METHODS FOR ANALYZING PEDIGREE DATA WITH MANY PARAMETERS

该项目的长期目标是开发新的 使用复杂模型分析系谱数据的方法。 与 随着基因数据的爆炸式增长，科学家们变得更加雄心勃勃， 在他们寻找疾病易感基因的努力中。 而不是 满足于简单显性或隐性d的作图， 注意力集中在复杂的特征上，如糖尿病或 老年痴呆症 与此同时，多个标志物的数据可能会被 用于单次分析，以增加检测能力 连锁或定位基因后，连锁已被发现。这些 这些变化在数据分析中造成了两个独立但相关的问题。 第一个问题是计算。由于模型复杂， 疾病表型和/或多种标记的使用， 计算LOD分数可能非常耗时 使用现有技术是不可行的。 第二个问题是 统计推断 目前宣布联系的标准是 lod得分为3.0或以上。 虽然这一标准相当 对于一个简单的性状来说，很明显， 当拟合的遗传模型具有许多参数时， 考虑诊断方案。 单独获得lod分数应该 不应被视为分析的结束。 其他推理工具， 例如P值和后验概率。 拟议研究 针对上述两个问题。 从计算上来说， 有效的算法是通过结合几种算法的优点来开发的， 技术. 其中包括传统的剥离算法，重要性 抽样和吉布斯抽样。 算法不仅仅是为了 计算lod得分，但也用于获得P值和后验 赔率 此外，关键重组事件的概率也可以 作为奖金获得，这有时可以大大帮助的任务， 理解数据。 新方法将使用以下两种方法进行测试： 模拟和真实的数据集。 后者包括恶性肿瘤的数据集， 黑色素瘤、阿尔茨海默病、一种罕见的糖尿病和骨质疏松症。

The broad, long-term objective of this project is to develop new methodology for analyzing pedigree data with complex models. With the current explosion of genetic data, scientists have become more ambitious in their effort to locate disease susceptibility genes. Instead of being satisfied with mapping simple dominant or recessive d , more attention is directed towards complex traits such as diabetes or Alzheimer disease. At the same time, data on multiple markers may be used in a single analysis, either to increase the power in detecting linkage or to localize the gene after linkage has been found. These changes created two separate, but related, problems in data analysis. The first problem is computations. Because of the complex models fitted to the disease phenotype and/or the use of multiple markers, simply computing the lod score can be extremely time consuming and sometimes infeasible using existing techniques. The second problem concerns statistical inference. The current criterion for declaring linkage is to have a lod score of 3.0 or above. While this criterion is quite appropriate for a simple trait, it is clear that adjustments axe needed when the genetic models fitted have many parameters and when multiple diagnostics schemes are considered. Getting the lod score alone should not be considered as the end of an analysis. Other inference tools, such as P-values and posterior odds, are needed. The proposed research is directed towards the two problems mentioned. Computationally, efficient algorithms are developed by combining the strengths of several techniques. These include the traditional peeling algorithm, importance sampling, and Gibbs sampling. Algorithms are not just developed for computing lod scores, but also for obtaining P-values and posterior odds. In addition, probabilities of key recombination events can also be obtained as a bonus, which can sometimes greatly assist the task of understanding the data. The new methodology will be tested using both simulated and real data sets. The later include data sets on malignant melanoma, Alzheimer disease, a rare form of diabetes, and osteoporosis.