RESUMO O objetivo com este trabalho foi estudar características "dias para um peso específico" em programas de melhoramento genético da raça Nelore, as comparando com características tradicionais como de ganho de peso médio diário. Assim, neste trabalho foram estimados parâmetros genéticos para as características dias para atingir 180kg (D180), dias para atingir 300kg (D300), ganho de peso pré-desmama (gpmdND), ganho de peso no período da desmama ao sobreano (gpmdDS) para uma população de bovinos da raça Nelore. O banco de dados utilizado é constituído por 60005 animais no arquivo de pedigree e 37234 informações de fenótipos de crescimento. Os dados foram organizados e preparados com auxílio do programa R, e para estimação de componentes de (co)variância realizou-se análises bicaracterísticas com o auxílio do software Wombat. A progênie dos animais foi analisada quanto à variabilidade das diferenças de progênie (Deps) por um coeficiente de variação genético aditivo. As herdabilidades para gpmdND e D180 foram as de maior valor, 0,4 e 0,45 respectivamente. Encontrou-se altas correlações genéticas entre as características, gpmdND e D180 (0,99) e gpmdDS e D300 (0,95). D180 e gpmdDS apresentaram os menores coeficientes de variação. A seleção pelas características pré-desmama produzirá animais que em média levarão o mesmo tempo para chegarem ao peso à desmama. Porém para o peso ao sobreano D300 apresentou vantagem em média de 82,44 dias em relação à gpmdDS. Assim, um índice de seleção que inclua as características D180 e D300 deve produzir os melhores resultados com relação à uniformidade dos selecionados e à velocidade de ganho.

Background and Aims There is little information on the incremental prognostic importance of frailty beyond conventional prognostic variables in heart failure (HF) populations from different country income levels. Methods A total of 3429 adults with HF (age 61 ± 14 years, 33% women) from 27 high-, middle- and low-income countries were prospectively studied. Baseline frailty was evaluated by the Fried index, incorporating handgrip strength, gait speed, physical activity, unintended weight loss, and self-reported exhaustion. Mean left ventricular ejection fraction was 39 ± 14% and 26% had New York Heart Association Class III/IV symptoms. Participants were followed for a median (25th to 75th percentile) of 3.1 (2.0–4.3) years. Cox proportional hazard models for death and HF hospitalization adjusted for country income level; age; sex; education; HF aetiology; left ventricular ejection fraction; diabetes; tobacco and alcohol use; New York Heart Association functional class; HF medication use; blood pressure; and haemoglobin, sodium, and creatinine concentrations were performed. The incremental discriminatory value of frailty over and above the MAGGIC risk score was evaluated by the area under the receiver-operating characteristic curve. Results At baseline, 18% of participants were robust, 61% pre-frail, and 21% frail. During follow-up, 565 (16%) participants died and 471 (14%) were hospitalized for HF. Respective adjusted hazard ratios (95% confidence interval) for death among the pre-frail and frail were 1.59 (1.12–2.26) and 2.92 (1.99–4.27). Respective adjusted hazard ratios (95% confidence interval) for HF hospitalization were 1.32 (0.93–1.87) and 1.97 (1.33–2.91). Findings were consistent among different country income levels and by most subgroups. Adding frailty to the MAGGIC risk score improved the discrimination of future death and HF hospitalization. Conclusions Frailty confers substantial incremental prognostic information to prognostic variables for predicting death and HF hospitalization. The relationship between frailty and these outcomes is consistent across countries at all income levels.

