METHODS AND THEORY FOR LINKAGE ANALYSIS

该项目的广泛和长期目标是开发高效的 连锁分析的计算方法，并调查 在作图中具有高密度标记的理论优势 性状基因。众所周知，在连锁中使用多个标记， 分析增加了建立染色体的能力， 特质基因说谎最近的理论研究表明， 标记的使用增加了收敛到真实基因位置的速率。 此外，初步研究表明，密集的标记减少了 模型错误引起的一些问题。具体来说，假设一个 数量性状与位于同一染色体上的两个基因有关， 但拟合的是单基因模型。利用稀疏标记，分析可以 会聚集到两个基因之间的位置与此相反， 标记，分析将收敛到基因的位置， 更大的影响，也表明存在其他基因。这 该项目旨在全面了解这一现象， 对复杂疾病的研究有重要意义。而 具有多个标记物具有许多优点，它导致严重的 大型人类谱系的计算问题。一种新的方法， 分析中成功实施了序贯插补 有糖尿病血统该项目将在一个更 灵活的方式，进一步提高其效率，使其 适用于更多的问题。该方法将在新的和 历史数据集，以研究使用许多标志的实际影响 同时在一个分析。计算问题甚至会出现， 如果是高度近亲繁殖的话，就只有一个标记近日 分块Gibbs方法，它结合了传统的精确 计算（剥离）与蒙特卡罗方法的吉布斯抽样，有 已成功实施，以分析高度近交系的猪。 该项目计划对近亲繁殖的人类数据实施阻塞Gibbs。由于 人类和猪数据之间的定性差异，许多挑战 问题必须解决。

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.