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，滥用大麻越来越普遍，即使是在弱势群体中也是如此。 人口。在对长期使用大麻可能产生的不良后果日益关切的情况下， 有证据表明，大麻使用障碍（CUD）与对几种药物的易感性存在遗传相关性。 行为（例如，较低的教育成就）和精神病（例如，精神分裂症）的结果，从而带来 质疑先前的因果关系最激烈的争论围绕着大麻的作用 使用和CUD在精神分裂症（SCZ）和精神病的病因。虽然现在有大量的 证据支持共享的遗传影响，研究还概述了精神病的影响，特别是 四氢大麻酚（THC）的高效形式。系统性地寻找支持 CUD和SCZ之间的这种共存不仅可以提供对共享生物学的见解，而且还可以概述 确定高危人群的途径。本指导研究科学家的发展 Award（K 01）提出了一项研究计划，该计划利用了一些目前最大的全基因组 关联研究（GWAS）数据集，以（a）进行CUD与SCZ的交叉疾病GWAS，并将其 从一个类似的交叉障碍分析大麻使用与SCZ的结果，以确定基因座的收敛和 发散效应;（B）使用遗传学方法测试因果关系，并利用 从人类和啮齿动物接触大麻和SCZ的模型中获得“组学数据”，以精细绘制重要的基因座， 进一步优先考虑生物相容性的因果变异;以及（c）利用来自以下的多基因风险评分： 这些交叉障碍分析，以确定与首次发作的精神病，大麻引起的精神病， 和儿童精神病倾向的独立样本。这些研究目标基于四个关键 培训目标，这将提高申请人的职业目标，成为一个NIH资助的独立 在成瘾和精神疾病的界面上工作的调查员。这些培训目标包括：（a） 对急性和慢性接触大麻的临床影响的深刻理解，（B）综合 生物信息学方法后GWAS注释，包括跨物种数据（c）的评价 大麻和SCZ之间共病的神经生物学基础，以及（d）职业发展， 领导和指导职位。申请人在现有资金和培训的基础上， 先进的统计遗传学，以解决并发症，通过增加新的方面，更广泛地涉及多， 组学数据整合，更具体地说，大麻和 SCZ。总之，这项培训和研究计划将产生一些对共享基因的初步见解。 CUD和SCZ的病因学基础，并为未来的功能靶向研究提供机会， 最终目标是生产治疗替代品，可以部分减轻严重的个人成本， SCZ患者中的慢性大麻使用。