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

项目摘要/摘要 这项拟议的研究的目标是发现成瘾的神经生物学解释的基因网络， 特别是香烟和阿片类药物。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-基因网络识别和整合(GNetII)-识别相关的基因网络 吸烟或阿片类药物的结果和网络在这些成瘾中共享，这是独一无二的。GNetII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路线 方法：研究方法。这些基石方法将使大规模全基因组关联研究整合成为可能 活体受试者的数据，死后人脑数据(RNA测序，DNA甲基化， 染色质免疫沉淀测序和变异基因型别) 死者(已故个人)和公共组学数据。吸烟和阿片类药物的后果是 基因相关，我们有两个高度相关的平行GWAS和多组学大脑数据 组织，背外侧前额叶皮质和伏隔核，对于这两种成瘾。 香烟和阿片类药物是美国可预防的发病率和死亡率的主要原因。这些 成瘾影响着数以百万计的美国成年人和年轻人，并具有很高的遗传性(例如，54%和71%的阿片类药物成瘾 成瘾和尼古丁依赖)。GWAS分析已经在全基因组范围内确定了300多个基因座 吸烟的意义。阿片类药物的GWA在样本大小上远远落后，但在全基因组范围内意义重大 基因座正在浮现。已知基因座的神经生物学效应在很大程度上是未知的，更多的基因座和联系 其中的基因座仍有待发现。我们假设将新的大数据科学方法应用于 脑组织中大规模的GWAS和基因调控数据将揭示以前未发现的关系 (如基因之间的上位性相互作用)，并增加成瘾的神经生物学知识。 我们提出了以下具体目标： 目的1：整合多组学数据以发现香烟相关基因网络。 目的2：整合多组学数据以发现阿片类药物相关基因网络。 目的3：整合多组学数据以寻找与成瘾相关的一般基因网络。 对于香烟，AIMS 1和AIMS 3将利用GWAS(N=528,259)和死后人类的多组学数据 吸烟者和非吸烟者的大脑(N=262)。对于阿片类药物，AIMS 2和AIMS 3将使用GWAS (n=49,178)和来自阿片类药物过量病例和对照死者的多组学脑数据(N=147)。分析 将在世界上最快的超级计算机Summit上运行，这将极大地提高 神经生物学上有意义的发现。我们的研究将捕捉整个基因组的复杂网络，以找到 以前未知的基因，以及有助于解释不断增长的数量的神经生物学基础 与香烟或阿片类药物结果相关的遗传基因座。

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.