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

基本信息

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
项目类别：
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&apos s Disease Alzheimer&apos s disease diagnosis Alzheimer&apos s disease pathology Alzheimer&apos 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

项目摘要

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.

抽象的阿尔茨海默病（AD）是老年人认知能力下降和死亡的主要原因。 AD 的选择有限，并且非常需要更好的与 AD 相关的医疗选择。大脑中β淀粉样蛋白斑块逐渐扩张，随着时间的推移导致脑组织损失然而，目前对 AD 的潜在驱动因素的理解还不完整。项目中，我们将结合使用遗传和表型数据以及功能数据的大型数据集基因组学，进一步阐明导致 AD 的生物过程、途径和细胞类型。我们将使用现有的功能基因组学数据以及新生成的大规模并行报告基因检测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异在疾病相关位点；这些将用于提高检测 AD 相关变异的能力，特别是此外，我们将专注于连接中间生物标志物，例如代谢物。和脑成像数据，以及糖尿病、心血管疾病和睡眠模式等特征对 AD 风险的影响我们的目标是进一步将 GWAS 信号划分为孟德尔随机化和聚类技术。总之，这项工作将导致更深入的研究。了解阿尔茨海默病的遗传驱动因素。