STATISTICAL METHODS FOR GENETIC CASE CONTROL STUDIES

描述：许多遗传研究是基于对多个DNA的分析 病例和对照的区域。通常每一项都要单独进行测试 与疾病联系在一起。然而，有些疾病可能需要相互作用 几个地区的多态现象。将开发方法来确定 当DNA出现时，增加疾病风险的多态组合 从病例和对照中分析了在多个 地区。这些方法将用于确定 连锁的人类白细胞抗原基因编码区遗传片段的多态性 增加胰岛素依赖型糖尿病(IDDM)风险的方法 还将开发用于设计此类研究和选择样本量的工具。 假设病例和对照的DNA可以按照以下方面进行分类 多个DNA区域的多态。问题是要找到一个更小的 区域和相应的多态的数量，因此 当个体具有这种组合的多态时，疾病是很高的。 这个问题有三个方面。1.发现少量DNA区域 以及最能预测疾病状态的多态。2.表达 这一决定的不确定性。3.如不包括样本，则将样本合并 对每个地区进行了分析。现代的、计算机密集的、统计的 技术，数据增强算法将用于合并数据 来自那些没有对每个地区进行分析的个人，以及 同时量化我们对每个事件的真实概率的不确定性 中国人群中正常个体和疾病个体的多态组合 我们抽样的人口。该算法会生成多个样本 患病个体和未患病个体各自具有的概率 可能的多态组合。每个样本都是后方的样本 分布，即，每个样本是该集合的等概率值 总体概率。样本之间的差异表示 由于样本大小和一些数据缺失的事实而产生的不确定性。 对于每个样本，我们选择两个或三个多态的所有组合 擅长预测疾病的人。一种组合的频率 估计该组合是一个 很好的预测者。

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.