Statistical Methods for Gene Mapping-a New Paradigm

基因图谱统计方法——新范式

• 批准号: 6844891
• 负责人: Shili Lin
• 金额: $ 14.75万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-01-20 至 2006-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6844891
• 关键词: bioinformatics; computer simulation; genetic mapping; genetic markers; genetic polymorphism; information dissemination; mathematical model; method development; molecular biology information system; statistics /biometry

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）：