Identifying genetic sources of comorbidity between cannabis and schizophrenia using genome-wide and integrative omics data

使用全基因组和综合组学数据识别大麻和精神分裂症之间共病的遗传来源

• 批准号: 10364741
• 负责人: Emma Covey Johnson
• 金额: $ 15.73万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10364741
• 关键词: Accounting; Acute; Address; Animal Model; Antipsychotic Agents; Behavior; Behavioral; Bioinformatics; Biological; Biology; Cannabis; Child; Childhood; Chronic; Clinical; Data; Data Analyses; Data Set; Depression and Suicide; Development; Disease; Educational Status; Epigenetic Process; Etiology; Exposure to; Foundations; Funding; Future; Gene Expression; General Population; Genes; Genetic; Genetic Predisposition to Disease; Geographic state; Goals; Heritability; Incidence; Individual; Investigation; Leadership; Maps; Mediating; Mental disorders; Mentored Research Scientist Development Award; Mentorship; Motivation; Multiomic Data; Neurobiology; Outcome; Pathway interactions; Patients; Persons; Policies; Positioning Attribute; Predisposition; Prefrontal Cortex; Psychoses; Psychotic Disorders; Public Health; REM Sleep; Recording of previous events; Relapse; Reporting; Research; Research Personnel; Risk; Rodent; Rodent Model; Role; Sampling; Schizophrenia; Source; Subgroup; Testing; Tetrahydrocannabinol; Therapeutic; Time; Tissue Model; Tissues; Training; United States; United States National Institutes of Health; Variant; Vulnerable Populations; Work; actionable mutation; addiction; adverse outcome; behavior test; career; career development; causal variant; clinical effect; clinical translation; comorbidity; cost; data integration; design; experimental study; first episode psychosis; follow-up; genetic architecture; genetic variant; genome wide association study; genome-wide; high risk; high risk population; human data; human model; human tissue; insight; longitudinal course; marijuana legalization; marijuana use; marijuana use disorder; marijuana user; multiple omics; novel; pleiotropism; polygenic risk score; population based; psychiatric comorbidity; psychosocial; skills; substance use treatment; transcriptomics

Project Summary Recreational cannabis use is becoming increasingly common in the United States, even within vulnerable populations. Amidst growing concerns surrounding the possible adverse consequences of chronic cannabis use, there is evidence that cannabis use disorder (CUD) is genetically correlated with susceptibility to several behavioral (e.g., lower educational achievement) and psychiatric (e.g., schizophrenia) outcomes, thus bringing into question prior causal claims. The most aggressively contested discussion surrounds the role of cannabis use and CUD in the etiology of schizophrenia (SCZ) and psychotic illness. While there is now an abundance of evidence supporting shared genetic influences, studies also outline the psychotomimetic effects of especially high potency forms of tetrahydrocannabinol (THC). A systematic search for pleiotropic variants that undergird this comorbidity between CUD and SCZ can not only provide insights into shared biology, but also outline avenues for identifying subgroups of individuals at greatest risk. This Mentored Research Scientist Development Award (K01) proposes a research plan that leverages some of the largest currently available genome-wide association study (GWAS) datasets to (a) conduct a cross-disorder GWAS of CUD with SCZ, and to contrast it with findings from a similar cross-disorder analysis of cannabis use with SCZ, to identify loci of convergent and divergent effect; (b) to test for a causal relationship using a genetically-informed approach and harness curated `omics data from human and rodent models of cannabis exposure and SCZ, to fine-map significant loci and further prioritize causal variants for biological plausibility; and (c) to utilize polygenic risk scores derived from these cross-disorder analyses to identify associations with first-episode psychosis, cannabis-induced psychosis, and childhood psychosis-proneness in independent samples. These research aims are founded on four key training objectives that will enhance the applicant's career goal of becoming an NIH-funded independent investigator who works at the interface of addictions and psychiatric illness. These training objectives include (a) a deep understanding of the clinical effects of acute and chronic exposure to cannabis, (b) integrative bioinformatics approaches for post-GWAS annotation, including cross-species data (c) an appreciation of the neurobiology underlying the comorbidity between cannabis and SCZ, and (d) career development towards leadership and mentorship positions. The applicant builds upon her current funding and training directed at advanced statistical genetics to addressing comorbidity by adding on novel facets relating more broadly to multi- omics data integration and more specifically to the unique yet ubiquitous comorbidity between cannabis and SCZ. Together, this training and research plan will produce some of the first insights into the shared genetic etiology underlying CUD and SCZ and provide opportunities for functionally targeted future studies, with the ultimate objective of producing therapeutic alternatives that can partially mitigate the serious personal costs of chronic cannabis use in SCZ patients.

项目摘要 娱乐性大麻的使用在美国变得越来越普遍，即使在脆弱的人群中也是如此 人口。在人们对长期使用大麻可能产生的不良后果日益担忧的情况下， 有证据表明，大麻使用障碍(Cud)与多种疾病的易感性有遗传关联。 行为(例如，较低的教育成就)和精神(例如，精神分裂症)结果，从而带来 质疑先前的因果关系主张。最具争议性的讨论围绕大麻的作用 在精神分裂症(SCZ)和精神病病因学中的应用和CUD。虽然现在有大量的 支持共同遗传影响的证据，研究还概述了特别是 高效形式的四氢大麻酚(THC)。系统地寻找支持的多效性变体 CUD和SCZ之间的这种共病不仅可以提供对共享生物学的洞察，而且还可以概述 确定处于最大风险的个人亚群的途径。这是研究科学家发展的指导者 AWARD(K01)提出了一项研究计划，该计划利用了目前全基因组范围内最大的一些可用的研究计划 关联研究(GWAS)数据集：(A)进行CUD和SCZ的交叉无序GWAS，并将其进行对比 利用与SCZ类似的大麻使用交叉紊乱分析的结果，确定汇聚和 不同的影响；(B)使用遗传知情的方法测试因果关系，并加以管理 来自人类和啮齿动物大麻暴露和SCZ模型的组学数据，以精细绘制重要基因座和 进一步优先考虑生物可信性的因果变异；以及(C)利用从 这些交叉障碍分析，以确定与首发精神病，大麻诱导的精神病， 和儿童期精神病--独立样本中的倾向。这些研究目标建立在四个关键的基础上 培训目标将增强申请者成为NIH资助的独立人士的职业目标 研究成瘾和精神疾病的调查员。这些培训目标包括：(A) 深入了解急性和长期接触大麻的临床影响，(B)综合 地理信息系统后注释的生物信息学方法，包括跨物种数据(C)评价 大麻和SCZ共病的神经生物学基础，以及(D) 领导力和导师职位。申请者以她目前的资金和培训为基础 先进的统计遗传学通过增加更广泛地与多种疾病相关的新方面来解决共病 组学数据集成，更具体地说，涉及大麻和大麻之间独特但普遍存在的共病 SCZ.总之，这项培训和研究计划将产生对共享基因的一些初步见解 CUD和SCZ的病因学基础，并为未来的功能靶向研究提供机会， 最终目标是生产能够部分缓解严重的个人成本的治疗替代方案 SCZ患者的慢性大麻使用情况。