Genomic Convergence in Alzheimer Disease

阿尔茨海默病的基因组趋同

• 批准号: 7382451
• 负责人: Margaret A. Pericak-Vance
• 金额: $ 121.08万
• 依托单位: UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-04-01 至 2013-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7382451
• 关键词: Advocate; Age; Age of Onset; Alzheimer' s Disease; Alzheimer' s disease risk; Apolipoprotein E; Candidate Disease Gene; Clinic; Clinical; Clinical Data; Complement; Complex; Data; Data Analyses; Data Linkages; Data Set; Development; Disease; Disease regression; Disease susceptibility; Early Intervention; Etiology; Evaluation; Family; Floods; Future; Gene Expression; Genes; Genetic; Genetic Epistasis; Genetic Predisposition to Disease; Genome; Genomics; Genotype; Glutathione S-Transferase; Heterogeneity; Individual; Intervention; Joints; Knowledge; Literature; Methods; Modeling; Molecular; Molecular Genetics; Movement; National Institute of Mental Health; Neurodegenerative Disorders; Numbers; Patients; Positioning Attribute; Predisposition; Process; Publications; Publishing; Rate; Risk; Risk Factors; Running; Solutions; Standards of Weights and Measures; Susceptibility Gene; Symptoms; Technology; Testing; Variant; base; case control; clinical phenotype; falls; follow-up; genome wide association study; innovation; interest; novel strategies; research study; serial analysis of gene expression

DESCRIPTION (provided by applicant): In the past, the field of Alzheimer disease (AD) genetics has benefited from the development of innovative paradigms that incorporate the latest genomic technologies combined with pristine patient data to dissect its complex etiology. Our group has successfully used this paradigm in both the identification of the APOE risk effect and more recently the glutathione S-transferase Omega-1 (GSTO1) age at onset (AAO) effect in AD. There is a new appreciation of the power of incorporating clinical phenotypes and developing clinical subphenotypes in attacking complex disorders. In addition, molecular genetic methods have continued to advance rapidly. Whole genome association (WGA) is a new approach that allows the direct evaluation of 300,000- 1,000,000 SNPs from across the genome for association with AD and provides the opportunity to perform a much more detailed examination of the genome than linkage studies. However, while the information content of WGA is extraordinarily high, the initial false positive rate using standard analyses is also high. Investigating each of the thousands of markers that will reach nominal significance is an ominous and inefficient task. One solution to this problem is the genomic convergence approach, which integrates disparate data types to sift through the volumes of existing data to prioritize the best candidate genes for intensive analysis. We have already demonstrated the utility of this approach with the identification of the GSTO1 gene. Thus we are proposing a WGA study of AD and will filter the results using existing linkage, candidate gene, and our recently generated microarray and Serial Analysis of Gene Expression (SAGE) data. A small set of candidate genes identified in multiple of these studies will be the focus of intensive follow-up analysis. Of particular importance will be our ability to follow-up using detailed clinical data on movement and psychiatric symptoms in a newly collected case-control dataset. Our unique position will enable us to marry the most powerful of new genomic approaches, WGA, to existing information to elucidate additional genetic effects contributing to this important neurodegenerative disease. The knowledge derived from this study will further our understanding of AD and will be crucial for future studies to develop and evaluate interventions. The knowledge derived from this study will further our understanding of the genetic etiology of AD. This understanding will be crucial for future studies to develop early interventions and more focused treatments, which will help alleviate the suffering of those with the disease and their families.

描述（由申请人提供）：过去，阿尔茨海默病（AD）遗传学领域受益于创新范式的发展，这些创新范式将最新的基因组技术与原始患者数据相结合，以剖析其复杂的病因。我们的小组已成功地使用这种范例来识别 APOE 风险效应以及最近的 AD 中谷胱甘肽 S 转移酶 Omega-1 (GSTO1) 发病年龄 (AAO) 效应。人们对整合临床表型和开发临床亚表型在治疗复杂疾病方面的力量有了新的认识。此外，分子遗传学方法继续快速发展。全基因组关联 (WGA) 是一种新方法，可直接评估整个基因组中 300,000-1,000,000 个 SNP 与 AD 的关联，并提供比连锁研究更详细的基因组检查的机会。然而，虽然 WGA 的信息含量非常高，但使用标准分析的初始误报率也很高。对数千个具有名义意义的标记中的每一个进行研究是一项不祥且低效的任务。该问题的一种解决方案是基因组收敛方法，该方法集成不同的数据类型来筛选大量现有数据，以优先考虑最佳候选基因进行深入分析。我们已经通过 GSTO1 基因的鉴定证明了这种方法的实用性。因此，我们提议对 AD 进行 WGA 研究，并将使用现有连锁、候选基因以及我们最近生成的微阵列和基因表达系列分析 (SAGE) 数据来过滤结果。在多项研究中鉴定出的一小组候选基因将成为深入后续分析的重点。特别重要的是我们能够在新收集的病例对照数据集中使用有关运动和精神症状的详细临床数据进行随访。我们独特的地位将使我们能够将最强大的新基因组方法 WGA 与现有信息结合起来，以阐明导致这种重要神经退行性疾病的其他遗传效应。从这项研究中获得的知识将进一步加深我们对 AD 的理解，并且对于未来开发和评估干预措施的研究至关重要。从这项研究中获得的知识将进一步加深我们对 AD 遗传病因学的理解。这种理解对于未来研究制定早期干预措施和更有针对性的治疗方法至关重要，这将有助于减轻患者及其家人的痛苦。