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: 10267762
• 负责人: Dana B Hancock
• 金额: $ 58.82万
• 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/10267762
• 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; 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.

项目概要/摘要 拟议研究的目标是发现神经生物学上可解释的成瘾基因网络， 为了实现这一目标，我们将应用一种新的多组学、多方法。 框架——基因网络识别和集成（GNetII）——识别相关的基因网络 独特的香烟或阿片类药物的结果以及这些成瘾者共享的网络包括。 全基因组上位性、可解释的人工智能、基因网络构建和证据线 这些基石方法将使大规模全基因组关联研究的整合成为可能。 (GWAS) 活体受试者数据、死后人脑数据（RNA 测序、DNA 甲基化、 来自成瘾病例和对照的染色质免疫沉淀测序和变异基因型 死者（已故个人）和公共组学数据吸烟和阿片类药物的结果。 遗传相关，我们有两个高度相关的并行 GWAS 和多组学大脑数据 组织，背外侧前额皮质和伏隔核，对于这两种成瘾。 香烟和阿片类药物是美国可预防的发病和死亡的主要原因。 成瘾影响着数百万美国成年人和青少年，并且具有高度遗传性（例如，阿片类药物成瘾比例为 54% 和 71%） GWAS 分析已在全基因组范围内识别出 300 多个位点 阿片类药物的 GWAS 在样本量上远远落后，但在全基因组范围内显着。 已知基因座的神经生物学效应在很大程度上是未知的，并且更多的基因座和联系。 我们率先应用新的大数据科学方法来发现其中的基因座。 脑组织中的大规模 GWAS 和基因调控数据将揭示以前未检测到的关系 （例如基因之间的上位相互作用）并增加成瘾背后的神经生物学知识。 我们提出以下具体目标： 目标 1：整合多组学数据以发现与香烟相关的基因网络。 目标 2：整合多组学数据以发现阿片类药物相关基因网络。 目标 3：整合多组学数据以找到与成瘾相关的一般基因网络。 对于香烟，目标 1 和 3 将利用 GWAS (N=528,259) 和死后人类的多组学数据 对于阿片类药物，目标 2 和 3 将使用 GWAS。 (N=49,178) 以及来自阿片类药物过量病例和对照决策的多组学大脑数据 (N=147)。 将在世界上最快的超级计算机 Summit 上进行，这将大大提高 我们的研究将捕获整个基因组中的复杂网络，以发现具有神经生物学意义的发现。 以前未知的基因，以及帮助解释越来越多的基因的神经生物学基础 与香烟或阿片类药物结果相关的基因位点。