Bioinformatics Strategies for Genome-Wide Association Studies

描述（由申请人提供）：全基因组关联研究（GWAS）是常见的，尽管缺乏全面的生物信息学方法来分析数据。常用的分析方法是采用参数统计，然后对执行的大量测试进行调整，以限制假阳性（即类型1错误）。有些人更喜欢这种不可知论的方法，因为没有假设哪些基因或基因组区域可能是重要的。这种逻辑表明，数据应该告诉我们重要的基因变异在哪里。我们提出的研究计划的目标是将这种不可知方法与生物信息学方法进行具体比较，生物信息学方法根据有关生化途径和基因功能的专业知识选择相关的snp。我们建议开发一种生物信息学方法，利用所研究基因的生物学和疾病的分子病理学知识，从GWAS中选择snp （AIM 1）。我们将修改和扩展探索性视觉分析（EVA）数据库和软件，该数据库和软件最初是为NLM BISTI项目的试点资金设计的微阵列研究。然后，我们将使用这种生物信息学方法以及一种不确定的统计学方法，在荷兰格罗宁根（AIM 2）的PREVEND研究中，在大量基于人群的白种人样本（n=2000）中检测与组织纤溶酶原激活剂（t-PA）和纤溶酶原激活剂抑制剂1 （PAI-1）血浆水平相关的snp。在PREVEND研究中通过两种方法确定的snp将首先在荷兰鹿特丹研究中基于人群的高加索人样本（n=2000）中进行评估，然后在非洲加纳心脏研究（AIM 3）中基于人群的黑人样本（n=2000）中进行验证。最后，我们将具体比较使用统计方法和生物信息学方法（AIM 4）复制和验证的snp数量和数量。我们的工作假设是，我们将获得更多的验证，因此更真实的snp使用生物信息学方法。

DESCRIPTION (provided by applicant): Genome-wide association studies (GWAS) are commonplace despite the lack of a comprehensive bioinformatics approach to the analysis of the data. The common method of analysis is to employ parametric statistics and then adjust for the large number of tests performed to limit false-positives (i.e. type 1 errors). This agnostic approach is preferred by some because no assumptions are made about which genes or genomic regions might be important. This logic suggests that the data should tell us where the important genetic variants are. The goal of our proposed research program is to specifically compare this agnostic approach with a bioinformatics approach that selects associated SNPs based on expert knowledge about biochemical pathways and gene function. We propose to develop a bioinformatics approach for selecting SNPs from a GWAS using knowledge about the biology of the genes being studied and the molecular pathology of disease (AIM 1). We will modify and extend the Exploratory Visual Analysis (EVA) database and software that was originally designed for microarray studies with pilot funding from the NLM BISTI program. We will then use this bioinformatics approach along with an agnostic statistical approach for detecting SNPs associated with plasma levels of tissue plasminogen activator (t-PA) and plasminogen activator inhibitor one (PAI-1) in a large population-based sample of Caucasians (n=2000) from the PREVEND study in Groningen, The Netherlands (AIM 2). Those SNPs identified by both methods in the PREVEND study will be evaluated first for replication in an independent population-based sample of Caucasians (n=2000) from the Rotterdam Study in the Netherlands and then for validation in a population-based sample of Blacks (n=2000) from the HeART Study in Ghana, Africa (AIM 3). Finally, we will specifically compare how many and which SNPs replicate and validate using the statistical approach and the bioinformatics approach (AIM 4). Our working hypothesis is that we will obtain more validated and hence more real SNPs using the bioinformatics approach.