Use of Next-Gen Sequencing to Identify Genetic Variants that Influence compulsive Oxycodone Intake in Outbred Rats

摘要 NIDA动物遗传学计划的目的是识别遗传、基因组、表观遗传变异， 导致成瘾类行为的生理和大脑功能，相关的行为内表型， 以及药物使用障碍的行为共病。在过去的四年里，我们的多学科和 高度合作的联盟一直在识别与增加的基因变异相关的 对强迫性羟考酮使用的易感性，对羟考酮止痛效果的耐受性，以及发育 对戒断诱导的痛觉过敏的研究--使用一种先进的慢性痛觉过敏模型进行第一次GWA N/NIH异种血(HS)静脉注射羟考酮。我们还创建了第一个 临床前羟考酮生物库，使没有资源的研究人员能够进行慢性 静脉给药或下一代基因组测序以执行高级遗传， 分子和细胞研究，以加深我们对类似成瘾的生物学变化的理解 行为。虽然这些努力在实现计划的里程碑方面非常成功，但它已经成为 很明显，我们的项目将受益于更大的样本量。特别是，增加样本量会导致 以指数而不是线性的方式增加识别的基因座数量，这将使我们能够识别性别- 特定的基因变种。此外，在过去四年中，在技术方面取得了巨大的进步 行为和基因分析，可用于提供前所未有的途径来识别单个 核苷酸和结构变异导致复杂的行为内表型，与 羟考酮使用障碍。这次竞争性更新的第一个目标是将目前的样本量增加一倍 增加已确定的基因变体的数量，包括性别特定的变体，并满足 生物库的要求。第二个目标是使用无标记的高通量行为表型 基于机器学习和深度神经网络识别行为内表型的姿态估计 这有助于预测和识别具有抗性、轻度、中度或严重表型的个体。 羟考酮类成瘾行为。第三个目标是使用遗传基因的方法学改进 分析，包括结构变异和串联重复序列的分析，以及加强与 基因表达数据。第四个目标是加强羟考酮生物库基础设施。这个项目是 可能会继续对该领域产生持续而强大的影响，因为它将提供指数级的 与类成瘾有关的遗传基因座、eQTL和Phewas分析数量的增加 行为；建立第一个高通量的类似成瘾行为的行为基元分析使用并行 视频记录和自动机器学习分析；识别新的行为内表型 对类似成瘾行为的脆弱性/抵抗力；以及扩大和改进羟考酮生物库产品 和基础设施。

Abstract The purpose of the NIDA Animal Genetics Program is to identify genetic, genomic, epigenetic variants, physiology and brain functions that contribute to addiction-like behaviors, related behavioral endophenotypes, and behavioral comorbidities to substance use disorder. During the past four years, our multidisciplinary and highly collaborative consortium has been identifying gene variants that are associated with increased vulnerability to compulsive oxycodone use, tolerance to the analgesic effects of oxycodone, and development of withdrawal-induced hyperalgesia by performing the first GWAS using an advanced model of chronic intravenous oxycodone self-administration in N/NIH heterogeneous stock (HS). We have also created the first preclinical oxycodone biobank which enables researchers who do not have the resources to perform chronic intravenous self-administration or next-generation genome sequencing to perform advanced genetic, molecular, and cellular studies to further our understanding of the biological changes underlying addiction-like behaviors. While these efforts have been very successful in achieving the planned milestones, it has become clear that our project would benefit from an even larger sample size. In particular, increasing sample sizes lead to exponential rather than linear increase in the number of loci identified, and would allow us to identify sex- specific gene variants. Moreover, in the past four years there has been tremendous technological advances in behavioral and genetic analysis that can be leveraged to provide unprecedented access to identify the single nucleotide and structural variants that contribute to complex behavioral endophenotypes of high relevance to oxycodone use-disorders. The first goal of this competing renewal is to double the sample size of the current GWAS to increase the number of gene variants identified including sex-specific variants and meet the demands of the Biobank. The second goal is to use high-throughput behavioral phenotyping using markerless pose estimation based on machine learning with deep neural network to identify behavioral endophenotypes that can help predict and identify individuals with a resistant, mild, moderate, or severe phenotype of oxycodone addiction-like behaviors. The third goal is to use methodological improvements of the genetic analysis, including the analysis of structural variants and tandem repeats, as well as enhanced integration with gene expression data. The fourth goal is to strengthen the oxycodone biobank infrastructure. This project is likely to continue having a sustained and powerful impact on the field because it will provide an exponential increase in the number of genetic loci identified, eQTLs and PheWAS analysis related to addiction-like behavior; establish the first high-throughput behavioral motifs analysis of addiction-like behaviors using parallel video-recording and automated machine learning analysis; identify novel behavioral endophenotypes of vulnerability/resistance to addiction-like behaviors; and expand and improve the Oxycodone Biobank offering and infrastructure.