Comparative Approach to Genomics of Complex Traits

描述：（由申请人提供）遗传易感性的决定因素 因为大多数常见特征都很复杂，可能涉及以下因素的贡献 多种基因变异这些基因功能上的变异 表型可以代表氨基酸替换以及基因外 可能影响表达水平的差异;在大多数情况下， 单核苷酸多态性（SNP）。虽然有人想分析一下 这些基因变异在一个完全公正的方式，最终测量 每个基因的每个变体的贡献，这显然是不切实际的， 这个时间点。另一种策略是找出那些基因最 可能对疾病变异做出贡献， 在这组基因中，然后进行关联研究，将基因 具有疾病表型的变体。以心血管疾病为模型 系统，该提案描述了一个多维度的方法，这一整体 问题.特别是，我们将使用多种方法来识别 候选基因最有可能对疾病变异做出贡献， 患者人群。这项工作将利用三个独特的临床 杜克的资源首先，我们已经开始收集一个大系列 心脏移植供体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独一无二的， 样本（数百个）以及表型的范围： 动脉粥样硬化到疾病的晚期形式。因此，它提供了一个 将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。这将是构成部分1的工作重点， 第五部分统计工作。第二，一项关于遗传学的大型研究， 早发性心血管疾病，代表了杜克大学 研究人员和葛兰素史克公司，提供了机会，以确定基因座 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鉴定额外的候选基因， 包括该基因是否真的在心血管组织中起作用， 没有这代表了组件2的重点， 构成部分6.第一部分和第二部分的共同努力， 候选基因的鉴定方法，将成为 发现该组基因内的SNP的底物（组分3）。大部分 这项工作将利用现有的关于SNPs的信息， 其他针对心血管疾病的主要研究。但是， 有必要在这一构成部分内作出努力， 可能的是那些序列变体，其然后可以是用于测定的对象， 临床人群。第三，也可能是最重要的资产， 杜克心血管数据库是一个由大约30个 几年前在杜克大学跟踪每种心血管疾病的临床过程 病人因此，我们现在可以接触到超过40，000名正在接受 定期跟踪，创建无与伦比的临床数据集。 该临床数据集为本研究提供了完全独特的资源， 从患者临床数据的数量和质量来看， 候选基因变异与疾病变异的验证。因此 在组件3中识别的SNP将进入一个扩展的基因分型程序 （组件4），使这些候选基因的验证点。一个主要 在如此规模的事业中，挑战将是统计能力， 在复杂的情况下寻找关联。构成部分5将制定 理解复杂基因表达数据集的方法，也将 发展复杂基因分型分析的统计方法 问题研究该计划还将增强并整合现有的和 在杜克大学发展生物信息学和基因组技术的教育项目 （构成部分6）。这种协同作用将对该计划产生明显的好处， 来自各个领域的多个学科的有才华的调查人员， 节目因此，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征。我们节目的高潮 将为临床医生提供必要的工具，以改善 患者和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.