Mapping Molecular and Phenotypic Interactions in Alzheimers Disease

绘制阿尔茨海默病的分子和表型相互作用图谱

• 批准号: 10347286
• 负责人: Stephen Montgomery
• 金额: $ 73.58万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10347286
• 关键词: ATAC-seq; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Alzheimer' s disease pathology; Alzheimer' s disease risk; Amyloid beta-Protein; Behavior; Biological Assay; Biological Factors; Biological Markers; Biological Process; Brain; Brain imaging; Cardiovascular Diseases; Cell Line; Chromatin; Chromosome Mapping; Cognitive deficits; Data; Data Set; Databases; Dementia; Development; Diabetes Mellitus; Disease; Elderly; Frequencies; Gene Expression Regulation; Gene Frequency; Genes; Genetic; Genotype-Tissue Expression Project; Goals; Guidelines; Immunity; Impaired cognition; Individual; Link; Maps; Medical; Mendelian randomization; Metabolic Pathway; Methods; Nerve; Neurodegenerative Disorders; Pathway interactions; Phenotype; Process; Publications; Quantitative Trait Loci; Reporter; Research; Resolution; Resources; Risk; Role; Senile Plaques; Signal Transduction; Site; Source; Structure; Sum; Talents; Techniques; Testing; Therapeutic Intervention; Time; Tissues; Validation; Variant; Work; base; biobank; brain cell; brain tissue; case control; cell type; cognitive function; computerized tools; deep sequencing; experience; functional genomics; genetic variant; genome analysis; genome wide association study; genome-wide; genomic data; improved; large datasets; lipid metabolism; molecular phenotype; mortality; next generation; novel; phenotypic data; programs; public health relevance; rare variant; sleep pattern; trait; transcriptome; transcriptome sequencing; working group

Abstract Alzheimer’s Disease (AD) is a leading cause of cognitive decline and mortality in the elderly. Treatment options for AD are limited, and there is a huge need for better medical options. AD is associated with progressive expansion of beta amyloid plaques in the brain, leading over time to loss of brain tissue and cognitive function. However, at present, understanding of the underlying drivers of AD is incomplete. In this project, we will use a combination of large data sets of genetic and phenotypic data, as well as functional genomics, to further elucidate the biological processes, pathways, and cell types leading to AD. Specifically, we will use existing functional genomics data, as well as newly generated Massively Parallel Reporter Assays and advanced colocalization and outlier-based statistical approaches to identify functional regulatory variants at disease-relevant loci; these will be used to increase the power to detect AD-associated variants, particularly for low-frequency sites. Furthermore, we will focus on linking intermediate biomarkers such as metabolites and brain imaging data, and traits such as diabetes, cardiovascular disease and sleep patterns to AD risk using Mendelian Randomization and clustering techniques. We will further aim to partition the GWAS signal into discrete biological factors, both by pathways/processes and by tissue. In sum, this work will lead to deeper understanding of the causal genetic drivers of Alzheimer’s Disease.

摘要 阿尔茨海默病（AD）是老年人认知能力下降和死亡的主要原因。治疗 AD的选择是有限的，并且存在对更好的医疗选择的巨大需求。AD与 大脑中β淀粉样蛋白斑块的进行性扩张，随着时间的推移导致脑组织的损失， 认知功能然而，目前，对AD的潜在驱动因素的理解是不完整的。在这 项目，我们将使用遗传和表型数据的大数据集的组合，以及功能 基因组学，以进一步阐明导致AD的生物学过程、途径和细胞类型。具体地说， 我们将利用现有的功能基因组学数据，以及新产生的大规模平行报告分析， 以及先进的共定位和基于离群值的统计方法来识别功能调节变体 在疾病相关位点;这些将用于增加检测AD相关变异的能力，特别是 低频网站此外，我们将重点关注中间生物标志物，如代谢物， 和大脑成像数据，以及糖尿病、心血管疾病和睡眠模式等特征与AD风险的关系。 孟德尔随机化和聚类技术。我们将进一步致力于将GWAS信号划分为 离散的生物学因素，通过途径/过程和组织。总之，这项工作将导致更深层次的 了解阿尔茨海默病的因果遗传驱动因素。