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。 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。我们发现了测量这三种类型的信息的统计量之间的成对协方差，并表明统计量是渐近三变量正态分布的。然后，我们分析比较了它们的功效，以根据疾病模型确定信息量最大的统计数据。我们的研究结果表明，HWD参数的差异是显性和隐性疾病模型的信息，而等位基因频率和LD参数的差异一般是信息，除了罕见的隐性疾病模型。在标记位点的哈代温伯格平衡和群体中标记间的连锁平衡下，检测这三种差异的统计量是相互独立的。了解统计量之间的成对协方差使得可以定义相互独立的统计量。这使我们能够对相同的数据进行顺序分析，而不需要调整对同一数据集进行的所有多个分析的显著性水平。因此，我们可以改进灵活的策略，增加全基因组关联研究的能力，而不需要收集新的独立样本。 在后来的研究中，我们开发了一个基于逻辑回归模型的通用框架，该框架通过同时考虑所有三种类型的信息来提高功效。

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). We found the pairwise covariances between statistics that measure these three types of information and show that the statistics are asymptotically trivariate normally distributed. Then we compared their power analytically to determine the most informative statistics according to the disease model. Our results show that differences in parameters for HWD are informative for dominant and recessive disease models, while differences in allele frequencies and in parameters for LD are generally informative except for rare recessive disease models. There is mutual independence of the statistics that detect these three differences under Hardy Weinberg equilibrium at the marker locus and linkage equilibrium between markers in the population. Knowing the pairwise covariances between the statistics makes it possible to define statistics that are mutually independent. This allows us to perform sequential analyses of the same data without the need to adjust significance levels for all the multiple analyses being performed on the same data set. As a result we can have improved flexible strategies to increase the power of genome-wide association studies without requiring the collection of a new, independent sample. In a later study, we have developed a general framework, based on a logistic regression model, that increases power by taking account of all three types of information simultaneously.