Functional genetic variants in substance use disorders

物质使用障碍的功能性遗传变异

• 批准号: 10654033
• 负责人: HOWARD J EDENBERG
• 金额: $ 57.69万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10654033
• 关键词: 3' Untranslated Regions; Adopted; Affect; Alleles; Animals; Astrocytes; Biological Assay; Brain; Catalogs; Cell Line; Cells; Code; Complex; Computer Models; Computing Methodologies; Data; Diagnosis; Disease; Enhancers; Etiology; Future; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genetic Variation; Goals; Lead; Machine Learning; Maps; Measures; Mendelian randomization; Methods; Microglia; Mission; Modeling; Multiomic Data; Neurons; Nucleic Acid Regulatory Sequences; Oligodendroglia; Output; Pathway interactions; Phenotype; Prevention; Regulatory Element; Reporter; Resources; Risk; Role; Series; Substance Use Disorder; Testing; United States National Institutes of Health; Untranslated RNA; Variant; brain cell; causal variant; cell type; design; experimental study; follow-up; functional genomics; genetic variant; genome wide association study; genomic variation; improved; predictive modeling; promoter; trait

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.

项目摘要 这项研究的长期目标是确定与之相关的功能遗传变异 SUDS，从而有助于理解这些毁灭性的机制 疾病。我们的总体目标是（i）设计和实施一系列实验方法 评估与SUD和相关性状和计算相关的遗传变异的影响 预测其他变体的影响的模型，以及（ii）建立统计遗传学 框架更有效地识别有助于疾病的其他基因。这 提案基于我们先前关于基因组变异在基因调节和 将生成新数据，以与现有的多媒体数据和建模相结合。我们建议 以下特定目的：1。确定功能性遗传变异，导致风险 通过影响基因调节，使用药物使用障碍和相关表型 一系列高通量记者测定法，以评估成千上万的非 - 增强子，启动子和3'UTR的编码变化在八个细胞系中的基因表达 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性状的适度关联的变体 从GWAS目录和实验中进行。最初的大型变体的数据将 使用机器学习方法来预测附加的功能影响 监管变体。然后将测试一个预测功能性的大量变体子集 随访HTRA，并且根据数据进行了预测模型。 2。识别基因 其表达变化有助于SUD的风险。我们将使用门德利安 基于从AIM 1 Plus GWAS数据得出的数据基于随机化的方法 PGC和其他大型财团，以鉴定其表达在特定细胞中的变化的因果基因 类型有助于与SUD相关的表型。完成这些研究后，我们将 创建了与SUD相关的遗传变异的独特，可访问的资源，以调节目标 特定脑细胞类型中的基因表达。这些基因可以作为高优先级候选者 用于未来的功能和基于动物的研究。