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 高达 40% 的阿尔茨海默氏病 (AD) 患者存在抑郁症状，并且持续存在 关于它们是否代表 AD 等慢性疾病的危险因素或前驱体征的争论。 抑郁症会影响压力反应，并可能加速生物衰老，进一步导致易感性 然而，精神特征和 AD 的遗传因素已被证实。 大多是分开学习的。 该提案旨在识别与共享遗传相关的编码和非编码调控变异 多个队列中抑郁症和 AD 的风险：英国生物银行、百万退伍军人计划、阿尔茨海默病 测序项目、退伍军人国家健康和复原力研究以及耶鲁-宾夕法尼亚大学研究，并重复 PsycheMERGE 的研究结果，累计研究了超过 100 万人，我们将评估两个主要风险。 AD 的因素 - APOE-ε4 携带者状态（模型 1）和患有抑郁症的 AD 父母史（模型 2）。 先前对 120 万人进行抑郁症基因调控表达研究的结果 基于海马体的数量表达性状位点 (QTL) 鉴定了几个在 AD 中发挥作用的基因 病理学（例如 PARK2、NEGR1、HSPA1A、ITPR3、NLGN1 和 DRD2）。 将抑郁症与 AD 表型分层将揭示抑郁症之间重叠的遗传贡献 和 AD 并阐明共同的分子机制和潜在的治疗靶点。 为了检验这些假设，本提案旨在开发一个多模式框架来研究 通过研究 Aim-1) 与相关编码区域的整个外显子组图谱来研究神经精神合并症 抑郁症和 AD 遗传风险（K99 期），Aim-2）转录组图谱以确定共享分子 通过将大规模 GWAS 与基于脑组织的分子 QTL 研究相结合，为抑郁症和 AD 风险奠定基础 （R00 相）和 Aim-3）表观基因组图谱，以确定与组合多基因相关的甲基化位点 抑郁症和 AD 的评分，并比较抑郁症和 AD 合并症之间的生物衰老，以及 单独紊乱（R00 期）。 随附的培训包括 i) 多个高通量数据分析中的教学课程 技术，ii) 从大规模数据集开发的生物标志物，iii) 计算编程和 iv) 老年学研究将包括写作研讨会、建筑指导。 投资组合，作为独立研究员建立实验室的培训机会。 由五位专家组成的顾问团队开发，他们是AD、精神病学、老龄化、大规模 基因组学和电子健康记录队列共同为拟议的研究提供指导。 并支持候选人的多学科神经精神病学研究生涯。