STATISTICAL METHODS FOR GENETIC CASE CONTROL STUDIES

描述：许多遗传学研究是基于分析多个DNA 病例区和对照区。 通常每一个都是单独测试的， 与疾病的联系。 然而，一些疾病可能需要相互作用 多个地区的多态性。 将开发用于确定 基因多态性的组合，增加疾病的风险， 从病例和对照中分析了多个位点的多态性， 地区 这些方法将用于确定 连锁HLA基因编码区基因片段多态性 增加胰岛素依赖型糖尿病（IDDM）的风险 还将开发用于设计此类研究和选择样本量。 假设病例组和对照组的DNA可以按照 多个DNA区域的多态性。 问题是要找到一个更小的 多个区域和相应的多态性， 当个体具有这种多态性的组合时，疾病是高的。 这个问题有三个方面。 1. 找到少量的DNA区域 和多态性来最佳地预测疾病状态。 2. 表达 这一决定的不确定性。 3. 在不符合以下条件的情况下， 分析每个区域。 一个现代化的，计算机密集的，统计的 技术，数据增强算法将用于合并数据 从没有分析过每个地区的人， 同时量化我们对每个事件的真实概率的不确定性， 正常和患病个体之间的多态性组合， 我们抽样的人口。 该算法产生多个样本的 患病个体和未患病个体各自 多态性的可能组合。 每一个都是从后面的样本 分布，即，每个样本是一组 人口概率。 样品间的变化表示 由于样本量和某些数据缺失而导致的不确定性。 对于每个样本，我们选择两个或三个多态性的所有组合 擅长预测疾病。 一个组合出现的频率 被选择估计组合是a的后验概率 很好的预测。

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.