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Mechanisms of action of SNPs associated with common disease

Mechanisms of action of SNPs associated with common disease
SNP与常见疾病相关的作用机制
批准号:
8116228
负责人:
OSNAT HERZBERG
金额:
$27.6万
依托单位国家:
美国
项目类别:
财政年份:
2009
资助国家:
美国
项目状态:
已结题
起止时间:
2009-09-30 至 2011-08-31

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项目成果

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中文摘要
翻译
描述(由申请人提供):最近的全基因组关联研究已经导致可靠地鉴定出与许多常见人类疾病的发病率增加相关的多种常见SNP。这些结果提供了诱人的前景的主要新见解的性质复杂性状疾病的一般和具体的了解机制的主要常见的人类疾病。获得最大的洞察力并不简单,需要开发和应用一系列计算和实验技术。该项目的目标是开发和应用这些方法,以目前可用的关联研究和新的结果,将很快提供。该项目的重点是基因和基因产物水平对人类常见疾病的贡献。这些过程构成了SNPs影响疾病风险的多层次机制的关键部分,充分了解它们的贡献对于有效研究更高层次的途径和子系统的影响至关重要。我们采取双管齐下的办法。首先,计算方法将用于确定一组可能的候选SNP在已建立的易感基因座常见疾病。对于这些SNP中的每一个,检查一组可能的作用机制。具体而言,影响基因表达水平;信使RNA结构;加工;稳定性和剪接;以及体内蛋白质折叠、结构、稳定性和功能。其次,对于已经明确发挥重要作用的机制之一,蛋白质中的错义SNP,我们将实验研究由此产生的氨基酸取代对体外功能的影响。为此,我们将克隆、表达和纯化一些受影响的蛋白质和SNP变体,并研究它们的性质,特别是结构稳定性和与适当结合伴侣的相互作用。最初的研究是关于常见疾病的大规模WTCCC全基因组关联数据,以及克罗恩病的额外关联数据。随着更多的数据变得可用,计算分析将扩展到包括额外的关联研究以及重新测序数据。结果将是一个全面的计算分析的机制,SNPs影响疾病的易感性在基因和蛋白质水平,并在高优先级的情况下,广泛的实验表征的影响相关的错义SNPs蛋白质功能。所有数据都将以可下载的形式提供,并通过一个交互式网络界面提供。此外,我们将开发一个注释和讨论工具,以便最大限度地利用结果。 公共卫生相关性:新的数据首次可靠地确定了个体间遗传变异与对一些常见人类疾病的易感性之间的许多关联。这些数据为研究疾病的潜在机制开辟了道路,从而开发了新的治疗方法。我们将使用计算和实验方法来确定基因组和蛋白质水平的机制,利用这些新数据。
英文摘要
DESCRIPTION (provided by applicant): Recent genome wide association studies have led to the reliable identification of multiple common SNPs associated with increased incidence of a number of common human diseases. These results offer the tantalizing prospect of major new insights into the nature of complex trait disease in general and specific understanding of the mechanisms underlying major common human diseases. Deriving maximum insight is not trivial, and requires the development and application of a range of computational and experimental techniques. The goal of this project is to develop and apply such methods to the currently available association studies and new results that will be available shortly. The project focuses on the gene and gene product level contributions to common human disease. These processes constitute a key part of the multiple level mechanisms by which SNPs influence disease risk, and a full understanding of their contributions is essential to effective investigation of higher level pathway and subsystems impact. We take a two pronged approach. First, computational methods will be used to identify a set of possible candidate SNPs in established susceptibility loci for common diseases. For each of these SNPs, a set of possible mechanisms of action is examined. Specifically, effects on gene expression level; messenger RNA structure; processing; stability and splicing; and on protein folding, structure, stability and function in vivo. Secondly, for one of the mechanisms that it is already clear plays a significant role, missense SNPs in proteins, we will experimentally investigate the impact of the resulting amino acid substitutions on in vitro function. To this end, we will clone, express and purify a number of affected proteins and the SNP variants, and investigate their properties, particularly structural stability and interaction with appropriate binding partners. Initial studies are on the large scale WTCCC genome wide association data for common diseases, and on additional association data for Crohn's disease. As more data become available, the computational analysis will be extended to include additional association studies as well as re-sequencing data. The outcome will be a comprehensive computational analysis of the mechanisms by which SNPs influence disease susceptibility at the gene and protein level, and in high priority cases, extensive experimental characterization of the impact of relevant missense SNPs on protein function. All data will be made available in downloadable form, as well as through an interactive web interface. In addition, we will develop an annotation and discussion facility, so that the results can be maximally exploited. PUBLIC HEALTH RELEVANCE: New data are for the first time reliably establishing many associations between genetic variation among individuals and susceptibility to a number of common human diseases. These data open the way for investigation of the mechanisms underlying disease and hence the development of new therapies. We will use computational and experimental methods to determine the genome and protein level mechanisms, making use of these new data.
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