Robust Methods for Complex Trait Association Mapping

描述（由申请人提供）：本研究的目的是解决与复杂性状关联作图（或不平衡作图）相关的一些统计问题。我们计划开发强大的，但有效的统计方法来处理一些重要的问题，尚未解决或尚未完全解决的文献。具体目标是： 1.当匿名标记可用但不一定彼此连锁平衡时，开发用于评估人群分层的简单而可靠的技术。 2.开发一种有效的方法，用于捕获多个遗传变异的同时效应，这些遗传变异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：在不预先分配单倍型的窗口大小的情况下映射疾病相关的单倍型;映射多个易感单倍型;以及当单倍型不能明确识别时映射。 将在R或C计算环境中开发和实施实现具体目标的软件，供公众分发。

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.