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

摘要 高达40%的阿尔茨海默病(AD)患者存在抑郁症状，并且正在进行的 关于它们是否代表AD的危险因素或前驱征兆的争论。慢性疾病，如 抑郁影响应激反应，并可能加速生物衰老，进一步加剧易感性。 与年龄相关的疾病，特别是认知能力下降。然而，精神特征的遗传学和阿尔茨海默病 大部分是单独研究的。 这项提案旨在识别与共享基因相关的编码和非编码调控变体 多个队列中患抑郁症和AD的风险：英国生物库、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，耶鲁-宾夕法尼亚大学研究，并复制 发表在《精神病学》杂志上的研究结果，累计研究了100多万人。我们将评估两个主要风险 AD-ApoE-ε4携带者状态(模型-1)和AD父母病史(模型-2)合并抑郁的因素我们的 在120万人中进行的抑郁症基因调控表达研究的先前发现 基于海马区表达的数量性状基因座(QTL)发现了几个在AD发病中起作用的基因 病理(如PARK2、NEGR1、HSPA1A、ITPR3、NLGN1和DRD2)。因此，我们假设 将抑郁症与AD表型进行分层将揭示抑郁症之间重叠的基因贡献 并阐明其共同的分子机制和潜在的治疗靶点。 为了检验假设，这项建议旨在开发一个多模式框架来研究 通过调查与以下相关的编码区的整个外显子组谱，来研究神经精神疾病的并存 抑郁症与阿尔茨海默病的遗传风险(K99期)，AIM-2)转录图谱以确定共同的分子 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期)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共病之间的生物老化，以及 单纯性紊乱(R00期)。 随附的培训包括授课课程：i)从多个高吞吐量进行数据分析 技术，二)从大规模数据集开发生物标记物，三)计算编程和四) 老年学研究。职业发展培训将包括写作研讨会、建筑指导 投资组合，培训机会，建立实验室作为独立的研究人员。这项培训计划是 由五名AD、精神病学、老龄化、大规模的专家组成的顾问团队 基因组学，以及拥有电子健康记录的队列。它们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。

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.