Robust Methods for Complex Trait Association Mapping

复杂性状关联映射的稳健方法

• 批准号: 7212146
• 负责人: Fei Zou
• 金额: $ 19.85万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-04-01 至 2010-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7212146
• 关键词: Address; Affect; Case-Control Studies; Complex; Computer software; Cystic Fibrosis; Dependence; Disease; Disease Association; Environment; Equilibrium; Genome; Haplotypes; Individual; Literature; Maps; Methodology; Methods; North Carolina; Nucleotides; Population; Positioning Attribute; Procedures; Range; Rate; Research; Sampling; Statistical Methods; Stratification; Susceptibility Gene; Techniques; Testing; Universities; Work; base; disorder risk; genetic variant; improved; novel; simulation; size; statistics; trait

DESCRIPTION (provided by applicant): The objectives of this research are to address some statistical issues related to association mapping (or disequilibrium mapping) for complex traits. We plan to develop robust, yet efficient statistical methods to deal with some important problems that have not been addressed or have not been fully resolved in the literature. The specific aims are: 1. To develop simple and robust techniques for assessing population stratification when anonymous markers are available, but are not necessarily in linkage equilibrium with each other. 2. To develop an efficient method for capturing the simultaneous effects of multiple genetic variants that individually make only a small contribution to the total disease risk, while controlling the overall false positive rate. 3. To explore robust non-parametric methods for estimating and assessing haplotypes associated with disease: mapping disease-associated haplotypes without pre-assigning window size of the haplotype; mapping multiple pre-disposing haplotypes; and mapping when haplotypes cannot be discerned unambiguously. Software to carry out the specific aims will be developed and implemented in the R or C computing environment for public distribution.

描述(由申请人提供)：本研究的目标是解决与复杂性状的关联映射(或不平衡映射)相关的一些统计问题。我们计划开发强大而有效的统计方法，以处理文献中尚未解决或尚未完全解决的一些重要问题。具体目标是： 1.开发简单而可靠的技术，用于在有匿名标记但不一定彼此连锁平衡的情况下评估种群分层。 2.开发一种有效的方法，在控制总体假阳性率的同时，捕获单独对总疾病风险只有很小贡献的多个遗传变异的同时影响。 3.探索用于估计和评估与疾病相关的单倍型的稳健的非参数方法：在不预先指定单倍型的窗口大小的情况下定位与疾病相关的单倍型；定位多个预处理的单倍型；以及当单倍型不能被明确识别时进行定位。 执行特定目标的软件将在R或C计算环境中开发和实现，以供公开分发。