Integrated target discovery in Alzheimer's disease

阿尔茨海默病的综合靶标发现

• 批准号: 9285327
• 负责人: Christopher David Brown
• 金额: $ 294.58万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9285327
• 关键词: ATAC-seq; Address; Affect; Age; Alleles; Alzheimer' s Disease; Alzheimer' s disease risk; American; Basic Science; Biological Assay; Brain; Brain region; Chromatin; Code; Collection; Complex; Data; Dimensions; Discipline; Disease; Environment; Epigenetic Process; Etiology; Future; Gene Expression; Gene Targeting; Genes; Genetic; Genome engineering; Genotype-Tissue Expression Project; Haplotypes; Hereditary Disease; Heritability; Link; Linkage Disequilibrium; Maps; Molecular; Molecular Conformation; Neurodegenerative Disorders; Neurons; Outcome; Pathway interactions; Pharmaceutical Preparations; Population; Prevalence; Prevention; Proteins; Protocols documentation; Quantitative Trait Loci; Randomized; Regulatory Element; Reporter; Risk; Sampling; Statistical Methods; Statistical Models; Tissues; Translating; Untranslated RNA; Update; Validation; Variant; analytical tool; base; brain tissue; disorder risk; experimental study; follow-up; genetic association; genetic variant; genome wide association study; improved; risk variant; therapeutic development; trait; translational impact

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.

项目摘要 阿尔茨海默病（AD）是一种高度遗传的进行性神经退行性疾病。然而，基因 AD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 基本上无法治愈现有的治疗AD的药物效果甚微。AD风险呈指数级增加， 65 ~ 69岁患病率为3.5%，85 ~ 89岁患病率为30.40%。一个具体的承诺 全基因组关联研究（GWAS）时代的一个重要特征是， 疾病预测、预防和治疗开发，但我们还没有看到这一承诺得到实现。而 GWAS已经快速且可重复地鉴定出与复杂疾病相关的艾德基因位点，如AD、危重病、 局限性限制了它们的翻译影响。绝大多数已确定的艾德疾病风险位点不在蛋白质- 编码基因，而是位于顺式调节元件（克雷斯）内，其中许多是组织特异性的。这 表明大多数复杂的疾病风险变异都会改变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， 仅从非编码GWAS关联中识别相关基因和途径通常是困难的。 此外，候选区域的后续实验表征已被证明是昂贵且费力的。在 在这项提案中，我们将通过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，我们将识别AD相关 克雷斯及其靶基因跨越十个大脑区域。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，以鉴定其表达水平因果影响的基因 AD风险。最后，在目标3中，我们建议结合以下方面来验证目标1和目标2中的预测： 高通量报告基因测定和靶向基因组工程。在整个提案中，我们将开发， 评估并公布利用众核计算环境的新分析工具， 将公开从大脑样本中产生的所有遗传和表观遗传数据。