Functional genetic variants in substance use disorders

项目总结 这项研究的长期目标是确定与 并因此有助于理解这些毁灭性事件背后的机制 精神错乱。我们的总体目标是(I)设计和实施一系列实验方法 评估与肥胖症和相关性状相关的遗传变异的影响以及计算 预测额外变异的影响的模型，以及(Ii)建立统计遗传学 框架，以更有效地识别导致疾病的其他基因。这 该建议建立在我们之前关于基因组变异在基因调控和基因调控中的作用的研究基础上。 将生成新的数据，以与现有的多组学数据和建模相结合。我们建议 以下是具体目标：1.找出导致糖尿病风险的功能性遗传变异 通过影响基因调控的物质使用障碍和相关表型，使用 系列高通量记者化验评估千千万万非 八种细胞系基因表达增强子、启动子和3‘端非编码区的编码变异 代表大脑中的四种主要细胞类型(神经元、小胶质细胞、星形胶质细胞和少突胶质细胞)。 将选择与物质使用障碍和相关特征有适度关联的变种 和正在进行的实验。关于最初的大型变种集的数据将 使用机器学习方法来预测额外的功能影响 监管变种。然后将对预测具有功能的变体的一大子集进行测试 通过跟踪HTRAS，并根据数据改进预测模型。2.识别基因 其表达的变化会增加肥厚症的风险。我们将使用孟德尔式 基于AIM 1数据的随机化方法和 PGC和其他大型联合体识别其在特定细胞中表达变化的原因基因 类型对与SUD相关的表型有贡献。在完成这些研究后，我们将拥有 创建了一个独特的、可访问的与SUD相关的基因变体资源，用于调节靶标 基因在特定脑细胞类型中的表达。这些基因可以作为高优先级的候选基因 用于未来的功能和动物研究。

PROJECT SUMMARY The long-term goal of this study is to identify functional genetic variants that are associated with SUDs, and thereby contribute to understanding the mechanisms underlying these devastating disorders. Our overall objectives are to (i) design and implement a series of experimental methods to assess the impact of genetic variants relevant to SUDs and related traits and computational models to predict the impact of additional variants, and (ii) establish a statistical genetics framework to more effectively identify additional genes that contribute to the disorders. This proposal builds upon our previous studies on the role of genomic variation in gene regulation and will generate new data to combine with existing multi-omics data and modeling. We propose the following Specific Aims: 1. Identify functional genetic variants that contribute to the risk for substance use disorders and related phenotypes by affecting gene regulation, using a series of high-throughput reporter assays to evaluate the impact of tens of thousands of non- coding variations in enhancers, promoters and 3’UTRs on gene expression in eight cell lines representing four major cell types in brain (neurons, microglia, astrocytes and oligodendrocytes). Variants with a modest association with substance use disorders and related traits will be selected from the GWAS catalog and experiments underway. Data on the initial large sets of variants will be used to predict, using machine learning approaches, the functional impact of additional regulatory variants. A large subset of the variants predicted to be functional will then be tested with follow-up HTRAs, and the predictive models refined based upon the data. 2. Identify genes whose expression changes contribute to the risk for SUDs. We will use Mendelian Randomization-based methods based on the data derived from Aim 1 plus GWAS data from the PGC and other large consortia to identify causal genes whose expression changes in specific cell types contribute to SUD-related phenotypes. Upon completion of these studies, we will have created a unique, accessible resource of SUD-associated genetic variants that regulate target gene expression in specific brain cell types. These genes can serve as high priority candidates for future functional and animal-based studies.