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Statistical Methods for Gene Mapping-a New Paradigm

基因图谱统计方法——新范式

基本信息

批准号：
7002200
负责人：
Shili Lin
金额：
$ 14.4万
依托单位：
OHIO STATE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2004
资助国家：
美国
起止时间：
2004-01-20 至 2008-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7002200
关键词：
bioinformatics computer simulation genetic mapping genetic markers genetic polymorphism information dissemination mathematical model method development molecular biology information system statistics /biometry

项目摘要

DESCRIPTION (provided by applicant): The broad long-term objective of this project is to use a new paradigm to develop a set of statistical methods for mapping disease genes. The methods developed will be most relevant in preliminary genome-scan studies. The main thrust of the paradigm lies in its formulation of the hypotheses for linkage. Traditionally, hypotheses for linkage are usually set up with the null hypothesis being no linkage and the alternative hypothesis being linkage. In our new formulation, the null and alternative hypotheses are being reversed, with the null hypothesis being tight linkage and the alternative hypothesis being loose linkage or no linkage. Two of the fundamental advantages with this new paradigm are: first, multiplicity adjustment for the number of tests performed in a genome-scan study is unnecessary, and second, the location of a disease gene can be narrowed down to a small genomic region, even at the stage of a preliminary genome-scan study. The specific aims are: 1. To develop methods for constructing confidence sets of markers or confidence regions (intervals) of disease gene locations based on parametric tests of the new formulation of hypotheses. Single-marker and multiple-marker approaches will be developed for data from general pedigrees. 2. To develop methods, parallel to those in specific aim 1, for constructing confidence sets of markers or confidence regions (intervals) of disease gene locations based on nonparametric tests using allele-sharing statistics. Again, single-marker and multiple-marker approaches will be developed for a wide variety of data types (including sib-ships of arbitrary size and general pedigrees) and statistics. 3. To carry out simulation studies to extensively evaluate and compare the methods developed under a variety of settings, including the underlying disease model, data type, marker density, and marker polymorphism, and to apply the developed methods to a wide range of real data sets available to the PI. 4. To implement the methodology developed in user friendly, well-documented programs, and to make the package of the programs available to other interested researchers.

描述(由申请人提供)：该项目广泛的长期目标是使用一种新的范例来开发一套绘制疾病基因图谱的统计方法。开发的方法将在初步的基因组扫描研究中发挥最大的作用。这一范式的主旨在于它提出了联系的假设。传统的链接假设通常是零假设为无链接，替代假设为链接。在我们的新公式中，零假设和替代假设正在颠倒，零假设是紧密联系，替代假设是松散联系或不联系。这种新模式的两个基本优势是：第一，不需要对基因组扫描研究中进行的测试数量进行多样性调整，第二，疾病基因的位置可以缩小到一个小的基因组区域，即使在初步的基因组扫描研究阶段也是如此。具体目标是： 1.发展基于新假设的参数检验的疾病基因位置的标记或置信域(区间)的置信集的构建方法。将开发单标记和多标记方法来处理来自一般家系的数据。 2.开发与特定目标1中的方法平行的方法，用于基于使用等位基因共享统计的非参数检验来构建疾病基因位置的标记或置信域(区间)的置信集。同样，将为各种数据类型(包括任意大小的兄弟姐妹和一般谱系)和统计数据开发单标记和多标记方法。 3.开展模拟研究，广泛评估和比较在各种环境下开发的方法，包括基础疾病模型、数据类型、标记密度和标记多态，并将开发的方法应用于PI可用的各种真实数据集。 4.执行在用户友好、文件齐全的方案中制定的方法，并将方案包提供给其他感兴趣的研究人员。