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

总结 阿尔茨海默氏病（AD）影响全球超过4400万人，并且该数字预计将增加两倍 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艾德 与AD相关的可改变的生活方式相关风险因素;如果这些风险因素确实是AD的因果关系，但不仅仅是 因此，在干预措施中，可以将其作为降低AD发病率的目标。为了缓解挑战， 面对可能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， AD和其他特征。具体来说，首先，超越现有的双样本孟德尔随机化（2SMR），我们 我将开发以下新方法，这些方法更强大，更稳健，建模不那么严格 假设：在存在混杂和无效工具的情况下进行全转录组关联研究 变量，多个性状的因果遗传变异的共定位检测，以及确定因果方向 使用多个（可能相关的）遗传变异作为工具变量的两个性状之间。二是 适应和应用新的和现有的方法，以多个大规模的GWAS数据集与AD和其他 分子/成像/临床特征，以全面搜索和识别AD靶基因， 区域及其功能连接，以及其他与AD有因果关系的危险因素。作为副产品， 我们将开发和分发实现所提出的方法的软件。

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.