Integrating Multiple Omics to Illuminate Gene Networks Underlying Cigarette Smoking and Opioids.

整合多个组学来阐明吸烟和阿片类药物背后的基因网络。

• 批准号: 10439854
• 负责人: Dana B Hancock
• 金额: $ 57.7万
• 依托单位: RESEARCH TRIANGLE INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-30 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10439854
• 关键词: Address; Affect; Algorithms; Artificial Intelligence; Attenuated; Autopsy; Biological Markers; Brain; Cessation of life; ChIP-seq; Cigarette; Complex; Cotinine; DNA Methylation; DNA methylation profiling; Data; Epidemic; Frequencies; Gene Expression Regulation; Genes; Genetic; Genetic Epistasis; Genome; Genotype; Genotype-Tissue Expression Project; Goals; Heritability; Human; Individual; Intelligence; International; Knowledge; Laboratories; Maps; Methods; Morbidity - disease rate; Multiomic Data; Neurobiology; Nicotine Dependence; Nucleus Accumbens; Opiate Addiction; Opioid; Oranges; Outcome; Pharmaceutical Preparations; Prefrontal Cortex; Public Health; Reporting; Research; Sample Size; Signal Transduction; Smoker; Smoking; Systems Biology; Testing; Time; Tissues; Toxicology; United States; Variant; Weight; addiction; base; big-data science; brain tissue; case control; cigarette addiction; cigarette smoking; gene network; genome wide association study; genome-wide; genomic locus; improved; insight; mortality; multiple omics; non-smoker; novel; opioid overdose; opioid use; random forest; smoking addiction; smoking initiation; supercomputer; symposium; transcriptome sequencing; young adult

PROJECT SUMMARY/ABSTRACT The goal of the proposed research is to discover neurobiologically interpretable gene networks for addiction, specifically for cigarettes and opioids. To achieve this goal, we will apply a new multi-omics, multi-method framework—Gene Network Identification and Integration (GNetII)—to identify gene networks associated uniquely with cigarette or opioid outcomes and networks shared across these addictions. GNetII includes genome-wide epistasis, Explainable Artificial Intelligence, gene network construction, and Lines-of-Evidence methods. These cornerstone methods will enable integration of large-scale genome-wide association study (GWAS) data in living subjects, postmortem human brain data (RNA-sequencing, DNA methylation, chromatin immunoprecipitation sequencing, and variant genotypes) from addiction case and control decedents (deceased individuals), and public omics data. Cigarette smoking and opioid outcomes are genetically correlated, and we have parallel GWAS and multi-omics brain data available in two highly relevant tissues, dorsolateral prefrontal cortex and nucleus accumbens, for both of these addictions. Cigarettes and opioids are leading causes of preventable morbidity and mortality in the United States. These addictions affect millions of U.S. adults and youths and are highly heritable (e.g., 54% and 71% for opioid addiction and nicotine dependence, respectively). GWAS analyses have identified 300+ loci at genome-wide significance for smoking. GWAS for opioids are farther behind in sample size, but genome-wide significant loci are emerging. Neurobiological effects of known loci are largely unknown, and more loci and connections among the loci are still to be discovered. We hypothesize that applying new big data science methods to large-scale GWAS and gene regulation data in brain tissue will reveal previously undetected relationships (such as epistatic interactions between genes) and add knowledge of the neurobiology underlying addiction. We propose the following specific aims: Aim 1: Integrate multi-omics data to discover cigarette-associated gene networks. Aim 2: Integrate multi-omics data to discover opioid-associated gene networks. Aim 3: Integrate multi-omics data to find general addiction-associated gene networks. For cigarettes, Aims 1 and 3 will leverage GWAS (N=528,259) and multi-omics data in postmortem human brain from active smoker and nonsmoker decedents (N=262). For opioids, Aims 2 and 3 will use GWAS (N=49,178) and multi-omics brain data from opioid overdose case and control decedents (N=147). Analyses will be performed on Summit, the world's fastest supercomputer, which will greatly improve the likelihood of neurobiologically meaningful discoveries. Our study will capture complex networks across the genome to find previously unknown genes, as well as help explain the neurobiological underpinnings for the growing number of genetic loci associated with cigarette or opioid outcomes.

项目摘要/摘要 拟议的研究的目的是发现成瘾的神经生物学解释基因网络， 为了实现这一目标，我们将采用新的多媒体，多方法 框架 - 基因网络识别和集成（GNETII） - 确定相关的基因网络 与这些成瘾共享的香烟或阿片类药物的结果和网络独特。 gnetii包括 全基因组的上毒，解释了人工智能，基因网络构建和证据 方法。这些基石方法将使大规模基因组关联研究能够整合 （GWAS）生命受试者，死后人脑数据中的数据（RNA测序，DNA甲基化， 染色质免疫沉淀测序和成瘾案例的变异基因型） 死者（已故的个人）和公共法学数据。吸烟和阿片类药物的结果是 通常相关的相关性，我们在两个高度相关的两个高度相关的GWA和多摩变大脑数据中可用 这两种添加物的组织，背外侧前额叶皮层和伏伏核。 香烟和阿片类药物是美国可预防的发病率和死亡率的主要原因。这些 成瘾会影响数百万的美国成年人和青年，并且是高度遗传的（例如，卵子的54％和71％ 成瘾和尼古丁依赖性）。 GWAS分析已确定在全基因组的300多个基因座 吸烟的意义。阿片类药物的GWAS在样本量中远远落后于样本量，但全基因组显着 基因座正在出现。已知基因座的神经生物学效应在很大程度上是未知的，更多的基因座和连接 在当地中，仍有待发现。我们假设将新的大数据科学方法应用于 脑组织中的大规模GWA和基因调节数据将揭示先前未发现的关系 （例如基因之间的同义相互作用）并增加对成瘾基础神经生物学的知识。 我们提出以下具体目标： 目标1：整合多摩斯数据以发现与民用相关的基因网络。 AIM 2：整合多摩变数据以发现与阿片类药物相关的基因网络。 AIM 3：整合多媒体数据以找到一般成瘾相关的基因网络。 对于香烟，目标1和3将利用GWAS（n = 528,259）和多摩学人的数据 活跃的吸烟者和非吸烟者的大脑决定（n = 262）。对于阿片类药物，目标2和3将使用GWAS （n = 49,178）和来自阿片类药物过量病例的多摩斯脑数据和控制决定（n = 147）。分析 将在世界上最快的超级计算机峰会上进行，这将大大提高 神经生物学上有意义的发现。我们的研究将捕获整个基因组的复杂网络，以找到 以前未知的基因，并有助于解释数量增长的神经生物学基础 与香烟或阿片类药物结局相关的遗传环境。