Statistical tests for association with X-linked genes

与 X 连锁基因关联的统计检验

• 批准号: 7210546
• 负责人: Eden R. Martin
• 金额: $ 23.47万
• 依托单位: UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-04-01 至 2010-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7210546
• 关键词: Affect; Age; Alzheimer' s Disease; Autistic Disorder; Blood Pressure; Candidate Disease Gene; Cardiovascular Diseases; Charge; Chromosome Mapping; Complex; Computer Simulation; Computer software; Data; Data Set; Defect; Disease; Environmental Risk Factor; Etiology; Face; Family; Funding; Genes; Genetic; Genetic Models; Genotype; Grant; Haplotypes; Hereditary Disease; Link; Localized; Maps; Methodology; Methods; Modeling; Parkinson Disease; Play; Predisposition; Quantitative Trait Loci; Research Personnel; Risk; Role; Sampling; Scheme; Score; Simulate; Speech Development; Statistical Methods; Susceptibility Gene; Testing; Today; United States National Institutes of Health; Variant; X Chromosome; autosome; base; design; disorder risk; early onset; novel; programs; sex; software development; tool; trait

DESCRIPTION (provided by applicant): Family-based tests of association are widely used in the search for genes contributing to complex diseases such as Parkinson's disease and autism. However, these methods have focused on the analysis of autosomal loci and are not generally useful for analysis of X-linked genes. Family-based tests that have been suggested for analysis of markers on the X chromosome are designed as tests of linkage, and are not necessarily valid tests of allelic association in linked regions. Therefore these methods are not useful for fine-mapping regions of linkage through association analysis, and we are aware of no family-based tests of association that are valid for analysis of X-linked markers. Clearly, there is a need to develop statistical methods designed for identifying important genetic factors on the X chromosome underlying complex diseases and quantitative trait variation. Through this project, we propose to develop new methodology for family-based association tests of X-linked loci. These methods will allow for inclusion of families with or without parental genotype data and will allow testing of disease risk (affected/unaffected) or quantitative traits. These methods will be thoroughly evaluated in simulated data, as well as real data from studies of Parkinson's disease, autism and early-onset cardiovascular disease. Finally software to implement the novel methods for X-linked analysis will be developed and distributed. There is compelling evidence of involvement of X-linked genes in many complex diseases, and the lack of appropriate association tests to make use of family-data hinders progress in fine-mapping disease genes on the X chromosome. Thus, the statistical methods and software developed through this grant will have an immediate application in gene mapping studies, and will help researchers identify and localize these important X-linked genes.

描述（由申请人提供）：以家族为基础的关联测试被广泛用于寻找导致复杂疾病（如帕金森病和自闭症）的基因。然而，这些方法集中于常染色体基因座的分析，通常不适用于X连锁基因的分析。已建议用于分析X染色体上标记的基于家族的测试被设计为连锁测试，并且不一定是连锁区域中等位基因关联的有效测试。因此，这些方法是没有用的精细映射区域的连锁通过关联分析，我们知道没有家庭为基础的测试是有效的关联分析的X连锁标记。显然，有必要开发统计方法，用于识别X染色体上的重要遗传因素，这些遗传因素是复杂疾病和数量性状变异的基础。通过这个项目，我们建议开发新的方法，以家庭为基础的关联测试的X连锁基因座。这些方法将允许包含具有或不具有亲本基因型数据的家族，并且将允许测试疾病风险（受影响/未受影响）或数量性状。这些方法将在模拟数据以及来自帕金森病、自闭症和早发性心血管疾病研究的真实的数据中进行彻底评估。最后，将开发和分发实现X连锁分析新方法的软件。有令人信服的证据表明，X连锁基因参与了许多复杂的疾病，缺乏适当的关联测试，利用家庭数据阻碍了在X染色体上精细定位疾病基因的进展。因此，通过这笔赠款开发的统计方法和软件将立即应用于基因作图研究，并将帮助研究人员识别和定位这些重要的X连锁基因。