Multipoint and significance methods for genome-wide association studies

描述：（由申请人提供）该项目将开发用于分析全基因组关联研究和多候选基因研究的统计方法，其中感兴趣的表型是定量的。这些方法将包括新的多点方法，旨在从现有数据中提取最大数量的信息，以及评估结果重要性的方法，这些方法有效地处理了这些大规模研究中进行的大量多重比较。提出的多点关联定位方法的动机是，即使对25万个snp进行全基因组扫描，许多影响表型的snp将是无型的。这个想法是评估一个无型SNP是否影响表型，首先使用周围的单倍型变异来预测无型SNP的合理基因型，然后评估预测的基因型和观察到的表型之间的关联。评估显著性的方法将基于控制“错误发现率”（阳性结果被证明是错误的比例）。这些方法将应用于全基因组扫描（1000个人中250000个snp）和候选基因研究，旨在确定基因变异和负责对他汀类药物差异反应的基因，以及来自候选基因研究的数据，旨在确定影响与动脉粥样硬化、斑块炎症和血栓形成相关的定量表型的遗传变异，所有这些因素都与心血管疾病相关。这些研究的发现可能有助于了解影响心血管疾病的遗传因素及其治疗。此外，将开发和分发执行统计方法的用户友好软件，使世界各地进行类似研究的其他研究人员能够利用这些工具。这些工具有可能提高旨在确定常见疾病的潜在遗传基础的研究的效力和效率，有可能导致维持健康和预防疾病的新治疗战略。公共卫生相关性：该项目将产生用于分析大规模研究的统计工具，旨在帮助了解常见疾病和药物反应的遗传基础。这些工具有可能提高这类研究的效力和效率，可能导致维持健康和预防疾病的新治疗战略。

DESCRIPTION: (provided by applicant) This project will develop statistical methods for analyzing both genome-wide association studies and studies on multiple candidate genes, where the phenotype of interest is quantitative. The methods will include novel multipoint methods designed to extract the maximum amount of information from the available data, and methods for assessing significance of the results that deal effectively with the large number of multiple comparisons being performed in these large-scale studies. The proposed multipoint approach to association mapping are motivated by the fact that, even with a genome-wide scan of 250,000 SNPs, many SNPs affecting phenotype will be untyped. The idea is to assess whether an untyped SNP affects phenotype by first using surrounding haplotypic variation to predict plausible genotypes at the untyped SNP, and then assessing association between the predicted genotypes and observed phenotypes. The methods for assessing significance will be based on controlling the "False Discovery Rate" (the proportion of positive findings that turn out to be incorrect). The methods will be applied to a genome-wide scan (250,000 SNPs in 1,000 individuals) and candidate gene studies aimed at identifying genetic variants and genes responsible for differential response to statin drugs, and to data from a candidate gene study aimed at identifying genetic variants affecting quantitative phenotypes associated with atherosclerosis, plaque inflammation, and thrombosis, all factors associated with cardio-vascular disease. Findings from these studies may aid understanding of the genetic factors affecting cardio-vascular disease, and its treatment. In addition, user friendly software implementing the statistical methods will be developed and distributed, allowing other researchers conducting similar studies world-wide to have access to these tools. These tools have the potential to improve the effectiveness and efficiency of studies aimed at determining the underlying genetic basis of common diseases, potentially leading to new treatment strategies for maintaining health and preventing disease. Public health relevance: This project will generate statistical tools for analyzing large-scale studies that aim to help understand the genetic basis of common diseases and drug response. These tools have the potential to improve the effectiveness and efficiency of such studies, potentially leading to new treatment strategies for maintaining health and preventing disease.