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: 10594423
• 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/10594423
• 关键词: Accounting; Acute; Address; Animal Model; Antipsychotic Agents; Behavior; Behavioral; Bioinformatics; Biological; Biology; Cannabis; Cannabis policy; Child; Childhood; Chronic; Clinical; Data; Data Analyses; Data Set; Development; Disease; Educational Status; Epigenetic Process; Etiology; Exposure to; Foundations; Funding; Future; Gene Expression; General Population; Genes; Genetic; Genetic Predisposition to Disease; Goals; Heritability; Incidence; Individual; Investigation; Leadership; Maps; Mediating; Mental Depression; Mental disorders; Mentored Research Scientist Development Award; Mentorship; Motivation; Multiomic Data; Neurobiology; Outcome; Pathway interactions; Patients; Persons; Pharmaceutical Preparations; Population; Positioning Attribute; Predisposition; Prefrontal Cortex; Psychoses; Psychotic Disorders; Public Health; REM Sleep; Recording of previous events; Recreation; Relapse; Reporting; Research; Research Personnel; Risk; Rodent; Rodent Model; Role; Sampling; Schizophrenia; Source; Subgroup; Suicide; Testing; Tetrahydrocannabinol; Therapeutic; Time; Tissue Model; Tissues; Training; US State; United States; United States National Institutes of Health; Variant; Vulnerable Populations; Work; actionable mutation; addiction; adverse outcome; 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; 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; skill acquisition; 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 在美国，即使在脆弱之内，娱乐性大麻的使用变得越来越普遍 人群。在围绕慢性大麻使用可能不利后果的日益严重的问题中， 有证据表明大麻使用障碍（CUD）在遗传上与几种易感性相关 行为（例如，较低的教育成就）和精神病学（例如精神分裂症）的结果，从而带来 提出事先因果主张。最激进的讨论围绕着大麻的角色 在精神分裂症（SCZ）和精神病疾病的病因中使用和cud。虽然现在有很多 支持共同遗传影响的证据，研究还概述了尤其 四氢大麻醇（THC）的高效力形式。对多效变体的系统搜索，该变体的经历 CUD和SCZ之间的合并症不仅可以提供有关共享生物学的见解，还可以概述 识别具有最大风险的个体亚组的途径。这位指导的研究科学家发展 奖项（K01）提出了一项研究计划，该计划利用当前最大的全基因组可用的研究计划 协会研究（GWAS）数据集至（a）进行CUD的跨disorder GWA与SCZ进行对比 从与SCZ一起使用大麻使用的类似跨disorder分析的发现，以识别收敛的基因座和 发散效果； （b）使用遗传信息的方法测试因果关系 `大麻暴露和SCZ的人类和啮齿动物模型的OMICS数据 进一步优先考虑因果变异的生物学合理性； （c）利用从 这些跨疾病分析以确定与第一集精神病，大麻诱导的精神病的关联， 和独立样本中的儿童精神病主持。这些研究目的建立在四个钥匙上 培训目标将增强申请人的职业目标，即成为NIH资助的独立 在成瘾和精神病界面工作的研究者。这些培训目标包括（a） 对急性和长期暴露大麻的临床作用有深刻的了解，（b）综合 GWAS后注释的生物信息学方法，包括跨物种数据（c）对 神经生物学是大麻和SCZ之间合并症的基础，以及（d）职业发展 领导和指导职位。申请人建立在她目前的资金和培训的基础上 高级统计遗传学通过增加与多种相关的新颖方面来解决合并症 OMICS数据集成，更具体地针对大麻和大麻之间的独特但无处不在的合并症 SCZ。该培训和研究计划将共同对共享的遗传产生一些最初的见解 病因和SCZ的病因，并为实现功能针对的未来研究提供了机会， 产生治疗替代品的最终目标，可以部分减轻严重的个人成本 SCZ患者的慢性大麻使用。