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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：
10574498
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/10574498
关键词：
ATAC-seq Alzheimer disease detection 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 Molecular Nerve Neurodegenerative Disorders Pathway interactions Phenotype Process Publications Quantitative Trait Loci Reporter Research Resolution Resources Risk Role Senile Plaques Signal Transduction Site Source Structure Talents Techniques Testing Therapeutic Intervention Time Tissues Validation Variant Work biobank brain cell brain tissue 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.

摘要