METHODS FOR MULTIPOINT ANALYSIS OF COMPLEX PHENOTYPES

高度信息量短的高分辨率遗传图谱的出现 串联重复序列多态(STRPs)已经使基因图谱 增加对常见和/或复杂疾病的易感性 在技术上是可行的。然而，统计和计算 有效利用这项技术的方法落后于 必备的实验室方法。这项提案的总体目标是 开发统计和计算工具，以有效地 这些密集遗传图谱的最大优势。我们之前已经 发展了一种利用多标记基因座进行遗传划分的方法(MIM 人类数量性状对位于特定基因座的变异 染色体区域。在这项提案中，我们计划继续并延长我们的 研究MIM方法，并结合这些新的理论 我们以前开发的计算机的扩展版本的发展 软件。我们将研究蒙特卡罗方法的实用性 大家系复杂性状的多点连锁分析 有大量丢失的数据。我们计划将这种方法调整为 几种非参数或无模型的链接分析方法，以增加 他们在一般血统中的力量。最后，我们将应用这些方法 在以上的具体目标中派生出的几个人类特征各不相同 使用之前从我们的基因组中收集的全基因组基因数据 正在进行的关联研究。

The advent of high resolution genetic maps of highly informative short tandem repeat polymorphisms (STRPs) has made the mapping of genes which confer increased susceptibility to common and/or complex diseases technically feasible. However, the statistical and computational methodology to efficiently utilize this technology is lagging behind the requisite laboratory methods. The overall goal of this proposal is to develop the statistical and computational tools to effectively take maximal advantage of these dense genetic maps. We have previously developed a method (MIM) using multiple marker loci to partition genetic variance of a human quantitative trait to loci located in specific chromosomal regions. In this proposal we plan to continue and extend our work on the MIM methodology and incorporate these new theoretical developments in an expanded version of our previously developed computer software. We will examine the utility of a Monte-Carlo method for multipoint linkage analysis of complex traits on large extended pedigrees with substantial amounts of missing data. We plan to adapt this method to several non-parametric or model-free linkage analysis methods to increase their power in general pedigrees. Lastly, we will apply the methods derived in the above specific aims to several human traits of varying complexity using previously gathered genome-wide genotypic data from our ongoing linkage studies.