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

项目摘要 大麻使用障碍（CUD）在美国很普遍，与其他药物使用高度共病 酒精使用障碍（AUD）等精神疾病（SUD）以及其他精神健康问题。而 大麻使用/滥用的病因学有环境和遗传两个组成部分，大麻使用和 有问题的使用被发现是高度遗传的。因此，确定CUD遗传风险因素的研究， 一般美国人群和高危人群具有很高的公共卫生重要性。然而，在这方面， 迄今为止，通过常规方法在人类基因组中鉴定的遗传因子是稀疏的， 只捕捉到了这种疾病整体遗传性的一小部分。一个关键的挑战， 成瘾遗传学是如何识别遗传相互作用和上位性调节，可能发挥更大的作用， 在决定成瘾行为风险方面的重要作用，而不是基因变异单独起作用，这可能 有助于解释缺失环节的关键部分遗传相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缺乏计算能力， 方法论为了应对这一挑战，我们提出了一个框架，系统地检测疾病相关的 背景特异性遗传途径相互作用是SUD风险的基础。该框架将适用于 CUD和共病AUD，以确定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 揭示了CUD的遗传结构。我们将利用遗传网络和通路拓扑结构， 在药物滥用中整合多层次组学，包括基因组学、转录组学和表观基因组学信号 相关组织。通过加强对功能相关基因和调控子集的关注， 先验分析，我们将能够大大提高检测遗传相互作用的统计能力， 具有高度生物学相关性和易于解释的结果，并可能提供临床可操作性 见解.拟议的研究将利用CUD和AUD的大型GWAS研究的结果， 三个严重大麻和酒精使用障碍水平升高的高风险人群队列， 全基因组序列数据。我们将补充上下文特定的路径级交互分析， 用于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节上位效应。经过仔细验证的发现 将使用独立研究队列纳入更大的CUD疾病模型进行预测， 潜在的干预，并将开辟新的研究途径，允许审讯成瘾 从系统的层面来研究基因。该框架将建立在这样一种方式，即很容易转移到其他 SUD和心理健康研究，并为遗传和表观遗传学的知情，精确 医学方法对SUD的预防和治疗。在该计划中开发的所有软件将免费 提供给研究界。

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.