Genomic Convergence in Alzheimer Disease

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。我们的小组已经成功地使用这种模式在两个APOE风险效应的识别和最近的谷胱甘肽S-转移酶欧米茄-1（GSTO 1）的发病年龄（AAO）在AD的影响。有一个新的认识的力量，纳入临床表型和发展临床亚表型在攻击复杂的疾病。此外，分子遗传学方法继续快速发展。全基因组关联（WGA）是一种新的方法，它允许直接评估来自整个基因组的300，000 - 1，000，000个SNP与AD的关联，并提供了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假阳性率也很高。调查数千个将达到名义意义的标记中的每一个是一项不祥且低效的任务。这个问题的一个解决方案是基因组融合方法，它整合了不同的数据类型，以筛选大量的现有数据，从而优先考虑最佳候选基因进行深入分析。我们已经证明了这种方法的实用性与GSTO 1基因的鉴定。因此，我们提出了AD的WGA研究，并将使用现有的连锁，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）数据过滤结果。在多项研究中确定的一小部分候选基因将成为后续分析的重点。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。我们的独特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，并将对未来的研究开发和评估干预措施至关重要。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。这种理解对于未来的研究至关重要，以制定早期干预措施和更有针对性的治疗方法，这将有助于减轻疾病患者及其家人的痛苦。

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.