IMPROVING THE POWER OF LINKAGE DISEQULIBRIUM MAPPING

这个子项目是许多利用 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。 已有许多基于单核苷酸多态性的测试被建议用于病例对照设计中的关联分析。可能的关联证据包括三种类型的信息：病例和对照之间等位基因频率的差异，哈代温伯格不平衡（HWD）参数和连锁不平衡（LD）参数。然而，用于LD的参数需要关于相位的知识，这通常是未知的，使得在实践中不进行修改的LD对比度测试是不可行的。用于处理相位不确定性的方法是：（1）每个个体的最可能的单体型对可以被认为是真实相位;（2）可以使用单体型的加权平均;（3）我们可以考虑复合LD，其不需要关于相位的任何信息。我们比较了这些方法来处理相位不确定性的有效性和效率，以及对他们的影响，在人口中的HWD，在同一时间确认结果的三种类型的信息。当标记之间的LD高时，推荐使用单倍型或最可能单倍型的加权平均来计算LD的LD对比试验，但否则推荐使用复合LD的LD对比试验。 在另一项研究中，我们检查了两个单标记测试和四个双标记测试。真正的关联模型的推导，他们让我们理解为什么只有一个线性项的模型通常会很好地适合一个SNP在弱LD与因果SNP，无论疾病模型，但不适合一个SNP在高LD与非加性疾病SNP。我们使用HapMap CEU群体数据中11号染色体的真实的LD参数研究关联检验的功效。在单标记测试中，等位基因测试在加性疾病的情况下平均具有最大的功效，但是对于显性、隐性和杂合子劣势疾病，基因型测试具有最大的功效。在四个双标记测试中，等位基因LD对比测试，它结合了两个标记的线性项和它们的相互作用项，为所研究的病例提供了最可靠的总体功效。因此，我们的研究结果支持在多标记测试中引入交互作用项以及线性项。

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. There have been many single nucleotide polymorphism-based tests suggested for association analysis in a case-control design. The possible evidence for association comprises three types of information: differences between cases and controls in allele frequencies, in parameters for Hardy Weinberg disequilibrium (HWD) and in parameters for linkage disequilibrium (LD). However, the parameters for LD require knowledge about phase, which is usually unknown, making the LD contrast test without modification infeasible in practice. Methods for handling phase uncertainty are: (1) the most probable haplotype pair for each individual can be considered as the true phase; (2) a weighted average of haplotypes can be used; (3) we can consider the composite LD, which does not require any information about phase. We compare these methods to handle phase uncertainty in terms of validity and efficiency, and the effect on them of HWD in the population, at the same time confirming results for the three types of information. When the LD between markers is high, the LD contrast test that uses a weighted average of haplotypes or the most probable haplotypes to calculate the LD is recommended, but otherwise the LD contrast test that uses the composite LD is recommended. In another study, we examine two single-marker tests and four two-marker tests. The true association models are derived and they allow us to understand why a model with only a linear term will generally fit well for a SNP in weak LD with a causal SNP, whatever the disease model, but not for a SNP in high LD with a non-additive disease SNP. We investigate the power of the association tests using real LD parameters from chromosome 11 in the HapMap CEU population data. Among the single-marker tests, the allelic test has on average the most power in the case of an additive disease, but for dominant, recessive, and heterozygote disadvantage diseases, the genotypic test has the most power. Among the four two-marker tests, the Allelic-LD contrast test, which incorporates linear terms for two markers and their interaction term, provides the most reliable power overall for the cases studied. Therefore, our result supports incorporating an interaction term as well as linear terms in multi-marker tests.