Statistical Methods for Gene Mapping-a New Paradigm

描述（由申请人提供）： 这个项目的广泛的长期目标是使用一种新的范例来开发一套用于绘制疾病基因的统计方法。开发的方法将在初步的基因组扫描研究中最相关。该范式的主旨在于它对联系假设的阐述。传统的连锁假说通常是以无连锁为零假设，以连锁为备择假设。在我们的新表述中，零假设和备择假设被颠倒，零假设是紧密联系，备择假设是松散联系或无联系。这种新模式的两个基本优点是：第一，在基因组扫描研究中进行的测试数量的多重性调整是不必要的，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。具体目标是： 1.开发基于新假设公式的参数检验构建标记置信集或疾病基因位置置信区（区间）的方法。单标记和多标记的方法将被开发用于一般家系的数据。 2.开发与具体目标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构建标记的置信集或疾病基因位置的置信区（区间）。同样，单标记和多标记方法将被开发用于各种各样的数据类型（包括任意大小的兄弟姐妹和一般谱系）和统计。 3.开展模拟研究，以广泛评价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性，并将开发的方法应用于PI可用的广泛真实的数据集。 4.以用户友好的，有据可查的程序实施开发的方法，并使其他感兴趣的研究人员可以使用该程序包。

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.