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， 生理学和大脑功能，有助于成瘾样行为，相关的行为内表型， 和行为共病与物质使用障碍。在过去的四年里，我们的多学科和 高度合作的联盟已经确定了与增加的 对羟考酮强迫使用的脆弱性、对羟考酮镇痛作用的耐受性和发育 通过使用慢性疼痛的高级模型进行第一次GWAS， 在N/NIH异质储备液（HS）中静脉内羟考酮自我给药。我们也创造了第一个 临床前羟考酮生物库，使没有资源进行慢性 静脉内自我给药或下一代基因组测序， 分子和细胞研究，以进一步了解成瘾样的生物学变化， 行为。虽然这些努力在实现计划的里程碑方面非常成功， 很明显，我们的项目将受益于更大的样本量。特别是，增加样本量导致 指数增长而不是线性增长的基因座的数量确定，并允许我们确定性别- 特定的基因变异。此外，在过去四年中， 行为和遗传分析，可以利用提供前所未有的访问，以确定单一的 核苷酸和结构变异，有助于复杂的行为内表型的高度相关性， 羟考酮使用障碍。这种竞争性更新的第一个目标是将目前的样本量增加一倍。 GWAS旨在增加鉴定的基因变异数量，包括性别特异性变异，并满足 生物银行的要求第二个目标是使用无标记的高通量行为表型分析， 基于深度神经网络的机器学习的姿势估计，以识别行为内表型 这可以帮助预测和识别具有耐药、轻度、中度或重度表型的个体， 羟考酮成瘾样行为第三个目标是使用遗传学的方法学改进， 分析，包括结构变异和串联重复序列的分析，以及与 基因表达数据。第四个目标是加强羟考酮生物库的基础设施。这个项目是 可能会继续对该领域产生持续和强大的影响，因为它将提供一个指数 增加的遗传位点的数量确定，eQTL和PheWAS分析相关的成瘾样 行为;建立第一个高通量的行为基序分析成瘾样行为使用并行 视频记录和自动化机器学习分析;识别新的行为内在表型 对成瘾样行为的脆弱性/抵抗力;以及扩大和改善羟考酮生物库的产品 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.