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

项目总结 大麻使用障碍(CUD)在美国很普遍，并与其他物质使用高度共存 精神障碍(SUD)，如酒精使用障碍(AUD)以及其他精神健康问题。而当 大麻使用/滥用的病因既有环境因素，也有遗传因素，大麻使用和 有问题的使用被发现是高度可遗传的。因此，确定慢性阻塞性肺病的遗传风险因素的研究 美国普通人群和高危人群对公共卫生具有很高的重要性。然而， 到目前为止，通过常规方法在人类基因组中识别的遗传因素很少，似乎 只捕捉到了这种疾病总体遗传性的一小部分。面临的一个关键挑战 成瘾遗传学是如何识别基因的相互作用和上位调节，可能起到更多的作用 在确定成瘾行为风险方面的重要作用比基因变异单独作用更重要，这可能 帮助解释缺失环节的一个关键部分。遗传交互作用很少被系统地考虑。 在物质使用的研究中，主要是由于缺乏统计能力和计算能力 方法论。为了应对这一挑战，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。该框架将应用于 CUD和共病AUD识别关键的遗传相互作用和多效性相互作用，填补了一个关键的空白 在揭示CUD的基因结构方面。我们将利用遗传网络和路径拓扑，并 整合多层组学，包括基因组学、转录组和表观组学信号与药物滥用 相关组织。通过加强对功能相关基因和调节亚集的关注 先验分析，我们将能够极大地提高检测遗传交互作用的统计能力， 具有高度的生物学相关性和易于解释的发现，并有可能提供临床上可操作的 洞察力。拟议的研究将利用全球气候变化研究组织对CUD和AUD的大型研究结果，以及 严重大麻和酒精使用障碍水平升高的三个高危人群队列 全基因组序列数据。我们将通过以下内容补充特定于上下文的路径级别交互分析 识别低阶和高阶遗传的高维变量筛选机器学习算法 与CUD相关的相互作用和调控上位性效应。经过仔细验证的发现 使用独立研究队列将被纳入更大的CUD疾病模型，用于预测和 潜在的干预，并将开辟新的研究途径，允许审问成瘾 从一个系统的层面上看遗传学。该框架将以一种易于转让给其他国家方式建立 SUD和精神健康研究，并为遗传和表观遗传信息的精确度奠定基础 从医学角度探讨如何防治自闭症。在该计划中开发的所有软件都将是免费的 可供研究界使用。

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.