METHODS AND THEORY FOR LINKAGE ANALYSIS

该项目的广泛、长期目标是开发高效的 连锁性分析的计算方法和研究 在作图中具有高密度标记的理论优势 特征基因。众所周知，在一个连锁中使用多个标记 分析增加了建立染色体的力量，在染色体上 特质基因决定了。最近的理论研究表明，更高的密度 标记的数目增加了向真实基因位置的收敛速度。 此外，初步研究表明，拥有密集的标记可以减少 由于型号说明错误而导致的一些问题。具体地说，假设一个 数量性状与位于同一染色体上的两个基因有关， 但符合单基因模型。利用稀疏标记，分析可以 会聚到这两个基因之间的一个位置。相比之下，随着密度的增加， 标记，则分析将会聚到具有 更大的影响，也暗示了另一个基因的存在。这 该项目旨在充分了解这一现象，从而可以 对复杂疾病的研究具有重要意义。而当 拥有多个标记有很多优点，它会导致严重的 大型人类谱系的计算问题。一种名为 已成功地实现了对该分析的顺序推算 有糖尿病血统的人。该项目将在更多的环境中实现该方法 灵活多变，进一步提高工作效率，使 适用于更多问题。这种方法将在新的和 历史数据集研究使用多个标记的实际影响 同时在一次分析中。甚至可能会出现计算问题 如果家系是高度近亲繁殖的，则只有一个标记。最近， Gibbs块方法，它结合了传统的精确方法 用Gibbs抽样的蒙特卡罗方法计算(剥离) 已经成功地应用于分析高度近交的猪的系谱。 该项目计划对近亲繁殖的人类数据实施阻止Gibbs。到期 人类和猪数据之间的质量差异，许多具有挑战性 问题将不得不得到解决。

The broad, long-term objectives of this project are to develop efficient computational methods for linkage analysis and to investigate the theoretical advantages of having a high density of markers in the mapping of trait genes. It is well known that using multiple markers in a linkage analysis increases the power to establish the chromosome on which the trait gene lies. Recent theoretical work demonstrates how a higher density of markers increases the rate of convergence to the true gene location. Moreover, preliminary research suggests that having dense markers reduces some problems caused by model misspecification. Specifically, suppose a quantitative trait is related to two genes lying on the same chromosome, but a monogenic model is fitted. With sparse markers, the analysis may converge to a location in between the two genes. In contrast, with dense markers, the analysis will converge to the location of the gene with a larger effect and also suggest the presence of the other gene. This project aims to acquire a full understanding of this phenomenon which can have important implications for the study of complex disorders. While having multiple markers has many advantages, it leads to serious computational problems for large human pedigrees. A novel method called sequential imputation had been successfully implemented for the analysis of a diabetes pedigree. This project will implement the method in a more flexible manner, further increasing its efficiency and making it applicable for more problems. The method will be tried on both new and historical data sets to study the practical impact of using many markers simultaneously in a single analysis. Computational problems can arise even with a single marker if the pedigree is highly inbred. Recently, the method of blocking Gibbs, which combines the traditional method of exact computations (peeling) with the Monte Carlo method of Gibbs sampling, had been successfully implemented to analyze a highly inbred pedigree of pigs. This project plans to implement blocking Gibbs for inbred human data. Due to qualitative differences between human and pig data, many challenging problems will have to be solved.