Integrated target discovery in Alzheimer's disease

项目摘要 阿尔茨海默病(AD)是一种高度遗传性的进行性神经退行性疾病。然而，基因 阿尔茨海默病的基础是复杂的，疾病风险的分子基础仍然知之甚少。因此，AD是 基本上无法治愈。现有的治疗阿尔茨海默病的药物只有轻微的效果。广告风险呈指数级增加 患病率由65~69岁的3.5%上升到85~80岁的30~40%。一种特殊的fic承诺 全基因组关联研究(GWAS)时代的一个重要结论是，遗传关联将转化为 疾病预测、预防和治疗发展，但我们还没有看到这一充满希望的fiLLED。而当 GWAs快速且可重复性地识别出与复杂疾病相关的遗传基因座，如AD、危重 限制因素限制了它们的翻译效果。绝大多数被识别的疾病风险基因座不在蛋白质内--fi 编码基因，而是位于顺式调节元件(CRE)中，其中许多是组织特异性fic。 这表明，大多数复杂的疾病风险变异会改变CRE的功能，从而影响基因表达， 这反过来又会影响疾病风险。这阻碍了我们对复杂疾病机制的理解，因为 通常很难单独从非编码的fi关联中识别相关的基因和途径。 此外，对候选区域的后续实验表征已被证明是昂贵和费力的。在……里面 在这项提案中，我们将采取统计和实验相结合的方法来解决这些关键限制 以确定影响AD风险的精确遗传变异和基因。在目标1中，我们将确定与AD相关的 Cres和他们的目标基因跨越了十个大脑区域。在目标2中，我们将开发统计方法来整合 基因表达和疾病关联数据，以确定其表达水平对疾病产生影响的基因 广告风险。最后，在目标3中，我们建议使用以下组合来验证目标1和目标2中的预测 高通量记者分析和靶向基因组工程。在整个提案中，我们将发展， 评估并公开利用多核计算环境的新分析工具，以及 将公开所有从大脑样本产生的遗传和表观遗传学数据。

Project Summary Alzheimers disease (AD) is a highly heritable, progressive neurodegenerative disorder. However, the genetic basis of AD is complex and the molecular basis of disease risk remains poorly understood. As a result, AD is essentially untreatable. Available drugs for AD are only marginally effective. AD risk increases exponentially with age with a prevalence of 3 5% by 65 69 years increasing to 30 40% by 85 89 years. A specific promise of the genome-wide association study (GWAS) era was that genetic associations would translate into improved disease prediction, prevention, and therapeutic development, but we have not seen this promise fulfilled. While GWAS have rapidly and reproducibly identified genetic loci associated with complex diseases such as AD, critical limitations restrict their translational impact. The vast majority of identified disease risk loci are not within protein- coding genes, but rather lie within cis-regulatory elements (CREs), many of which are tissue specific. This suggests that most complex disease risk variants modify the function of a CRE, which impacts gene expression, which, in turn, affect disease risk. This has impeded our understanding of complex disease mechanisms because it is typically difficult to identify the relevant genes and pathways from non-coding GWAS associations alone. Moreover, follow up experimental characterization of candidate regions has proven expensive and laborious. In this proposal, we will address these critical limitations by taking a combined statistical and experimental approach to identify the precise genetic variants and genes that affect AD risk. In Aim 1, we will identify AD-associated CREs and their target genes across ten brain regions. In Aim 2, we will develop statistical methods to integrate gene expression and disease association data to identify the genes whose expression levels causally affect AD risk. Finally, in Aim 3, we propose to validate the predictions made in Aims 1 and 2 with a combination of high throughput reporter assays and targeted genome engineering. Throughout this proposal, we will develop, evaluate, and make public new analytic tools that take advantage of many-core computing environments, and will make publicly available all of the genetic and epigenetic data generated from the brain samples.