STATISTICAL METHODS FOR GENETIC CASE CONTROL STUDIES

遗传病例对照研究的统计方法

• 批准号: 2750132
• 负责人: DAVID Alan SCHOENFELD
• 金额: $ 7.36万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-08-01 至 2000-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2750132
• 关键词: DNA; computer data analysis; data management; disease /disorder proneness /risk; genetic polymorphism; histocompatibility antigens; human population genetics; insulin dependent diabetes mellitus; mathematical model; model design /development; nucleic acid sequence; statistics /biometry

DESCRIPTION: Many genetic studies are based on analyzing multiple DNA regions of cases and controls. Usually each is tested separately for association with disease. However some diseases may require interacting polymorphisms at several regions. Methods will be developed for determining combinations of polymorphisms that increase the risk of disease when DNA from cases and controls have been analyzed for polymorphisms at multiple regions. These methods will be used to determine combinations of polymorphisms of genetic fragments in the coding regions of linked HLA genes that increase the risk of Insulin Dependent Diabetes Mellitus (IDDM) Methods will also be developed for designing such studies and choosing sample sizes. Suppose that the DNA of cases and controls can be classified in terms of polymorphisms at multiple DNA regions. The problem is to find a smaller number of regions and corresponding polymorphisms so that the risk of disease is high when an individual has this combination of polymorphisms. This problem has three facets. 1. Finding a small number of DNA regions and polymorphisms that optimally predict disease status. 2. Expressing the uncertainty of this determination. 3. Incorporating samples where not every region is analyzed. A modern, computer intensive, statistical technique, the Data Augmentation Algorithm will be used to incorporate data from individuals who have not had every region analyzed and to simultaneously quantify our uncertainty about the true probability of each combination of polymorphisms among normal and diseased individuals in the population that we sampled. The algorithm produces multiple samples of the probabilities that a diseased and a no-diseased individual have each possible combination of polymorphisms. Each is a sample from the posterior distribution, i.e., each sample is an equally likely value of the set of population probabilities. The variation from sample to sample expresses the uncertainty due to the sample size and the fact that some data was missing. For each sample we select all the combinations of two or three polymorphisms that are good at predicting disease. The frequency with which a combination is selected estimates the posterior probability that the combination is a good predictor.

描述：许多遗传研究基于分析多个DNA 案件和控制区域。 通常每个分别测试 与疾病有关。 但是，有些疾病可能需要互动 多个地区的多态性。 将开发用于确定的方法 当DNA时增加疾病风险的多态性的组合 从病例和对照中分析了多态性的多态性 地区。 这些方法将用于确定 连接的HLA基因编码区域中遗传碎片的多态性 这增加了胰岛素依赖性糖尿病（IDDM）方法的风险 还将开发用于设计此类研究和选择样本量的情况。 假设病例和对照组的DNA可以按照 多态性的多态性在多个DNA区域。 问题是找到一个较小的 区域数量和相应的多态性 当一个人具有这种​​多态性的组合时，疾病就很高。 这个问题有三个方面。 1。找到少量的DNA区域 以及最佳预测疾病状况的多态性。 2。表达 这种决定的不确定性。 3。合并样品 分析每个区域。 现代，计算机密集型，统计 技术，数据增强算法将用于合并数据 来自没有分析每个地区的个人 同时量化我们对每个的真实概率的不确定性 正常人和患病个体中多态性的结合 我们采样的人口。 该算法产生多个样本 患病和无疾病的人的概率每个 多态性的可能组合。 每个都是后部的样本 分布，即每个样本是该集合集的同样可能的值 人口概率。 从样品到样品的变化表示 由于样本量和缺少某些数据而引起的不确定性。 对于每个样本，我们选择两个或三个多态性的所有组合 善于预测疾病。 组合的频率 选择估计该组合是一个后验概率 好的预测指标。