Genomic Convergence in Alzheimer Disease

描述(申请人提供)：在过去，阿尔茨海默病(AD)遗传学领域受益于创新范例的发展，这些范例结合了最新的基因组技术和原始的患者数据，以剖析其复杂的病因。我们的团队已经成功地将这一范例用于识别载脂蛋白E风险效应和最近发现的AD的谷胱甘肽S转移酶欧米茄-1(GSTO-1)发病年龄效应。人们对结合临床表型和发展临床亚型在治疗复杂疾病中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)是一种新的方法，它允许直接评估整个基因组中300,000-1,000,000个SNP与AD的关联，并提供了比连锁研究更详细的基因组检查的机会。然而，尽管WGA的信息量非常高，但使用标准分析的初始假阳性率也很高。研究将达到名义意义的数千个标记中的每一个都是一项不祥且效率低下的任务。这个问题的一种解决方案是基因组融合方法，它整合了不同的数据类型，以筛选现有数据量，以确定最佳候选基因的优先顺序，以便进行密集分析。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，我们建议对AD进行WGA研究，并将使用现有的连锁、候选基因和我们最近生成的微阵列和基因表达序列分析(SAGE)数据来筛选结果。在多项研究中确定的一小部分候选基因将成为密集后续分析的重点。尤其重要的是，我们能够在新收集的病例对照数据集中使用关于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与现有信息相结合，以阐明导致这种重要的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阿尔茨海默病遗传病因的理解。这一认识将对未来的研究至关重要，以制定早期干预和更有针对性的治疗，这将有助于减轻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.