Multipoint and significance methods for genome-wide association studies

全基因组关联研究的多点和显着性方法

基本信息

批准号：
7246514
负责人：
MATTHEW STEPHENS
金额：
$ 29.31万
依托单位：
UNIVERSITY OF CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2006
资助国家：
美国
起止时间：
2006-06-15 至 2011-05-31
项目状态：
已结题

项目摘要

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.

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对多个候选基因的研究的统计方法，其中感兴趣的表型是定量的。该方法将包括旨在从可用数据中提取最大信息的新型多点方法，以及评估结果的重要性，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。提出的关联映射的多点方法是由以下事实激发的：即使在全基因组扫描250,000个SNP的情况下，许多影响表型的SNP也将被取消。这个想法是通过首先使用周围的单倍型变异来评估非型SNP是否会影响表型，以预测未型SNP处的合理基因型，然后评估预测的基因型和观察到的表型之间的关联。评估显着性的方法将基于控制“错误的发现率”（结果是不正确的积极发现的比例）。该方法将应用于全基因组扫描（1,000名个人中的25万个SNP）和候选基因研究，旨在鉴定遗传变异和基因，负责对他汀类药物的差异反应，以及来自候选基因研究的数据，旨在识别与构成疾病相关的定量表型，质量促进症和pro症的遗传型，旨在鉴定遗传变异。这些研究的发现可能有助于理解影响心血管疾病及其治疗的遗传因素。此外，将开发和分发实施统计方法的用户友好软件，使其他研究人员在全球范围内进行类似研究的研究人员可以访问这些工具。这些工具有可能提高旨在确定常见疾病的基本遗传基础的研究的有效性和效率，这有可能导致维持健康和预防疾病的新治疗策略。公共卫生相关性：该项目将生成统计工具，用于分析旨在帮助了解常见疾病和药物反应的遗传基础的大规模研究。这些工具具有提高此类研究的有效性和效率的潜力，有可能导致维持健康和预防疾病的新治疗策略。