Robust Methods for Complex Trait Association Mapping

复杂性状关联映射的稳健方法

基本信息

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
项目类别：
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

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

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项目摘要

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.

这项研究的目标是解决与关联映射有关的一些统计问题（或复杂性状的不平衡映射）。我们计划开发强大而有效的统计方法处理一些尚未解决或尚未完全解决的重要问题文学。具体目的是： 1。开发简单而强大的技术来评估匿名时人口分层标记可用，但不一定彼此之间的链接平衡。 2。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做出很小的贡献，同时控制总体误报速度。 3。探索可靠的非参数方法，用于估计和评估与疾病：映射与疾病相关的单倍型，而没有预分配单倍型的窗户大小；绘制多个预插入单倍型；当单倍型无法识别时映射明确。将在R或C计算中开发和实施特定目标的软件公共分销环境。