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.

项目概要 大麻使用障碍 (CUD) 在美国很普遍，并且与其他物质使用高度共存 疾病（SUD），例如酒精使用障碍（AUD），以及其他心理健康问题。 大麻使用/滥用的病因有环境和遗传因素，大麻的使用和滥用 发现有问题的使用具有高度遗传性，因此，研究确定了 CUD 的遗传风险因素。 然而，美国普通人群和高危人群具有很高的公共卫生重要性。 迄今为止，通过传统方法在人类基因组中鉴定出的遗传因素很少，而且似乎 仅捕获了该疾病总体遗传力的一小部分。 遗传成瘾是如何识别可能发挥更大作用的遗传相互作用和上位调节 在决定成瘾行为风险方面，比基因变异单独发挥的作用更重要，这可能 帮助解释缺失环节的关键部分很少被系统地考虑。 在物质使用研究中，主要是由于缺乏统计能力和计算能力 为了应对这一挑战，我们提出了一个系统检测疾病相关的框架。 该框架将应用于构成 SUD 风险的特定遗传途径相互作用。 CUD 和共病 AUD 可识别关键的遗传相互作用和多效性相互作用，填补关键空白 在揭示 CUD 的遗传结构时，我们将利用遗传网络和通路拓扑结构。 整合多层组学，包括药物滥用中的基因组学、转录组学和表观基因组信号 通过加强对功能相关基因和调控子集的关注。 先验分析，我们将能够显着提高检测遗传相互作用的统计能力，得出 高度生物学相关且易于解释的发现，并有可能提供临床上可操作的 拟议的研究将利用 CUD 和 AUD 的大型 GWAS 研究结果以及 三个严重大麻和酒精使用障碍水平升高的高危人群 我们将补充上下文特定的通路水平相互作用分析。 高维变量筛选机器学习算法来识别低阶和高阶遗传 与 CUD 相关的相互作用和监管上位效应经过仔细验证。 使用独立研究队列将被纳入更大的 CUD 疾病模型中进行预测和 潜在的干预措施，并将通过允许审讯成瘾开辟新的研究途径 该框架将以易于转移到其他系统的方式构建。 SUD 和心理健康研究并为遗传和表观遗传的精确研究奠定了基础 该计划中开发的所有软件都将免费。 可供研究界使用。