This study aimed to evaluate the accuracy of genomic prediction with simulated data, using SNP markers, causal quantitative trait nucleotide (QTN), and the combination of both. The methods used were the best linear unbiased prediction (GBLUP) and single-step GBLUP (ssGBLUP), with alternative SNP weights. Data were simulated using the package AlphasimR. Trait heritability of 0.3 was assumed, and genetic variance was fully accounted for by 100 or 1000 QTNs. A population with an effective size of 200 was selected, and 20 generations were simulated. The genomic information mimicked the 29 bovine chromosomes and included 50k SNP markers evenly distributed across the genome. Approximately 16800 genotypes were available from selected sires and dams in generations 16–19, and 2000 animals in generation 20. Phenotypes for young animals were not included in the analysis, as they were used in the validation. For GBLUP, three pseudo-phenotypes were considered: the raw phenotype, the true breeding value, and the true breeding value with noise added. The genomic relationship matrix was weighted using quadratic weights, calculated based on the SNP variance, and non-linear A, following different equation parameters. The scenario with exclusively causal variants presented accuracies close to 1 for 100 QTL, and slightly lower in the 1000 QTL. For the SNP + QTN scenario, quadratic weights promoted higher accuracy gains than the SNPs alone, especially in the 100 QTN trait. Accuracies converged at higher values for both quadratic and non-linear A weights in the 100 QTN scenario. For the 1000 QTN trait, quadratic weights diverged and reduced accuracy, while non-linear A maintained accuracy at their peaks, depending on the equation parameters. Parameters of non-linear A for highest accuracy were different in each scenario and type of analysis. Proportionally, gains in accuracy were more prominent with GBLUP than with ssGBLUP.

The accuracy of genetic selection in dairy can be increased by the adoption of new technologies, such as the inclusion of sequence data. In simulation studies, assigning different weights to causative single‐nucleotide polymorphism (SNP) markers led to better predictions depending on the genomic prediction method used. However, it is still not clear how the weights should be calculated. Our objective was to evaluate the accuracy of a multi‐step method (GBLUP) and single‐step GBLUP with simulated data using regular SNP, causatives variants (QTN) and the combination of both. Additionally, we compared the accuracies of all previous scenarios using alternatives for SNP weighting. The data were simulated assuming a single trait with a heritability of 0.3. The effective population size (Ne) was approximately 200. The pedigree contained 440,000 animals, and approximately 16,800 individuals were genotyped. A total of 49,974 SNP markers were evenly placed throughout the genome, and 100, 1000 and 2000 causative QTN were simulated. Both GBLUP and ssGBLUP were used in this study. We evaluated quadratic and nonlinear SNP weights in addition to the unweighted G. The inclusion of QTN to panels led to significant accuracy gains. Nonlinear A was demonstrated to be superior to quadratic weighting and unweighted approaches; however, results from Nonlinear A were dependent on the equation parameters. The unweighted approach was more suitable for less polygenic scenarios. Finally, SNP weighting might help elucidate trait architecture features based on changes in the accuracy of genomic prediction.

Acasalamentos endogâmicos são comumente utilizados como estratégia para o incremento da uniformidade de rebanhos, por aumentar a frequência de genótipos homozigotos na população. Este estudo tem o objetivo de identificar efeitos da endogamia sobre a uniformidade de progênie em um rebanho comercial de gado de corte da raça Nelore. Um total de 3320 animais compôs o banco de genótipos, em três densidades de painéis (~30.00, ~75.000, ~780.000 SNPs). Os painéis em baixa densidade foram imputados para alta por meio do software Fimpute. Valores genéticos (VGs) foram estimados para 595.232 animais presentes no pedigree, e para nove características: Peso ao nascimento (PESNAS), peso à desmama (PESDES), peso aos 18 meses (PES18), ganho de peso pós desmama (GP345), circunferência escrotal aos 18 meses (CE), precocidade (PREC), musculosidade (MUSC), dias para se atingir 180 Kg (D180) e dias para se atingir 300 Kg (D300). A variabilidade dos VGs foi calculada entre os descentes de um mesmo touro (mínimo de 10), para cada característica, e então os mesmos touros foram separados em dois grupos: Alta Prepotência (AP) e Baixa Prepotência (BP). Os grupos foram comparados de acordo com os níveis de três coeficientes de endogamia, um calculado por meio de registros genealógicos (FPED), outro por meio de uma matriz de relacionamento genômico (FGRM) e outro ainda por meio de corridas de homozigose (FROH). Foram também comparados quanto à caracterização de suas corridas de homozigose (ROH). De modo geral os coeficientes de endogamia não se diferiram entre os grupos. Foram encontrados associações entre prepotência e endogamia nos grupos AP. Resultados indicam que a posição no genoma em que se encontra as ROH pode influenciar o nível de prepotência.