AD Gene Discovery: Exome Chip, New Endophenotypes & Functional Studies in CHARGE

描述（由申请人提供）：这项竞争性续期拨款申请旨在继续一项合作，该合作使用大型前瞻性流行病学队列（基因组流行病学心脏和衰老研究队列（CHARGE））的全基因组关联数据，以及>万名患者中> - 60年的风险因素、痴呆、AD、MRI和认知内表型数据，以确定阿尔茨海默病（AD）风险的新基因/位点。它发表了50篇论文，帮助鉴定了9个新的AD基因座（在CHARGE-lead的一篇论文中，BIN1和EPHA1首次达到了全基因组意义），以及8个AD内表型基因座。CHARGE帮助建立了国际阿尔茨海默病基因组学项目（IGAP），并与bbbb25个队列/联盟建立了合作关系，如通过meta分析增强神经成像（ENIGMA）联盟。然而，迄今为止鉴定的基因共同解释了观察到的60-80%阿尔茨海默病遗传率的<35%。缺失的遗传力可以部分解释为多种低频或罕见的遗传变异，这些变异可以通过分析原始CHARGE和另外4个队列（CHARGE- plus样本：n=45,910, ~4058 AD）中的外显子组芯片（EC）数据来经济有效地检测到。在这个应用中，我们提出以下建议：目的1：使用EC数据搜索与临床AD事件和AD内表型相关的罕见遗传变异。我们已经证明，AD内表型的GWAS，例如海马和总脑容量，言语记忆，可以识别与大脑衰老有关的新位点，以及AD背后的生物学途径。我们建议使用EC数据来寻找与这些已建立的AD内表型相关的罕见变异。目的2：为了了解临床前阿尔茨海默病，即临床阿尔茨海默病前1-2年的早期病理变化阶段，在此阶段阿尔茨海默病最有可能接受干预，我们提出了GWAS和EC分析整个CHARGE-Plus和30-65岁年轻队列中新兴的、敏感的MRI内表型、认知和循环生物标志物（血浆淀粉样蛋白和聚集蛋白）数据。目标3：为了更好地了解已鉴定基因的生物学、流行病学和公共卫生意义，我们将研究已知基因座和新基因座之间的基因-基因相互作用，以及基因-环境相互作用（目标1和2基因座与中年胆固醇和BMI的相互作用）和全基因组的相互作用。CHARGE队列具有中年暴露于各种环境协变量的发病前数据，具有独特的优势，可以研究这种相互作用，并在需要时允许分析其他协变量。目标4：最后，我们将利用CHARGE中创建的生物信息学注释数据库、可用的系统和大脑mRNA、miRNA、甲基化数据（分别为n~8000和750）、尸检大脑和果蝇tau和-淀粉样蛋白模型中的海马神经元表达来探索鉴定变异的生物学特性。我们寻求适度的分析资源，利用现有的表型和基因型数据价值数百万，发现新的阿尔茨海默病基因和潜在的新的预防和治疗方法。

DESCRIPTION (provided by applicant): This competitive renewal grant application seeks to continue a collaboration that used genome-wide association data in large, prospective epidemiological cohorts, the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE), with >2-6 decades of risk factor, dementia, AD, MRI and cognitive endophenotype data in >30,000 persons to identify new genes/loci underlying the risk of Alzheimer's disease (AD). It produced >50 publications, helped identify 9 novel loci for AD (BIN1 & EPHA1 first reached genome-wide significance in a CHARGE-lead publication), and 8 for AD endophenotypes. CHARGE helped found the International Genomics of Alzheimer Project (IGAP) and established collaborations with >25 cohorts/ consortia such as the Enhancing Neuro Imaging through Meta-Analysis (ENIGMA) consortium. However, genes identified to date collectively explain <35% of the observed AD heritability of 60-80%. The missing heritability could be partly explained by multiple low-frequency or rare genetic variants that can be detected cost-effectively via analysis of exome chip (EC) data now available in the original CHARGE and in 4 additional cohorts (the CHARGE-Plus sample: n=45,910, ~4058 with AD). In this application we propose the following: Aim 1: To use EC data to search for rare genetic variants related to incident clinical AD, and to AD endophenotypes. We have shown that GWAS of AD endophenotypes, e.g. hippocampal & total brain volumes, verbal memory can identify novel loci implicated in brain aging, and biological pathways underlying AD. We propose to use EC data to search for rare variants related to these established AD endophenotypes. Aim 2: To understand preclinical AD, the stage of early pathological changes 1-2 decades before clinical AD, during which AD is likely most amenable to intervention, we propose GWAS and EC analyses of emerging novel, sensitive MRI endophenotypes, cognitive and circulating biomarker (plasma ¿-amyloid and clusterin) data in the entire CHARGE-Plus and in younger cohorts aged 30-65 years. Aim 3: To better understand the biology, epidemiological and public health significance of the identified genes we will study gene-gene interactions between known and novel loci, and gene-environment interactions both targeted (interaction of loci from Aims 1&2 with midlife cholesterol and BMI), and genome-wide. The CHARGE cohorts, with premorbid data on mid-life exposure to a wide-range of environmental covariates, are uniquely positioned to study such interactions and permit analyses of other covariates when indicated. Aim 4: Finally, we will explore the biology of the identified variants using bio-informatics annotation databases created in CHARGE, available systemic and brain mRNA, miRNA, methylation data (n~8000 & 750, respectively), hippocampal neuronal expression in autopsy brains and in Drosophila tau & ¿-amyloid models. We seek modest analytic resources to leverage existing phenotypic and genotypic data worth millions, to discover new AD genes and potentially novel prevention and treatment approaches for AD.