Robust Methods for Complex Trait Association Mapping

这项研究的目标是解决与关联映射(或 复杂性状的不平衡作图)。我们计划开发强大而高效的统计方法来 处理一些没有得到解决或没有完全解决的重要问题 文学。具体目标是： 1.开发简单而可靠的技术来评估匿名情况下的人口分层 标记是可用的，但不一定彼此处于连锁平衡状态。 2.开发一种有效的方法来捕捉多个遗传变异的同时影响 个体对总的疾病风险只有很小的贡献，同时控制总体的假阳性 费率。 3.探索稳健的非参数方法估计和评估与以下相关的单倍型 疾病：在不预先分配单倍型的窗口大小的情况下绘制与疾病相关的单倍型； 映射多个预处理的单倍型；无法识别单倍型时的映射 毫不含糊。 实现特定目标的软件将在R或C计算中开发和实现 公共分发的环境。

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.