IMPROVING THE POWER OF LINKAGE DISEQULIBRIUM MAPPING

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 关联研究为寻找复杂人类疾病的潜在遗传变异提供了一种令人兴奋的方法。然而，基因变异的鉴定仍然面临困难的挑战，重要的是开发强大的新统计方法。目前，关联方法可以依赖于单基因座分析--即一次分析一个基因座的关联，通常是单核苷酸多态(SNP)--或者依赖于多基因座分析，在多基因座分析中，使用多个SNPs来提取关于连锁不平衡(LD)的最大信息。已有研究表明，单基因座分析可能具有较低的功耗，因为单个SNP往往具有有限的LD信息。信息更丰富的多位点分析可以根据单倍型或基因类型进行。它可能会失去动力，因为涉及的自由度往往很多。理想的方法必须充分利用来自多个轨迹的重要信息，但避免增加自由度。因此，我们开发了两种方法来从多个SNP中捕获信息。我们开发了一种基于加权傅里叶变换系数的测试，对低频分量给予了更多的权重。我们开发了一种复杂疾病的关联作图方法，通过挖掘正常个体中很少出现的受影响个体的单倍型片段(即阶段性基因型对)的共享，现在扩展到解决来自无关个体的数量性状作图问题。该方法已进一步扩展，以纳入单倍型歧义。使用模拟和真实数据集进行的大量实验研究证明了该方法的有效性。仿真结果证明了该方法的有效性，并且与其他常用方法相比，该方法具有更高的效率。这些方法为识别复杂疾病背后的致病基因变异提供了额外的工具。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Association studies offer an exciting approach to finding underlying genetic variants of complex human diseases. However, identification of genetic variants still includes difficult challenges, and it is important to develop powerful new statistical methods. Currently, association methods may depend on single-locus analysis--that is, analysis of the association of one locus, which is typically a single-nucleotide polymorphism (SNP), at a time--or on multilocus analysis, in which multiple SNPs are used to allow extraction of maximum information about linkage disequilibrium (LD). It has been shown that single-locus analysis may have low power because a single SNP often has limited LD information. Multilocus analysis, which is more informative, can be performed on the basis of either haplotypes or genotypes. It may lose power because of the often large number of degrees of freedom involved. The ideal method must make full use of important information from multiple loci but avoid increasing the degrees of freedom. Therefore, we have developed two methods to capture information from multiple SNPs. We developed a test based on weighted Fourier transformation coefficients, with more weight given to the low-frequency components. We developed an association mapping method for complex diseaes by mining the sharing of haplotype segments (i.e. phased genotype pairs) in affected individuals that are rarely present in normal individuals, now extended to address the problem of quantitative trait mapping from unrelated individuals. The approach has been further extended to incorporate haplotype ambiguities. The effectiveness of the approaches was demonstrated by extensive experimental studies using both simulated and real data sets. Our simulation results demonstrate the validity and substantially higher power of the proposed method compared with other common methods. These methods provide additional tools for ithe identifiying of causative genetic variants underlying complex diseases.