METHODS FOR MULTIPOINT ANALYSIS OF COMPLEX PHENOTYPES

高分辨率遗传图谱的出现， 串联重复序列多态性（STRP）已经使基因的定位， 增加对常见和/或复杂疾病的易感性 技术上可行。然而，统计和计算 有效利用这项技术的方法落后于 必要的实验室方法。本提案的总体目标是 开发统计和计算工具， 这些密集的遗传图谱的最大优势。我们先前已经 开发了一种方法（MIM），使用多个标记位点来划分遗传 人类数量性状对特定基因座的变异 染色体区域在本提案中，我们计划继续并扩大我们的 研究MIM方法，并将这些新的理论 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 有大量的缺失数据我们计划采用这种方法， 几种非参数或无模型的连锁分析方法， 他们在一般谱系中的力量。最后，我们将应用这些方法 在上述具体目标中得出的几个不同的人类特征 复杂性使用以前收集的全基因组基因型数据，从我们的 正在进行的联系研究。

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.