Genomic determinants of sleep traits as risk and protective factors for Alzheimer's disease

睡眠特征的基因组决定因素作为阿尔茨海默病的风险和保护因素

• 批准号: 10453007
• 负责人: Ignazio Stefano Piras
• 金额: $ 19.2万
• 依托单位: TRANSLATIONAL GENOMICS RESEARCH INST
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10453007
• 关键词: Abeta synthesis; Algorithms; Alzheimer' s Disease; Alzheimer' s disease brain; Alzheimer' s disease risk; Amyloid; Bioinformatics; Brain; Brain region; Candidate Disease Gene; Cerebrospinal Fluid; Clinical; Complex; Computer software; Data; Data Analyses; Data Set; Detection; Development; Disease; Disease Outcome; Disease Progression; Etiology; Future; Gene Expression; Genes; Genetic; Genetic Diseases; Genetic Transcription; Genomics; Habits; Heterogeneity; Impaired cognition; Intervention; Leg; Life Style; Light; Linkage Disequilibrium; Mendelian randomization; Methods; Molecular; Movement Disorders; Neurodegenerative Disorders; Neurofibrillary Tangles; Observational Study; Obstructive Sleep Apnea; Onset of illness; Outcome; Participant; Periodicity; Pharmaceutical Preparations; Phenotype; Production; RNA; Risk; Risk Factors; Sampling; Senile Plaques; Sleep; Sleep Disorders; Sleep disturbances; Sleeplessness; Synapses; Testing; Time; United States; Variant; amyloid formation; analytical method; base; biobank; causal variant; cohort; design; disorder risk; effective therapy; epidemiology study; genetic association; genetic variant; genome wide association study; genome-wide; genomic data; improved; innovation; instrument; machine learning algorithm; machine learning method; modifiable risk; prevent; protective factors; statistics; symptomatic improvement; tau Proteins; trait; transcriptome; transcriptomics

PROJECT SUMMARY Alzheimer's disease (AD) is the most prevalent neurodegenerative disease in the United States and there are no effective treatments or cure. The detection of modifiable protective or risk factors can improve the possibility of intervention through life-style habits focused to reduce the disease risk or elevate disease protection. Sleep disorders and disturbances have recently been recognized as risk factors for AD according to evidence from epidemiological studies as well as associations with specific AD neuropathological hallmarks such as plaques and tangles in the brain. However, the causal relationship between sleep disorders and disturbances and AD has not been well established. In this secondary data analysis proposal, we aim to study the causal effects of sleep traits on AD using large publicly available genomics datasets including the UK Biobank (UKB), the AD Genetic Consortium (ADGC), and others. We will use a bioinformatics workflow consisting of innovative analytical methods designed to shed light on the causal relationship and identify specific genomics factors involved. The project will be carried out as follows: 1) We will leverage large-scale genome-wide association studies (GWAS) conducted on sleep traits to prioritize genes using a method (transcriptome-wide association study - TWAS) capable of detecting phenotype-associated genes under genetic control and simultaneously related to changes in gene expression. Then, AD RNA profiling studies will be analyzed using pseudotime algorithms, extracting latent temporal information and ordering the samples according to disease progression. Genes identified in this step (showing a high correlation with the disease progression and previously detected in the TWAS) will be further investigated by Mendelian randomization to assess the causal relationship between sleep traits (exposure) and AD (outcome). 2) A second independent analysis will be conducted by Mendelian randomization, prioritizing variants by statistical significance from the large scale GWAS conducted on sleep traits and assessing the causal relationship with AD. Additionally, a recently developed algorithm (latent causal variable method) will be applied as well to detect causal relationships between sleep traits and AD. This analytical workflow and the large size of the cohorts included will provide us with the statistical power to identify modifiable risk and protective factors to demonstrate a causal relationship with AD.

项目摘要 阿尔茨海默氏病（AD）是美国最普遍的神经退行性疾病 没有有效的治疗或治愈。检测可修改的保护或风险因素可以改善可能性 通过生活方式的习惯进行干预，以降低疾病风险或提高疾病保护。睡觉 根据来自 流行病学研究以及与特定AD神经病理学标志（如斑块）的关联 和大脑缠结。但是，睡眠障碍与干扰与广告之间的因果关系 尚未确定。 在此二次数据分析建议中，我们旨在研究睡眠特征对AD的因果影响 大型公共基因组学数据集，包括英国生物库（UKB），AD遗传财团 （ADGC）等。我们将使用由创新分析方法组成的生物信息学工作流程 旨在阐明因果关系并确定涉及的特定基因组因素。项目 将进行如下： 1）我们将利用对睡眠特征进行的大规模基因组关联研究（GWAS） 使用一种能够检测的方法（TWAS）优先考虑基因（TWAS） 在遗传控制下与表型相关的基因，并与基因的变化同时相关 表达。然后，将使用假频率算法分析AD RNA分析研究，从而提取潜在 时间信息并根据疾病进展订购样品。在此确定的基因 步骤（显示与疾病进展的高相关性，并在TWA中检测到） 通过孟德尔随机化进一步研究以评估睡眠特征之间的因果关系 （暴露）和AD（结果）。 2）第二次独立分析将通过孟德尔随机化进行，将变体优先考虑 在睡眠特征上进行的大规模GWA的统计显着性并评估因果关系 与AD的关系。此外，最近开发的算法（潜在因果变量法）将是 也用于检测睡眠特征与AD之间的因果关系。 这种分析工作流程和包括的同类人群的大尺寸将为我们提供统计能力 确定可修改的风险和保护因素，以证明与AD的因果关系。