Comparative Genomics of Non-Coding Regions to Facilitate Translational Research

非编码区域的比较基因组学促进转化研究

• 批准号: 8055442
• 负责人: INNA DUBCHAK
• 金额: $ 80.9万
• 依托单位: UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-04-15 至 2013-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8055442
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DESCRIPTION (provided by applicant): There is increasing evidence that variations in non-coding sequences that regulate gene expression play an important role in human disease. However, the identification of non-coding, regulatory polymorphisms contributing to disease is limited by our inability to identify which variants reside within gene regulatory sequences. Multiple recent studies indicate that highly conserved non-coding regions identified by comparative sequence analysis often possess gene regulatory activity. Thus, sequence conservation alone can prioritize noncoding sequences for gene regulatory function. While it is reasonable to expect that variations in highly conserved non-coding regions are more likely to be deleterious, very little research has been directed to validate this hypothesis in clinical populations. Accordingly, the goal of this proposal is to investigate the utility of comparative genomics for the prioritization of functional non-coding sequence variations in several disease models and to build computational resources enabling the employment of this strategy by clinical investigators. To accomplish this, we will first classify non- coding sequence variations based on a range of comparative genomic criteria to estimate their likelihood of deleteriousness. Collaboration with i2b2 clinical investigators will enable us to test the validity of this classification in a clinical study of asthma. Building on the success of VISTA, our suite of comparative genomic tools already widely used by the biomedical community, we will develop a user-friendly "clinical comparative genome portal" to automate this process. Finally, integration of the portal into i2b2's Hive architecture and clinical research chart will enable clinical investigators to exploit the synergies of computational and clinical sequence-based data in their analysis of i2b2 rich clinical databases and to accelerate the translation of clinical genetic data into functional insights. To facilitate the dissemination and adoption of the comparative portal, in coordination with i2b2 we will organize online and offline training activities specifically targeted at clinical users. PUBLIC HEALTH REVELANCE: Genetic studies of common human diseases will become increasingly frequent in the near future, thanks to advances in genomic science and sequencing technologies. Common diseases, such as diabetes and cardiovascular disease, are among the major causes of human morbidity and mortality. Novel computational approaches are needed to help translate the genetic information obtained in such studies into functional information that can be used to understand the mechanisms of disease and develop new diagnostic and therapeutic approaches. In this proposal, we will use our expertise with comparative sequence analysis to develop a computational platform to help integrate computational and clinical data based on the sequence of the human genome and accelerate the translation of genetic findings into functional and medical insights. This platform will be widely available to clinical investigators through the NIH-supported i2b2 clinical research chart.

描述（申请人提供）：越来越多的证据表明，调节基因表达的非编码序列的变异在人类疾病中发挥着重要作用。然而，非编码的，调节多态性的疾病的鉴定是有限的，我们无法确定哪些变异存在于基因调控序列。最近的多项研究表明，通过比较序列分析鉴定的高度保守的非编码区通常具有基因调控活性。因此，仅序列保守性就可以优先考虑非编码序列的基因调控功能。虽然可以合理地预期高度保守的非编码区的变异更可能是有害的，但很少有研究针对临床人群验证这一假设。因此，本提案的目标是研究比较基因组学在几种疾病模型中对功能性非编码序列变异进行优先排序的实用性，并建立使临床研究者能够采用这种策略的计算资源。为了实现这一点，我们将首先基于一系列比较基因组标准对非编码序列变异进行分类，以估计它们的遗传可能性。与i2b2临床研究者的合作将使我们能够在哮喘的临床研究中测试这种分类的有效性。在VISTA成功的基础上，我们的一套比较基因组工具已经被生物医学界广泛使用，我们将开发一个用户友好的“临床比较基因组门户网站”，以自动化这一过程。最后，将门户网站集成到i2b2的Hive架构和临床研究图表中，将使临床研究人员能够在分析i2b2丰富的临床数据库时利用基于计算和临床序列的数据的协同作用，并加速将临床遗传数据转化为功能性见解。为了促进比较门户网站的传播和采用，我们将与i2b2协调，组织专门针对临床用户的在线和离线培训活动。公共卫生部门：由于基因组科学和测序技术的进步，在不久的将来，对人类常见疾病的基因研究将越来越频繁。糖尿病和心血管疾病等常见疾病是人类发病率和死亡率的主要原因。需要新的计算方法来帮助将这些研究中获得的遗传信息转化为可用于理解疾病机制和开发新的诊断和治疗方法的功能信息。在这项提案中，我们将利用我们在比较序列分析方面的专业知识开发一个计算平台，以帮助整合基于人类基因组序列的计算和临床数据，并加速将遗传发现转化为功能和医学见解。该平台将通过NIH支持的i2b2临床研究图表广泛提供给临床研究者。