RESUMO
BACKGROUND/AIMS: Statistical analysis of age-at-onset involving family data is particularly complicated because there is a correlation pattern that needs to be modeled and also because there are measurements that are censored. In this paper, our main purpose was to evaluate the effect of genetic and shared family environmental factors on age-at-onset of three cardiovascular risk factors: hypertension, diabetes and high cholesterol. METHODS: The mixed-effects Cox model proposed by Pankratz et al. [2005] was used to analyze the data from 81 families, involving 1,675 individuals from the village of Baependi, in the state of Minas Gerais, Brazil. RESULTS: The analyses performed showed that the polygenic effect plays a greater role than the shared family environmental effect in explaining the variability of the age-at-onset of hypertension, diabetes and high cholesterol. The model which simultaneously evaluated both effects indicated that there are individuals which may have risk of hypertension due to polygenic effects 130% higher than the overall average risk for the entire sample. For diabetes and high cholesterol the risks of some individuals were 115 and 45%, respectively, higher than the overall average risk for the entire population. CONCLUSIONS: Results showed evidence of significant polygenic effects indicating that age-at-onset is a useful trait for gene mapping of the common complex diseases analyzed. In addition, we found that the polygenic random component might absorb the effects of some covariates usually considered in the risk evaluation, such as gender, age and BMI.
Assuntos
Idade de Início , Doenças Cardiovasculares/genética , Brasil , Humanos , Modelos de Riscos Proporcionais , Fatores de RiscoRESUMO
Cattle breeding programmes need objective criteria in order to evaluate and subsequently improve production systems. This work uses a logistic growth curve model for evaluating sires based on their progeny weight measured repeatedly over time. The parameters of the curve are described as a linear function of fixed and random effects. A Bayesian approach is used for the estimation. Analysis of the weights recorded on animals of the Nellore breed shows that growth curve models with fixed and random effects can be useful to evaluate and selecting sires.
Programas de melhoramento de bovinos necessitam critérios objetivos para avaliar e subsequentemente melhorar o sistema de produção. Este trabalho faz uso de um modelo de curva de crescimento logístico para avaliar touros com base nos pesos de suas progênies registrados repetidamente ao longo do tempo. Os parâmetros da curva são descritos como funções lineares de efeitos fixos e aleatórios. Uma abordagem Bayesiana é considerada para a estimação dos parâmetros. Análise de dados de pesos de animais da raça Nelore mostra que os modelos de curva de crescimento com efeitos fixos e aleatórios podem ser úteis para avaliar e selecionar touros.
RESUMO
Cattle breeding programmes need objective criteria in order to evaluate and subsequently improve production systems. This work uses a logistic growth curve model for evaluating sires based on their progeny weight measured repeatedly over time. The parameters of the curve are described as a linear function of fixed and random effects. A Bayesian approach is used for the estimation. Analysis of the weights recorded on animals of the Nellore breed shows that growth curve models with fixed and random effects can be useful to evaluate and selecting sires.
Programas de melhoramento de bovinos necessitam critérios objetivos para avaliar e subsequentemente melhorar o sistema de produção. Este trabalho faz uso de um modelo de curva de crescimento logístico para avaliar touros com base nos pesos de suas progênies registrados repetidamente ao longo do tempo. Os parâmetros da curva são descritos como funções lineares de efeitos fixos e aleatórios. Uma abordagem Bayesiana é considerada para a estimação dos parâmetros. Análise de dados de pesos de animais da raça Nelore mostra que os modelos de curva de crescimento com efeitos fixos e aleatórios podem ser úteis para avaliar e selecionar touros.