Comparative Approach to Genomics of Complex Traits

描述：(申请人提供)遗传易感性的决定因素 因为大多数常见的特征都是复杂的，可能涉及到 多种基因变异。这些基因功能上的变异会影响关键的 表型可以代表氨基酸替代，也可以代表基因外 可能影响表达式级别的差异；在大多数情况下，这些差异是 单核苷酸多态(SNP)。尽管有人想要化验一下 这些基因变异以一种完全公正的方式，最终测量 每个基因的每个变种的贡献，这显然是不切实际的 这个时间点。另一种策略是最大限度地识别这些基因 可能对疾病变异做出贡献，识别变异 在这组基因中，然后进行关联研究，以连接基因 带有疾病表型的变异。以心血管疾病为模型 系统，本提案描述了一种多维的总体方法 有问题。特别是，我们将使用多种方法来识别 最有可能对疾病变异做出贡献的候选基因 病人的群体。这项工作将利用三个独特的临床 杜克大学的资源。首先，我们已经开始收集大型系列 心脏移植供者的主动脉样本作为血管组织的来源 用于基因表达分析。这些样本在数量上是唯一的 样本(数百个)以及表型范围：早期阶段 动脉粥样硬化是一种晚期疾病。因此，它提供了一个 使基因表达谱与疾病发展相匹配的机会 以一种非常独特的方式。这将是组件1以及 构成部分5中的统计努力。第二，对人类基因组遗传学的大规模研究 早发性心血管疾病，代表着杜克大学 调查人员和葛兰素史克提供了识别基因座的机会 与疾病的发展有关。这提供了一种机制 在没有任何偏见的情况下识别其他候选基因， 包括该基因是否真的在心血管组织内发挥作用 不。这代表了组成部分2的重点，以及生物信息学方面的努力 构成部分1和2的综合努力，采取不同的 识别候选基因的方法，然后将成为 底物以发现这组基因中的SNPs(成分3)。大部分 这项工作将利用关于SNPs的现有信息以及 其他针对心血管疾病的主要研究。但是，它也将 有必要在这一组成部分内作出努力，以尽可能详尽地确定 可能是那些序列变体，然后可以作为检测的对象 临床人群。第三，可能也是其中最重要的资产 该计划是杜克大学心血管数据库，这是一项由大约30名 几年前在杜克大学跟踪研究每一种心血管疾病的临床过程 有耐心的。因此，我们现在可以接触到40,000多名患者 定期跟踪，创建无与伦比的临床数据集。 这一临床数据集为这项研究提供了一个完全独特的资源， 无论是从患者临床数据的数量还是质量上都允许 候选基因变异与疾病变异的验证。因此， 成分3中确定的单核苷酸多态将进入大规模的基因分型计划 (组件4)，将这些候选基因带到验证点。一位少校 在如此大规模的事业中，挑战将是统计能力 在复杂的情况下找出联系。组件5，将开发 理解复杂基因表达数据集的方法也将 发展用于复杂基因分型分析的统计方法 学习。该计划还将增强和整合现有的和 杜克大学发展生物信息学和基因组技术教育项目 (组件6)。这种协同效应将对该计划产生明显的好处，为 在每个关键领域来自多个学科的有才华的研究人员 这个项目。因此，我们的项目将推进基因组科学的前沿。 和技术领域的共同特点。我们节目的高潮 将为临床医生提供必要的工具，以改善风险分层 并设计新的预防和治疗策略。

DESCRIPTION: (provided by applicant) The determinants of genetic susceptibility for most common traits are complex, likely to involve contributions from multiple gene variants. These variations in gene function that affect critical phenotypes can represent amino acid replacements as well as extragenic differences that might affect expression levels; in most instances, these are single nucleotide polymorphisms (SNPs). Although one would like to assay for these gene variants in a completely unbiased fashion, ultimately measuring the contribution of every variant of every gene, this is obviously impractical at this point in time. An alternative strategy is to identify those genes most likely to make contributions to disease variation, identify the variations within this group of genes, and then conduct association studies to link gene variants with disease phenotype. Using cardiovascular disease as a model system, this proposal describes a multi-dimensional approach to this overall problem. In particular, we will use multiple methods for the identification of candidate genes most likely to make contributions to disease variation within populations of patients. This work will take advantage of three unique clinical resources here at Duke. First, we have begun the collection of a large series of aorta samples from heart transplant donors as a source of vascular tissue for gene expression analysis. These samples are unique in the volume of the samples (hundreds) as well as the range of phenotype: early stages of atherosclerosis to advanced forms of the disease. As such, it provides an opportunity to match gene expression profiles with the development of disease in a very unique way. This will be the focus of work in Component 1 as well as statistical efforts in Component 5. Second, a large study of the genetics of early onset cardiovascular disease, representing a collaboration between Duke investigators and GlaxoSmith-Kline, offers the opportunity to identify loci that are linked with the development of disease. This provides a mechanism for the identification of additional candidate genes without any bias whatsoever, including whether the gene actually functions within cardiovascular tissue or not. This represents the focus of Component 2, and bioinformatic efforts in Component 6. The combined efforts of Component I and 2, taking different approaches to the identification of candidate genes, will then be the source of substrate to discover SNPs within this group of genes (Component 3). Much of this work will take advantage of existing information regarding SNPs as well as other major studies directed at cardiovascular disease. But, it will also necessitate efforts within this component to identify as exhaustively as possible those sequence variants that can then be the subject for assays in clinical populations. Third, and possibly the most important asset of this program, is the Duke Cardiovascular Database, an effort initiated some thirty years ago at Duke to follow the clinical course of every cardiovascular disease patient. As such, we now have access to over 40,000 patients who are being followed on a regular basis, creating a clinical dataset that is unmatched. This clinical dataset provides a completely unique resource for this study, both from the quantity as well as the quality of patient clinical data to allow the validation of candidate gene variants with disease variation. Thus, the SNPs identified in Component 3 will go into an expansive genotyping program (Component 4), to bring these candidate genes to a point of validation. A major challenge in an undertaking of such magnitude will be the statistical power to find associations in complex situations. Component 5, will develop the methodologies for understanding the complex gene expression datasets, will also develop the statistical approaches to the analysis of the complex genotyping studies. The program will also enhance and integrate with existing and developing educational programs in bioinformatics and genome technology at Duke (Component 6). This synergy will be of clear benefit to the program, bringing in talented investigators from multiple disciplines in each area critical for the program. Hence, our project will advance the frontiers of genome sciences and technology in the field of common traits. The culmination of our program will provide essential tools to clinicians to improve risk stratification of patients and to design novel preventive and therapeutic strategies.