Identifying specific genetic pathway interactions for drug use and abuse through integrative omics

通过综合组学确定药物使用和滥用的特定遗传途径相互作用

• 批准号: 10663216
• 负责人: Qian Peng
• 金额: $ 54.3万
• 依托单位: SCRIPPS RESEARCH INSTITUTE, THE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10663216
• 关键词: Addictive Behavior; Address; Affect; Alcohols; Cohort Studies; Communities; Complement; Computing Methodologies; Data; Disease; Disease model; Drug abuse; Drug usage; Epigenetic Process; Etiology; Genes; Genetic; Genomics; Hereditary Disease; Heritability; Human Genome; Individual; Intervention; Knowledge; Link; Mental Health; Methods; Outcome; Pathway interactions; Play; Population; Prevention; Public Health; Regulation; Research; Risk; Role; Signal Transduction; Substance Use Disorder; System; Tissues; addiction; alcohol comorbidity; alcohol use disorder; clinically actionable; cohort; comorbidity; disorder prevention; epigenomics; genetic architecture; genetic risk factor; genetic variant; genome wide association study; high dimensionality; high risk population; insight; machine learning algorithm; marijuana use; marijuana use disorder; precision medicine; programs; screening; software development; substance use; transcriptomics; whole genome

PROJECT SUMMARY Cannabis use disorders (CUD) are prevalent in the U.S., and highly comorbid with other substance use disorders (SUD) such as alcohol use disorder (AUD), as well as with other mental health problems. While the etiology of cannabis use/misuse have both environmental and genetic components, cannabis use and problematic use are found to be highly heritable. Thus, studies that identify the genetic risk factors for CUD in the general U.S. populations, and in the high-risk populations, are of high public health importance. However, the genetic factors identified in the human genome thus far by conventional methods are sparse and appear to have only captured a very small fraction of the overall heritability for the disorder. One key challenge in addiction genetics is how to identify genetic interactions and epistatic regulations that may play a more important role in determining risk for addictive behaviors than what gene variants do individually, and that may help explain a critical part of the missing link. Genetic interactions have rarely been systematically considered in studies of substance use, primarily due to lack of statistical power and shortage of computational methodology. To address the challenge, we propose a framework to systematically detect disease-relevant context specific genetic pathway interactions that underlie the risk for SUD. The framework will be applied to CUD and comorbid AUD to identify crucial genetic interactions and pleiotropic interactions, filling a critical gap in uncovering the genetic architectures of CUD. We will leverage genetic network and pathway topology and integrate multiple layers of omics including genomics, transcriptomic and epigenomic signals in drug abuse relevant tissues. By sharpening the focus on the functionally connected gene and regulation subsets through a priori analyses, we will be able to dramatically boost the statistical power to detect genetic interactions, arrive at highly biologically relevant and readily interpretable findings, and potentially provide clinically actionable insights. The proposed study will utilize outcomes from large GWAS studies for CUD and AUD, together with three high-risk population cohorts with elevated levels of severe cannabis and alcohol use disorders that have whole genome sequence data. We will complement the context specific pathway-level interaction analysis with high-dimensional variable screening machine-learning algorithms to identify both low and high order genetic interactions and regulatory epistatic effects associated with CUD. The findings that are carefully validated using independent study cohorts will be incorporated into a larger disease model of CUD for prediction and potential intervention, and will open up new avenues of research by allowing interrogation of the addiction genetics from a system’s level. The framework will be build in such a way that is readily transferable to other SUD and mental health studies and sets the stage for a genetically and epigenetically informed, precision medicine approach to SUD prevention and treatment. All software developed in the program will be freely available to the research community.

项目摘要 大麻使用障碍（CUD）在美国普遍存在，并且与其他药物使用高度合并 疾病（SUD），例如酒精使用障碍（AUD）以及其他心理健康问题。而 大麻使用/遗物的病因具有环境和遗传成分，大麻使用和 发现有问题的用途是高度遗传的。这是确定CUD遗传危险因素的研究 美国普通人口以及在高风险人口中，具有很高的公共健康重要性。然而， 到目前为止，通过常规方法在人类基因组中鉴定的遗传因素很稀疏，似乎 仅捕获了该疾病总体遗传力的一小部分。一个关键挑战 成瘾遗传学是如何识别遗传相互作用和上认识法规的方法 在确定添加剂行为风险中的重要作用比基因变体单独做的事情，这可能 帮助解释缺失链接的关键部分。遗传相互作用很少被系统地考虑 在对物质使用的研究中，主要是由于缺乏统计能力和计算短缺 方法论。为了应对挑战，我们提出了一个系统地检测与疾病相关的框架 背景特定的遗传途径相互作用是SUD风险的基础。该框架将用于 CUD和合并症aud确定关键的遗传相互作用和多效相互作用，填补了关键的间隙 在发现CUD的遗传体系结构时。我们将利用遗传网络和途径拓扑以及 综合的多层OMIC层，包括基因组学，转录组和表观基因组信号，药物滥用 相关组织。通过锐化对功能连接的基因和调节子集的关注 先验分析，我们将能够显着提高统计能力以检测遗传相互作用，到达 在高度生物学相关且易于解释的发现下，并有可能提供临床可行的发现 见解。拟议的研究将利用大型GWAS研究的结果进行CUD和AUD，以及 三个高风险人群队列的严重大麻和酒精使用障碍水平升高 整个基因组序列数据。我们将使用上下文特定的途径级交互分析 高维变量筛选机学习算法，以识别低和高阶遗传 与CUD相关的相互作用和调节性认识效应。经过精心验证的发现 使用独立研究队列将纳入更大的CUD疾病模型进行预测和 潜在的干预措施，并将通过询问成瘾来开辟新的研究途径 来自系统级别的遗传学。该框架将以容易转移到其他的方式建立 SUD和心理健康研究，为遗传和表观知识的精确度奠定了基础 药物预防和治疗方法。该程序中开发的所有软件都是免费的 可用于研究社区。