IMPROVING THE POWER OF LINKAGE DISEQULIBRIUM MAPPING

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 关联研究为发现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，遗传变异的识别仍然存在困难的挑战，开发强大的新统计方法非常重要。目前，关联方法可能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性（SNP）-或多基因座分析，其中使用多个SNP来提取有关连锁不平衡（LD）的最大信息。已经表明，单基因座分析可能具有低功效，因为单个SNP通常具有有限的LD信息。多基因座分析，这是更多的信息，可以进行单倍型或基因型的基础上。它可能会失去权力，因为往往涉及大量的自由度。理想的方法必须充分利用多个位点的重要信息，但要避免增加自由度。因此，我们开发了两种方法来捕获来自多个SNP的信息。 我们开发了一种基于加权傅里叶变换系数的测试，其中低频分量具有更多的权重。我们开发了一种关联映射方法，通过挖掘在正常个体中很少存在的受影响个体中的单倍型片段（即阶段性基因型对）的共享，用于复杂疾病，现在扩展到解决无关个体的数量性状映射问题。 该方法已被进一步扩展到包括单倍型模糊性。使用模拟和真实的数据集进行了大量的实验研究，证明了该方法的有效性。 我们的仿真结果表明，该方法的有效性和显着更高的功率相比，其他常见的方法。这些方法为鉴定复杂疾病的致病遗传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.