Multi-omics Gene Network Identification (Project 4)

项目总结/摘要 项目4的目标是发现阿片类药物的神经生物学解释的基因网络 成瘾（OA），但在其他方面错过了传统的统计方法。为了实现这一目标，我们将 应用我们的多组学，多方法框架-基因网络识别和整合（GNetII）- 通过利用人类和啮齿动物模型中的P50数据来识别OA相关基因网络。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 证据法（LOE）。这些基础方法使个人层面的多个层面的整合成为可能。 数据，我们将专门整合220，722年的大规模全基因组关联研究（GWAS）数据， 来自项目1和协同核心的活体受试者，基因调控数据（RNA测序，DNA 甲基化、染色质免疫沉淀测序和变异基因型） 641例OA患者的相关脑组织（包括前额叶皮层、杏仁核和杏仁核）， 对照死亡动物（死亡个体）来自项目2，实验小鼠和大鼠模型结果来自 项目3和其他公共组学数据。 在美国，OA是可预防的发病率和死亡率的主要原因， 美国成年人和年轻人的数量前所未有。OA具有高度遗传性（约54%）。然而，很少有基因位点 最终确定了OA和相关结局，以及基于常见遗传变异的遗传力 只能解释OA变异的17%我们假设上位性（即，变异体或基因的相互作用） 导致了遗传性缺失将大数据科学方法应用于大规模GWAS，基因 大脑组织中的调控数据，跨物种数据将揭示以前未被发现的关系，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： 目的1：通过全基因组上位性构建OA相关基因网络。 目标2：使用死后人脑数据构建多组学网络。 目标3：整合跨物种的网络，以找到具有多个LOE的OA相关基因网络。 分析将在强大的高性能计算架构在橡树岭进行 这将大大提高神经生物学意义的发现的可能性。我们 这项研究将捕捉整个基因组的复杂网络，以发现以前未知的基因，并将有助于 解释了P50和P50之间出现的遗传基因座的神经生物学基础。 更广阔的领域。

PROJECT SUMMARY/ABSTRACT The goal of Project 4 is to discover neurobiologically interpretable gene networks that underlie opioid addiction (OA) but are otherwise missed by traditional statistical approaches. To achieve this goal, we will apply our multi-omics, multi-method framework—Gene Network Identification and Integration (GNetII)—to identify OA-associated gene networks, by capitalizing on P50 data across human and rodent models. GNetII includes genome-wide epistasis, explainable artificial intelligence, gene network construction, and lines-of- evidence (LOE) methods. These cornerstone methods enable integration of multiple levels of individual-level data, and we will specifically integrate large-scale genome-wide association study (GWAS) data in 220,722 living subjects from Project 1 and the Synergy Core, gene regulation data (RNA-sequencing, DNA methylation, chromatin immunoprecipitation sequencing, and variant genotypes) from multiple addiction- relevant brain tissues (including prefrontal cortex, nucleus accumbens, and amygdala) from 641 OA case and control decedents (deceased individuals) from Project 2, experimental mouse and rat model results from Project 3, and additional public omics data. OA is a leading cause of preventable morbidity and mortality in the United States, afflicting an unprecedented number of U.S. adults and youth. OA is highly heritable (~54%). Yet, few genetic loci have been conclusively identified for OA and related outcomes, and common genetic variant-based heritability explains only 17% of the variance in OA. We hypothesize that epistasis (i.e., interaction of variants or genes) contributes to the missing heritability. Applying big data science methods to large-scale GWAS, gene regulation data in brain tissue, and cross-species data will reveal previously undetected relationships and add knowledge of the neurobiology underlying addiction. We propose the following specific aims: Aim 1: Build OA-associated gene networks via genome-wide epistasis. Aim 2: Build multi-omics networks using postmortem human brain data. Aim 3: Integrate networks across species to find OA-associated gene networks with multiple LOE. Analyses will be performed on the powerful high performance computing architectures at the Oak Ridge National Laboratory which will greatly improve the likelihood of neurobiologically meaningful discoveries. Our study will capture complex networks across the genome to find previously unknown genes and will help explain the neurobiological underpinnings for the genetic loci that emerge from across the P50 and the broader field.