Robust Methods for Complex Trait Association Mapping

复杂性状关联映射的稳健方法

• 批准号: 7391773
• 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/7391773
• 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

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.

本研究的目的是解决与关联映射（或 不平衡作图）。我们计划开发强大而有效的统计方法， 处理一些过去没有处理或没有完全解决的重要问题， 文学具体目标是： 1.开发简单而强大的技术，用于评估匿名情况下的人口分层 标记是可用的，但不一定彼此连锁平衡。 2.为了开发一种有效的方法来捕获多种遗传变异的同时影响， 单独对总疾病风险的贡献很小，同时控制总体假阳性 率 3.探索稳健的非参数方法，用于估计和评估与以下相关的单倍型： 疾病：绘制疾病相关的单倍型，而不预先分配单倍型的窗口大小; 映射多个预处理单倍型;以及当单倍型不能辨别时映射 毫不含糊 将在R或C计算中开发和实现特定目标的软件 公共分配的环境。