Alzheimer's Disease Genetics Consortium

描述（由申请人提供）：GWA方法现已成功用于检测越来越多的遗传复杂疾病的疾病相关易感基因。在这些研究中，成功的一个关键因素是样本量足够大，以便有足够的能力检测具有全基因组显著性的适度效应量的基因。检测给定效应量所需的样本取决于遗传异质性，这对于AD是难以预测的。然而，对于其他疾病，如2型糖尿病，在4，549例病例和5，579例对照的初始发现队列（1期，3项研究合并）中检测到了比值比约为1.3的易感基因，随后是10，053例病例和12，289例对照的复制数据集（2期）。目前的基因分型平台允许使用~ 550，000个SNP覆盖人类基因组的~92%的连锁不平衡景观。通常，在发现阶段名义上检测到的前SNP的约1%然后在复制数据集中进行测试。在最初的发现实验中，验证的基因座不在SNP的顶级中并不罕见。因此，大量的重复样本对这些研究的成功至关重要。就准确诊断而言，复制样本的质量对于GWA研究的成功至关重要，因为不正确的诊断可能导致确认真实基因座的能力降低。ADGC正在形成，以合作使用AD研究社区的集体资源来识别AD基因。临床、神经病理学、分子和统计学专业知识存在于AD研究界。此外，ADC还收集了许多所需的表型数据和DNA样本。ADGC的主要目标是确定影响AD易感性的基因变异。易感基因可能影响发病年龄、疾病前驱期和轻度认知障碍（MCI）阶段的进展速度。次要目标是鉴定影响特定AD相关内表型的基因，例如神经病理学特征（例如淀粉样蛋白负荷、缠结负荷等）、生物标志物测量[例如脑脊液（CSF）A？和tau水平、MRI测量]、疾病进展速率、对环境因素（例如药物、非药物环境因素）的反应。

DESCRIPTION (provided by applicant): GWA methods have now been successfully used to detect disease-related susceptibility genes for a growing 1st of genetically complex disorders. In these studies, a critical element for success is that the sample size be large enough so that there is adequately power to detect genes with modest effect sizes at genome-wide significance. The sample needed to detect a given effect size depends on genetic heterogeneity, which is difficult to predict for AD. However, for other diseases such as type 2 diabetes, susceptibility genes with odds ratios of ~1.3 have been detected with initial discovery cohorts of 4,549 cases and 5,579 controls (phase 1, 3 studies combined) followed by a replication dataset of 10,053 cases and 12,289 controls (phase 2). Current genotyping platforms permit coverage of ~92% of the linkage disequilibrium landscape of the human genome using ~550,000 SNP's. Typically, ~1% of the top SNP's nominally detected in the discovery phase are then tested in the replication dataset. It is not unusual that validated loci not in the top tier of SNP's from the initial discovery experiment. Thus a large replication samples is critical to the success of these studies. The quality of the replication samples in terms of accurate diagnosis is critical to the success of GWA studies because incorrect diagnoses can result in reduced power to confirm true loci. The ADGC is being formed to collaboratively use the collective resources of AD research community to identify AD genes. The clinical, neuropathologic, molecular and statistical expertise exists within the AD research community. Also, much of the needed phenotype data and DNA samples also exist, gathered by the ADCs. The primary goal of the ADGC will be to identify variability in genes that influences susceptibility to AD. Susceptibility genes potentially influence onset-age, rate of progression through the prodromal and mild cognitive impairment (MCI) phase of the disease. Secondary goals are to identify genes that influence specific AD- related endophenotypes such as neuropathology features (e.g. amyloid load, tangle load, etc), biomarker measures [e.g. cerebral spinal fluid (CSF) A? and tau levels, MRI measures], rate-of-disease progression, responses to environmental factors (e.g. drugs, non-pharmaceutical environmental factors).