SURVIVAL MODEL FOR GENETIC EPIDEMIOLOGY

遗传流行病学的生存模型

• 批准号: 2095036
• 负责人: Duncan C. Thomas
• 金额: $ 35.11万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-04-01 至 1995-03-09
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2095036
• 关键词: Alzheimer's disease; breast neoplasms; cardiovascular disorder epidemiology; computer program /software; computer simulation; data collection methodology /evaluation; disease /disorder proneness /risk; epidemiology; gene interaction; genetic disorder; genetic markers; genetic models; human data; mathematical model; medical records; neoplasm /cancer epidemiology; vital statistics; Alzheimer's disease; breast neoplasms; cardiovascular disorder epidemiology; computer program /software; computer simulation; data collection methodology /evaluation; disease /disorder proneness /risk; epidemiology; gene interaction; genetic disorder; genetic markers; genetic models; human data; mathematical model; medical records; neoplasm /cancer epidemiology; vital statistics

The aim of this proposal is to develop practical procedures for the analysis of disease incidence data in genetic epidemiology, allowing for censored observation of outcomes, ascertainment probabilities, measured environmental exposures or covariates, genetic markers, unmeasured genetic and environmental factors, and interactions among them. The underlying model is an extension of "frailty" models for multivariate survival data in which the correlation in outcomes is modeled in terms of one or more latent variables ("frailties") that are shared by family members. In the frailty models that have been developed so far, all members of a family are assumed to have the same frailty. Although this allows one to test the phenomenon of familial aggregation, it precludes exploration of the genetic or environmental basis of such aggregation. In the proposed extension of the method, each individual would have a unique frailty, subject to some known correlational structure induced by family relationships with a small number of parameters to be estimated. Major gene(s) and polygenic models of inheritance will be considered, together with linkage to genetic markers, environmental risk factors, and gene-environment (GxE) interactions. We propose to fit the modal and estimate the posterior distribution of modal parameters using a Monte Carlo technique known as Gibbs sampling, in which each unknown quantity (frailty or modal parameter) is successively replaced by random values drawn from their respective posterior distributions, given the observed data and the current values of the other unknowns. This work will consist of four activities, (1) Further theoretical development of the methods, including major-gene and multifactorial models, adaptations required for case-control and other designs, adjustments for ascertainment and missing data, and asymptotic calculations; (2) Development of practical computer programs, using sequential and parallel processing techniques, as well as simple approximations that allow valid analysis using standard packages; (3) Simulation studies of the performance of the methods compared with standard methods in genetics and epidemiology; this will include assessment of the power to distinguish alternative genetic models and patterns of GxE interactions; and (4) Application to various data sets, including breast cancer in families of probands with premenopausal bilateral breast cancer, Alzheimer disease in extended pedigrees, and cardiovascular disease and breast cancer in twins.

该提议的目的是为 分析遗传流行病学中疾病发病率数据，允许 测量的结果，确定概率的审查观察 环境暴露或协变量，遗传标记，未得到的 遗传和环境因素以及它们之间的相互作用。 这 基础模型是多元模型的“脆弱”模型的扩展 生存数据以结果的相关性是根据 由家人共享的一个或多个潜在变量（“脆弱”） 成员。 在到目前为止开发的脆弱模型中，所有成员 假定家庭的脆弱。 虽然这允许 一个用于测试家族聚集现象的一种，它排除了 探索这种聚集的遗传或环境基础。 在该方法的拟议扩展中，每个人都有一个 独特的脆弱，受到某些已知的相关结构的约束 家庭关系与少数参数要估计。 将考虑主要基因和遗传的多基因模型 以及与遗传标记，环境风险因素和 基因环境（GXE）相互作用。 我们建议适合模态和 使用蒙特估计模态参数的后验分布 卡洛技术称为吉布斯采样，其中每个未知数量 （脆弱或模态参数）被随机值连续取代 鉴于观察到的 数据和其他未知数的当前值。 这项工作将包括四个活动，（1）进一步的理论 这些方法的开发，包括主要基因和多因素 模型，病例控制所需的改编和其他设计， 调整确定和缺少数据以及渐近性的调整 计算； （2）开发实用计算机程序，使用 顺序和并行处理技术，以及简单 允许使用标准软件包有效分析的近似值； （3） 与该方法的性能相比的模拟研究 遗传学和流行病学的标准方法；这将包括 评估区分替代遗传模型的能力和 GXE相互作用的模式； （4）应用于各种数据集， 包括绝经前的概率家族中的乳腺癌 双侧乳腺癌，延长的谱系中的阿尔茨海默氏病和 双胞胎的心血管疾病和乳腺癌。