Inferência bayesiana aplicada à estimação de herdabilidades dos parâmetros da curva de crescimento de fêmeas da raça Nelore

Objetivou-se estimar parâmetros genéticos, utilizando inferência Bayesiana, para as estimativas dos parâmetros individuais de peso à maturidade (Â) e taxa de crescimento, obtidos pela função de crescimento Brody. O arquivo estava constituído de 14.563 registros de pesos e idades referentes a 1.158 fêmeas da raça Nelore, participantes do Programa de Melhoramento Genético da Raça Nelore. Para a análise das estimativas dos parâmetros da curva, via inferência bayesiana, foi proposto um modelo animal unicaráter, que incluiu como fixo o efeito de grupo contemporâneo (animais nascidos no mesmo estado, no mesmo trimestre do ano, mesmo ano e mesmo regime alimentar) e como aleatórios os efeitos genético direto e residual. Nessa análise, foram utilizados dois diferentes tamanhos para as cadeias geradas pelo algoritmo de amostragem de Gibbs, de 550 e 1.100 mil ciclos, com períodos de descarte amostral de 50 e 100 mil ciclos, respectivamente, e amostragens a cada 500 e 1.000 ciclos, respectivamente. As médias posteriores da variância genética aditiva e residual foram próximas, tanto para  quanto para a, mesmo quando implementados diferentes tamanhos para as cadeias geradas pelo algoritmo de amostragem de Gibbs. Os coeficientes de herdabilidade estimados para Â, variaram de 0,44 a 0,46, amplitude semelhante aos 0,46 a 0,48 obtidos para as estimativas de. Essas magnitudes indicam que a seleção pode ser usada como instrumento para alterar a forma da curva de crescimento desses animais. Entretanto, o uso das informações obtidas, visando à alteração da curva de crescimento dos animais, deve ser feito com grande cautela, uma vez que as características a serem trabalhadas na modificação do formato da curva de crescimento, de acordo com resultados da literatura especializada, são negativamente correlacionadas.The objective of this study was to estimate genetic parameters using Bayesian inference for the estimates of individual parameters of mature weight (Â) and growth rate, obtained by Brody growth function. The file consisted of 14,563 records relating to weights and ages of 1,158 Nelore females, participants in the Genetic Improvement Program of the Nellore. For the estimates analysis of the curve parameters via Bayesian inference, it was used an univariated animal model that included as fixed effect of contemporary group (animals born on the same state, in the same quarter of the year, the same year and feedlot) and random as the direct genetic and residual. In this analysis it was used two different sizes for the chains generated by Gibbs sampling algorithm, 550 and 1.1 million cycles, with initial discarding of 50 000 and sample of 100 000 cycles, respectively, and sampled every 500 cycles and 1000, respectively. The mean posterior and residual additive genetic variance showed a small variation to both  and, even when implemented in different sizes for the chains generated by Gibbs sampling algorithm. The heritability estimates for Â, ranged from 0.44 to 0.46, similar to the range 0.46 to 0.48 obtained for the estimates of. These magnitudes indicate that the selection can be used as a tool to change the shape of the growth curve of these animals. However, the use of the information to amend the growth curve of animals, must be done with great care, since the traits to be worked in the modification of the shape of the growth curve according to the literature results are negatively correlated.Campus de São Gabriel Universidade Federal do Pampa (UNIPAMPA)Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA) Pecuária SudesteUniversidade de Brasília (UnB) Faculdade de Agronomia e Medicina Veterinária (FAV)UNESP Faculdade de Ciências Agrárias e Veterinárias (FCAV) Departamento de ZootecniaAssociação Nacional de Criadores e Pesquisadores (ANCP)UNESP Faculdade de Ciências Agrárias e Veterinárias (FCAV) Departamento de Zootecni

Comparison of Prediction Models for Lynch Syndrome among Individuals with Colorectal Cancer

BACKGROUND: Recent guidelines recommend the Lynch Syndrome prediction models MMRPredict, MMRPro, and PREMM1,2,6 for the identification of MMR gene mutation carriers. We compared the predictive performance and clinical usefulness of these prediction models to identify mutation carriers. METHODS: Pedigree data from CRC patients in 11 North American, European, and Australian cohorts (6 clinic- and 5 population-based sites) were used to calculate predicted probabilities of pathogenic MLH1, MSH2, or MSH6 gene mutations by each model and gene-specific predictions by MMRPro and PREMM1,2,6. We examined discrimination with area under the receiver operating characteristic curve (AUC), calibration with observed to expected (O/E) ratio, and clinical usefulness using decision curve analysis to select patients for further evaluation. All statistical tests were two-sided. RESULTS: Mutations were detected in 539 of 2304 (23%) individuals from the clinic-based cohorts (237 MLH1, 251 MSH2, 51 MSH6) and 150 of 3451 (4.4%) individuals from the population-based cohorts (47 MLH1, 71 MSH2, 32 MSH6). Discrimination was similar for clinic- and population-based cohorts: AUCs of 0.76 vs 0.77 for MMRPredict, 0.82 vs 0.85 for MMRPro, and 0.85 vs 0.88 for PREMM1,2,6. For clinic- and population-based cohorts, O/E deviated from 1 for MMRPredict (0.38 and 0.31, respectively) and MMRPro (0.62 and 0.36) but were more satisfactory for PREMM1,2,6 (1.0 and 0.70). MMRPro or PREMM1,2,6 predictions were clinically useful at thresholds of 5% or greater and in particular at greater than 15%. CONCLUSIONS: MMRPro and PREMM1,2,6 can well be used to select CRC patients from genetics clinics or population-based settings for tumor and/or germline testing at a 5% or higher risk. If no MMR deficiency is detected and risk exceeds 15%, we suggest considering additional genetic etiologies for the cause of cancer in the family