Multi-modal intersection of depression and genetic liability to Alzheimers disease

抑郁症与阿尔茨海默病遗传易感性的多模式交叉

• 批准号: 10697330
• 负责人: Gita A Pathak
• 金额: $ 9.99万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10697330
• 关键词: Acceleration; Advisory Committees; Affect; Aging; Alleles; Alzheimer' s Disease; Alzheimer' s disease pathology; Alzheimer' s disease risk; Architecture; Biological Aging; Biological Markers; Brain; Brain imaging; Chronic; Code; DRD2 gene; Data; Data Analyses; Dementia; Development; Diagnosis; Disease; Drug Targeting; Educational workshop; Electronic Health Record; Family history of; Genes; Genetic; Genetic Risk; Genetic Variation; Genomics; Genotype; Gerontology; Health; Healthcare; Heritability; Hippocampus; Impaired cognition; Individual; Laboratories; Measures; Mediating; Mental Depression; Mental Health; Mentors; Messenger RNA; Methylation; Modeling; Molecular; Neurodegenerative Disorders; Outcome; PARK2 gene; Participant; Pathogenesis; Persons; Phase; Phenotype; Population; Predisposition; Prefrontal Cortex; Process; Proteome; Proxy; Psychiatry; Psychopathology; Quantitative Trait Loci; Recording of previous events; Regulatory Pathway; Research; Research Personnel; Risk; Risk Factors; Role; SNP array; Signal Transduction; Site; Stratification; Testing; Training; United States; Untranslated RNA; Variant; Veterans; Writing; age related; apolipoprotein E-4; biobank; biological adaptation to stress; brain tissue; brain volume; care burden; career; carrier status; cohort; comorbid depression; comorbidity; depressive symptoms; disease phenotype; drug repurposing; epigenome; epigenome-wide association studies; epigenomic profiling; epigenomics; exome; genetic information; genetic variant; genome wide association study; gray matter; high throughput technology; interest; large scale data; member; model design; multidisciplinary; multimodality; neuropsychiatry; novel; phenome; programs; protein expression; protein function; psychiatric comorbidity; psychogenetics; resilience; risk variant; therapeutic target; training opportunity; trait; transcriptome; transcriptomic profiling; transcriptomics

ABSTRACT Depressive symptoms are present in up to 40% of individuals with Alzheimer’s disease (AD) and an ongoing debate regarding whether they represent a risk factor or a prodromal sign of AD. Chronic conditions such as depression impact the stress response and may accelerate biological aging further contributing to susceptibility to age-related conditions specifically cognitive decline. However, genetics of psychiatric traits and AD have been mostly studied separately. This proposal aims to identify coding and non-coding regulatory variants associated with shared genetic risk for depression and AD in multiple cohorts: UK biobank, Million Veteran Program, Alzheimer’s Disease Sequencing Project, National Health and Resilience in Veterans Study, and Yale-Penn study, and replicate findings in PsycheMERGE, cumulatively studying more than 1 million individuals. We will assess two major risk factors of AD - APOE-ε4 carrier status (Model-1) and parental history of AD (Model-2) with depression. Our previous findings from the genetically regulated expression study of depression in 1.2 million individuals using hippocampus-based expression quantitative trait loci (QTL) identified several genes which have roles in AD pathology (e.g. PARK2, NEGR1, HSPA1A, ITPR3, NLGN1, and DRD2). Therefore, we hypothesize that stratifying depression with AD phenotypes will uncover overlapping genetic contributions between depression and AD and elucidate the shared molecular mechanisms, and potential therapeutic targets. To test theses hypotheses, this proposal aims to develop a multi-modal framework to study neuropsychiatric comorbidities by investigating, Aim-1) whole exome profiles for coding regions associated with depression and genetic risk for AD (K99 phase), Aim-2) transcriptomic profiles to identify a shared molecular basis for depression and AD risk by integrating large-scale GWAS with brain tissue-based molecular QTL studies (R00 phase), and Aim-3) epigenome profiles to identify methylation sites associated with a combined polygenic score of depression and AD, and compare biological aging between depression and AD comorbidity, and either disorder alone (R00 phase). The accompanying training includes didactic courses in i) data analysis from multiple high throughput technologies, ii) developing biomarkers from large-scale datasets, iii) computational programming and iv) gerontological studies. The professional development training will include writing workshops, building mentoring portfolio, training opportunities to establish laboratory as an independent researcher. This training plan was developed under advisory team comprised of five members who are experts in AD, psychiatry, aging, large-scale genomics, and cohorts with electronic health records. Together they provide guidance on the proposed study and support a multidisciplinary neuropsychiatric research career for the candidate.

抽象的 多达40％的阿尔茨海默氏病（AD）的患者中有抑郁症状存在 关于它们是代表AD的危险因素还是前瞻性迹象的辩论。慢性条件，例如 抑郁会影响压力反应，并可能加速生物衰老，进一步导致易感性 与年龄有关的条件特别认知下降。但是，精神病和广告的遗传学一直是 主要研究。 该建议旨在确定与共享遗传相关的编码和非编码调节变体 多个队列中的抑郁症和广告风险：英国生物库，百万退伍军人计划，阿尔茨海默氏病 在退伍军人研究中的测序项目，国家健康和韧性以及耶鲁 - 佩恩研究，并复制 Psychemerge的发现，累计研究了超过100万人。我们将评估两个主要风险 AD-APOE-ε4载体状态（Model-1）和抑郁症的AD（模型2）的父母历史的因素。我们的 从一般监管的表达研究研究中的抑郁症的先前发现 基于海马的表达定量性状基因座（QTL）鉴定了几个在AD中具有作用的基因 病理学（例如PARK2，NEGR1，HSPA1A，ITPR3，NLGN1和DRD2）。因此，我们假设 用AD表型对抑郁进行分层会发现抑郁症之间的重叠遗传贡献 AD并阐明了共享的分子机制以及潜在的治疗靶标。 为了检验这些假设，该提案旨在开发一个多模式框架来研究 通过调查，AIM-1）与与之相关的编码区域的整体外部概况，神经精神上的合并症 AD的抑郁和遗传风险（K99阶段），AIM-2）转录组曲线以识别共享分子 通过将大规模GWA与脑组织基于脑组织的分子QTL研究整合到抑郁症和AD风险的基础 （R00相）和AIM-3）表观基因组轮廓，以识别与组合多基因相关的甲基化位点 抑郁和AD的评分，并比较抑郁症和合并症之间的生物衰老，并且要么 单独的疾病（R00期）。 参与培训包括i）来自多个高吞吐量的数据分析 技术，ii）从大规模数据集开发生物标志物，iii）计算编程和IV） 老年学研究。专业发展培训将包括写作研讨会，建设指导 投资组合，培训机会，以建立实验室为独立研究人员。这个培训计划是 在咨询团队下开发的五名成员是广告，精神病学，衰老，大规模的专家 基因组学和与电子健康记录的队列。他们一起为拟议的研究提供指导 并支持候选人的多学科神经精神研究职业。