrumento PSQI-BR, e do diabetes-related emotional distress, avaliado pelo instrumento do B-DDS.

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The objective of this study was to investigate the impact of accounting for parent average (PA) and genotyped daughters' average (GDA) on the estimation of deregressed estimated breeding values (dEBVs) used as pseudo-phenotypes in multiple-step genomic evaluations. Genomic estimated breeding values (GEBVs) were predicted, in eight different simulated scenarios, using dEBVs calculated based on four methods. These methods included PA and GDA in the dEBV (VR) or only GDA (VRpa) and excluded both PA and GDA from the dEBV with either all information or only information from PA and GDA (JA and NEW, respectively). In general, VR and NEW showed the lowest and highest GEBV reliabilities across scenarios, respectively. Among all deregression methods, VRpa and NEW provided the most consistent bias estimates across the majority of scenarios, and they significantly yielded the least biased GEBVs. Our results indicate that removing PA and GDA information from dEBVs used in multiple-step genomic evaluations can increase the reliability of GEBVs, when both bulls and their daughters are included in the training population.

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BACKGROUND: Single-step genomic best linear unbiased prediction (SSGBLUP) is a comprehensive method for genomic prediction. Point estimates of marker effects from SSGBLUP are often used for genome-wide association studies (GWAS) without a formal framework of hypothesis testing. Our objective was to implement p-values for single-marker GWAS studies within the single-step GWAS (SSGWAS) framework by deriving computational algorithms and procedures, and by applying these to a large beef cattle population. METHODS: P-values were obtained based on the prediction error (co)variances for single nucleotide polymorphisms (SNPs), which were obtained from the prediction error (co)variances of genomic predictions based on the inverse of the coefficient matrix and formulas to estimate SNP effects. RESULTS: Computation of p-values took a negligible time for a dataset with almost 2 million animals in the pedigree and 1424 genotyped sires, and no inflation of statistics was observed. The SNPs that passed the Bonferroni threshold of 10-5.9 were the same as those that explained the highest proportion of additive genetic variance, but even at the same significance levels and effects, some of them explained less genetic variance due to lower allele frequency. CONCLUSIONS: The use of a p-value for SSGWAS is a very general and efficient strategy to identify quantitative trait loci (QTL). It can be used for complex datasets such as those used in animal breeding, where only a proportion of the pedigreed animals are genotyped.

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Pooling semen of multiple boars is commonly used in swine production systems. Compared with single boar systems, this technique changes family structure creating maternal half-sib families. The aim of this simulation study was to investigate how pooling semen affects the accuracy of estimating direct and maternal effects for individual piglet birth weight, in purebred pigs. Different scenarios of pooling semen were simulated by allowing the same female to mate from 1 to 6 boars, per insemination, whereas litter size was kept constant (N = 12). In each pooled boar scenario, genomic information was used to construct either the genomic relationship matrix (G) or to reconstruct pedigree in addition to G. Genotypes were generated for 60,000 SNPs evenly distributed across 18 autosomes. From the 5 simulated generations, only animals from generations 3 to 5 were genotyped (N = 36,000). Direct and maternal true breeding values (TBV) were computed as the sum of the effects of the 1,080 QTLs. Phenotypes were constructed as the sum of direct TBV, maternal TBV, an overall mean of 1.25 kg, and a residual effect. The simulated heritabilities for direct and maternal effects were 0.056 and 0.19, respectively, and the genetic correlation between both effects was -0.25. All simulations were replicated 5 times. Variance components and direct and maternal heritability were estimated using average information REML. Predictions were computed via pedigree-based BLUP and single-step genomic BLUP (ssGBLUP). Genotyped littermates in the last generation were used for validation. Prediction accuracies were calculated as correlations between EBV and TBV for direct (accdirect) and maternal (accmat) effects. When boars were known, accdirect were 0.21 (1 boar) and 0.26 (6 boars) for BLUP, whereas for ssGBLUP, they were 0.38 (1 boar) and 0.43 (6 boars). When boars were unknown, accdirect was lower in BLUP but similar in ssGBLUP. For the scenario with known boars, accmat was 0.58 and 0.63 for 1 and 6 boars, respectively, under ssGBLUP. For unknown boars, accmat was 0.63 for 2 boars and 0.62 for 6 boars in ssGBLUP. In general, accdirect and accmat were lower in the single-boar scenario compared with pooled semen scenarios, indicating that a half-sib structure is more adequate to estimate direct and maternal effects. Using pooled semen from multiple boars can help us to improve accuracy of predicting maternal and direct effects when maternal half-sib families are larger than 2.

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