Discovering causal genes, brain regions and other risk factors for Alzheimer'a disease

发现阿尔茨海默病的致病基因、大脑区域和其他危险因素

• 批准号: 10561609
• 负责人: Wei Pan
• 金额: $ 62.26万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2025-02-21
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10561609
• 关键词: Affect; Alzheimer' s Disease; Alzheimer' s disease related dementia; Alzheimer' s disease risk; Area; Biological; Brain; Brain imaging; Brain region; Clinical; Cohort Studies; Communities; Complex; Computer software; Data; Data Analyses; Data Set; Detection; Disease; Documentation; Early Diagnosis; Environment; Etiology; Gene Expression; Genes; Genetic; Goals; Image; Incidence; Individual; Intervention; Joints; Lasso; Least-Squares Analysis; Life Style; Linkage Disequilibrium; Maps; Mediating; Mendelian randomization; Methods; Modeling; Molecular; Observational Study; Observational epidemiology; Outcome; Phenotype; Prevention; Public Domains; Relaxation; Research; Risk; Risk Factors; Role; Sampling; Statistical Computing; Statistical Methods; Testing; Tissue-Specific Gene Expression; Tissues; Writing; biobank; causal variant; computerized tools; epidemiology study; flexibility; genetic variant; genome wide association study; insight; multiple datasets; novel; pleiotropism; programs; protective factors; response; software development; statistics; theories; therapeutic development; trait; transcriptome; web site

Summary Alzheimer's disease (AD) affects over 44 million individuals worldwide, and the number is projected to triple by 2050. However, currently there is no cure for AD. Observational epidemiology studies have identified some modifiable lifestyle-related risk factors associated with AD; if these risk factors are indeed causal to, but not just effects of, AD, they can be targeted in interventions to reduce the incidence of AD. To alleviate the challenges facing observational studies with likely confounding and reverse causation, we develop and apply a suite of novel, robust and powerful causal inference methods by integrating the large amount of existing large-scale GWAS of AD and other traits. Specifically, first, going beyond existing two-sample Mendelian randomization (2SMR), we will develop the following new methods that are more powerful and more robust with less stringent modeling assumptions: transcriptome-wide association studies in the presence of confounding and invalid instrumental variables, co-localization detection of causal genetic variants for multiple traits, and orienting the causal direction between two traits using multiple (possibly correlated) genetic variants as instrumental variables. Second, we will adapt and apply both the new and existing methods to multiple large-scale GWAS datasets with AD and other molecular/imaging/clinical traits to comprehensively search and identify not only AD target genes, but also brain areas and their functional connectivities, and other risk factors, that are putatively causal to AD. As a byproduct, we will develop and distribute software implementing the proposed methods.

概括 阿尔茨海默病 (AD) 影响全球超过 4400 万人，预计这一数字将增加两倍 到 2050 年。然而，目前还没有治愈 AD 的方法。观察性流行病学研究已经确定了一些 与 AD 相关的可改变的生活方式相关危险因素；如果这些风险因素确实是（但不仅仅是）的原因 AD 的影响，因此可以有针对性地采取干预措施，以减少 AD 的发病率。为了缓解挑战 面对可能存在混杂和反向因果关系的观察性研究，我们开发并应用了一套新颖的、 通过整合大量现有的大规模 GWAS 来构建鲁棒且强大的因果推理方法 AD等特质。具体来说，首先，超越现有的双样本孟德尔随机化（2SMR），我们 将开发以下新方法，这些方法更强大，更鲁棒，建模不太严格 假设：在存在混杂和无效仪器的情况下进行全转录组关联研究 变量，对多个性状的因果遗传变异进行共定位检测，并确定因果方向 使用多个（可能相关的）遗传变异作为工具变量来分析两个性状之间的关系。其次，我们将 将新方法和现有方法适应并应用到具有 AD 和其他功能的多个大规模 GWAS 数据集 分子/影像/临床特征，全面检索和鉴定AD靶基因以及大脑 区域及其功能连接以及其他风险因素，这些因素被认为是导致AD的原因。作为副产品， 我们将开发和分发实施所提议方法的软件。