Building Bridges to Allow Cross-species Translational genetics for the Study of Addiction

为成瘾研究搭建跨物种转化遗传学桥梁

• 批准号: 10458063
• 负责人: Sandra Sanchez Roige
• 金额: $ 47.4万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10458063
• 关键词: Affect; Alcohols; Animal Model; Architecture; Cannabis; Catalogs; Data; Data Set; Development; Diagnosis; Functional disorder; Gene Expression; Genes; Genetic; Genetic study; Genotype; Genotype-Tissue Expression Project; Human; Human Genetics; Human Genome; Individual; Intervention; Investigation; Mental disorders; Methodology; Methods; Molecular; Monitor; Nature; Network-based; Oceans; Opioid; Pathway Analysis; Pharmaceutical Preparations; Phenotype; Research; Resources; Risk; Rodent; Signal Transduction; Substance Use Disorder; Sum; Techniques; Testing; Tobacco; Transcript; Translating; Translations; addiction; base; clinical translation; disability; follow-up; genetic association; genetic signature; genetic variant; genome wide association study; genomic data; genomic locus; genomic variation; improved; innovation; insight; multiple omics; neurobiological mechanism; novel; novel therapeutics; polygenic risk score; predictive modeling; small molecule; tool; trait; transcriptome; transcriptomics; translational genetics

PROJECT SUMMARY Substance use disorders are among the most common psychiatric disorders and are a leading cause of disability throughout the world. Over the last decade, genome-wide association studies (GWAS) have identified numerous genetic loci that contribute to addiction (opioid, tobacco, cannabis, alcohol). Turning these discoveries into mechanistic insights, a necessary first step for understanding the pathophysiology of addiction, and ultimately leading to the development of new therapies, is challenging. Model organisms serve as excellent tools to understand how human genomic variation affects traits. However, integration of human GWAS data with studies in model organisms has been limited. This is because GWAS do not lead to the identification of genes, but genetic variants (or SNPs), which cannot be easily translated across species. In addition, human GWAS have shown that risk for addiction is highly polygenic, but the existing strategies for cross-species translation do not capture the polygenic architecture of addiction. I am proposing an innovative solution to this problem by developing a framework for cross-species polygenic translation. Polygenic risk scores (PRS), which are a widely used tool for human genetic studies, predict risk for a trait by summing the contributions of numerous SNPs. Because these SNPs are species-specific, it is not possible to apply a PRS to another species. I am proposing to use transcriptomic analyses to overcome this obstacle. Leveraging resources and techniques from well-established statistical genetic tools, I will develop a method that will allow translation of polygenic signals from humans to rodents, and vice- versa. This will be accomplished by using the following steps: 1) use GWAS for addiction-related traits in human and model organisms to compile catalogs of genetic variants; 2) use transcriptomic data from GTEx and analogous datasets from model organisms to build gene prediction models, allowing estimation of transcript levels in individuals based on genotype information, 3) determine the association between these estimated transcript levels and addiction-related traits and 4) use these gene-level associations (rather than SNP level associations) to calculate Polygenic Transcriptomic Risk Scores (PTRS). This approach translates SNPs into gene estimated gene expression levels and then takes advantage of the fact that, while the same SNPs do not exist across species, gene orthology can be used to translate between species. In addition, I will use complementary methodologies, including cross-species network analyses and other tools, that also account for the polygenic nature of addiction. Developing tools that allow polygenic studies to share information between humans and model organisms will be transformative by opening up entirely new lines of research. For example, PTRS will provide a novel means of validating animal models of addiction, as it will be possible to empirically test whether the genetic signature for addiction in humans is related to the genetic signature of addiction phenotypes in rodents. PTRS will also serve as a toolkit for drug repositioning, namely studies aimed at identifying small molecules and other interventions that can alter the global gene expression in model organisms in a way that lower risk, as predicted by PTRS and network- based analyses.

项目概要 物质使用障碍是最常见的精神疾病之一，也是导致残疾的主要原因 全世界。在过去的十年中，全基因组关联研究（GWAS）已经发现了许多遗传 导致成瘾的位点（阿片类药物、烟草、大麻、酒精）。将这些发现转化为机械见解 了解成瘾病理生理学的必要第一步，并最终导致新疗法的开发 疗法，具有挑战性。模型生物体是了解人类基因组变异如何影响的优秀工具 特征。然而，人类 GWAS 数据与模式生物研究的整合受到限制。这是因为 GWAS 不会导致基因的识别，而是导致遗传变异（或 SNP）的识别，而遗传变异不能轻易地在不同国家之间进行翻译。 物种。此外，人类 GWAS 已表明成瘾风险具有高度多基因性，但现有策略 跨物种翻译并不能捕捉成瘾的多基因结构。我提出一个创新的解决方案 通过开发跨物种多基因翻译框架来解决这个问题。多基因风险评分 (PRS) 人类遗传学研究中广泛使用的工具，通过总结众多 SNP 的贡献来预测性状的风险。 由于这些 SNP 具有物种特异性，因此不可能将 PRS 应用于其他物种。我建议使用 转录组分析可以克服这一障碍。利用完善的统计资源和技术 遗传工具，我将开发一种方法，将多基因信号从人类翻译到啮齿动物，反之亦然- 反之亦然。这将通过以下步骤来完成：1）使用 GWAS 来分析人类和成瘾相关的特征 模型生物体以编制遗传变异目录； 2) 使用 GTEx 和类似数据集的转录组数据 从模型生物体到建立基因预测模型，允许根据 基因型信息，3) 确定这些估计的转录水平与成瘾相关特征之间的关联 4) 使用这些基因水平关联（而不是 SNP 水平关联）来计算多基因转录组风险 分数 (PTRS)。这种方法将 SNP 转化为基因估计的基因表达水平，然后利用 事实上，虽然不同物种之间不存在相同的 SNP，但基因直系同源性可用于在物种之间进行翻译。在 此外，我将使用补充方法，包括跨物种网络分析和其他工具，这些方法也 占 成瘾的多基因本质。开发允许多基因研究之间共享信息的工具 人类和模式生物将通过开辟全新的研究领域来实现变革。例如，PTRS 将提供一种验证成瘾动物模型的新方法，因为将有可能通过经验测试是否 人类成瘾的遗传特征与啮齿类动物成瘾表型的遗传特征有关。 PTRS 将 也可作为药物重新定位的工具包，即旨在识别小分子和其他干预措施的研究 正如 PTRS 和网络所预测的那样，它可以以降低风险的方式改变模型生物体中的整体基因表达 为基础的分析。