Multipoint and significance methods for genome-wide association studies

描述：(申请人提供)这个项目将开发统计方法来分析全基因组关联研究和对多个候选基因的研究，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，以及评估结果的重要性的方法，这些方法有效地处理在这些大规模研究中进行的大量多重比较。建议的多点关联作图方法的动机是这样一个事实，即即使对250,000个SNP进行全基因组扫描，许多影响表型的SNP将是未分型的。这个想法是通过首先使用周围的单倍型变异来预测非类型性SNP上可能的基因类型，然后评估预测的基因类型和观察到的表型之间的关联，来评估非类型性SNP是否影响表型。评估重要性的方法将基于控制“错误发现率”(最终被证明是不正确的阳性发现的比例)。这些方法将应用于全基因组扫描(1,000人中有250,000个SNPs)和候选基因研究，旨在确定基因变异和导致对他汀类药物的差异反应的基因，以及候选基因研究的数据，该候选基因研究旨在识别影响与动脉粥样硬化、斑块炎症和血栓形成相关的量化表型的遗传变异，所有这些因素都与心血管疾病有关。这些研究的发现可能有助于理解影响心血管疾病的遗传因素及其治疗。此外，还将开发和分发实施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使用这些工具。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，有可能导致维护健康和预防疾病的新治疗战略。公共卫生相关性：该项目将产生统计工具，用于分析旨在帮助理解常见疾病的遗传基础和药物反应的大规模研究。这些工具有可能提高这类研究的效力和效率，有可能导致维持健康和预防疾病的新治疗策略。

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.