Statistical Methods for Gene Mapping Based on a Confidence Set Approach

基于置信集方法的基因作图统计方法

• 批准号: 0306800
• 负责人: Shili Lin
• 金额: --
• 依托单位: Ohio State University Research Foundation -DO NOT USE
• 依托单位国家: 美国
• 项目类别: Continuing grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-08-01 至 2007-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0306800&HistoricalAwards=false
• 关键词: Statistical; Methods; Gene; Mapping; Based

Multiple testing is an important but difficult statistical issue in many areas of genetic research. One particular multiple testing problem arises when many markers are screened throughout the genome for their linkage or association with a disease locus, which is the focus of this project, with a broad long-term objective of developing methods applicable in various areas of genetic and genomic research. The main thrust of the proposed approach lies in its formulation of the hypotheses for linkage. Traditionally, hypotheses for linkage are usually set up with the null hypotheses being no linkage and the alternative hypothesis being linkage. In the 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 is unnecessary for the number of tests performed in a genome-scan study, 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 first specific aim is to develop methods for constructing confidence sets of markers or confidence regions (intervals) of disease gene locations based on parametric tests of the hypotheses. Single-marker and multiple-marker approaches will be developed for data from general pedigrees. The second specific aim can be viewed as a non-parametric counterpart of the first aim. Methods will be developed for constructing confidence sets/regions based on non-parametric tests using allele-sharing statistics. A wide variety of allele-sharing statistics and data types, ranging from simple structures (sibships, relative pairs) to general pedigrees, will be considered.With the completion of the Human Genome Project, and the development of high throughput technology for genotyping, it is now a routine matter to search up to thousands of genetic markers distributed throughout the genome to look for disease susceptibility genes. This project aims at developing statistical methods suitable for probing each of these markers without compromising the power of finding nearby susceptibility locus. As the number of participating families increases, the rate of falsely implicating a marker not located within a short distance from a disease gene will eventually go down to zero. This would not only increase the chance of successful identification of disease genes, but would also save tremendous resources by reducing the chance of going after "ghost" genes. Thus, the methods developed can be a valuable tool to the gene mapping community. In particular, it is expected that the methods developed in this project will be applied to data from projects, on which the investigator is collaborating with medical doctors and other researchers, on a range of autoimmune diseases, including systemic lupus erythematosus and multiple sclerosis.

在遗传研究的许多领域中，多重测试是一个重要但困难的统计问题。 当在整个基因组中筛选许多标记物与疾病位点的连锁或关联时，就会出现一个特殊的多重测试问题，这是该项目的重点，其广泛的长期目标是开发适用于遗传和基因组研究各个领域的方法。 所提出方法的主旨在于其建立联系假设。 传统上，通常建立关联假设，原假设不存在关联，备择假设存在关联。 在新的表述中，原假设和备择假设被“颠倒”，原假设是紧密联系，备择假设是松散联系或无联系。 这种新范式的两个基本优点是：首先，对于基因组扫描研究中进行的测试数量而言，不需要进行多重性调整；其次，即使在初步基因组扫描研究阶段，疾病基因的位置也可以缩小到一个小的基因组区域。 第一个具体目标是开发基于假设的参数检验构建疾病基因位置标记置信集或置信区域（区间）的方法。 将为来自一般谱系的数据开发单标记和多标记方法。 第二个具体目标可以被视为第一个目标的非参数对应物。 将开发基于使用等位基因共享统计的非参数测试构建置信集/区域的方法。 将考虑各种等位基因共享统计数据和数据类型，从简单的结构（同胞、亲属对）到一般的谱系。随着人类基因组计划的完成，以及基因分型高通量技术的发展，搜索分布在整个基因组中的多达数千个遗传标记以寻找疾病易感基因已成为常规问题。 该项目旨在开发适合探测每个标记的统计方法，而不影响寻找附近易感位点的能力。 随着参与家庭数量的增加，错误暗示不在疾病基因附近的标记的比率最终将降至零。 这不仅会增加成功识别疾病基因的机会，而且还可以通过减少寻找“幽灵”基因的机会来节省大量资源。 因此，所开发的方法可以成为基因图谱界的宝贵工具。 特别是，预计该项目开发的方法将应用于研究人员与医生和其他研究人员合作的一系列自身免疫性疾病项目的数据，包括系统性红斑狼疮和多发性硬化症。