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: 10681221
• 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/10681221
• 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; Positioning Attribute; Research; Resources; Risk; Rodent; Signal Transduction; Substance Use Disorder; Sum; Techniques; Testing; Tobacco; Transcript; Translating; Translations; addiction; clinical translation; data integration; disability; follow-up; genetic association; genetic signature; genetic variant; genome wide association study; genomic data; genomic locus; genomic variation; improved; innovation; insight; model organism; multiple omics; neurobiological mechanism; novel; novel therapeutics; polygenic risk score; predictive modeling; risk prediction; 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.

项目摘要 物质使用障碍是最常见的精神疾病之一，是残疾的主要原因 在全世界都有。在过去的十年里，全基因组关联研究（GWAS）已经确定了许多遗传学上的差异。 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的见解， 了解成瘾的病理生理学的必要的第一步，并最终导致新的发展。 治疗，具有挑战性。模式生物是了解人类基因组变异如何影响 性状然而，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，但遗传变异（或SNP），这不能很容易地翻译。 物种此外，人类GWAS已经表明，成瘾风险是高度多基因的，但现有的策略， 跨物种翻译不能捕捉成瘾的多基因结构。我提出了一个创新的解决方案， 通过开发跨物种多基因翻译的框架来解决这个问题。多基因风险评分（PRS），这是一个 人类遗传学研究中广泛使用的工具，通过总结许多SNP的贡献来预测一个特征的风险。 因为这些SNP是物种特异性的，所以不可能将PRS应用于另一个物种。我建议用 转录组学分析来克服这一障碍。利用完善的统计数据库的资源和技术， 基因工具，我将开发一种方法，将允许翻译多基因信号从人类到啮齿动物，和副- 亦然这将通过使用以下步骤来实现：1）使用GWAS用于人类中与成瘾相关的特征， 模拟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 从模式生物中建立基因预测模型，允许基于以下信息估计个体中的转录水平： 基因型信息，3）确定这些估计的转录水平和成瘾相关性状之间的关联 和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 评分（PTRS）。这种方法将SNP翻译成基因估计的基因表达水平，然后利用SNP的基因表达水平。 事实上，虽然不同物种之间不存在相同的SNP，但基因同源性可用于物种之间的翻译。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， 占 成瘾的多基因性开发工具，允许多基因研究之间共享信息 人类和模式生物将通过开辟全新的研究路线而发生变革。例如，PTRS 这将提供一种验证成瘾动物模型的新方法，因为它将有可能以经验的方式测试成瘾动物模型是否具有成瘾性。 人类成瘾的遗传标记与啮齿动物成瘾表型的遗传标记有关。PTRS将 也可作为药物重新定位的工具包，即旨在确定小分子和其他干预措施的研究 这可以改变模式生物的整体基因表达，降低风险，正如PTRS和网络预测的那样， 基于分析。

项目成果

Shared genetic architecture between attention-deficit/hyperactivity disorder and lifespan.

• DOI: 10.1038/s41386-023-01555-x
• 发表时间: 2023-06
• 期刊: NEUROPSYCHOPHARMACOLOGY
• 影响因子: 7.6
• 作者: Vilar-Ribo, Laura;Cabana-Dominguez, Judit;Martorell, Lourdes;Antoni Ramos-Quiroga, Josep;Sanchez-Roige, Sandra;Palmer, Abraham A.;Vilella, Elisabet;Ribases, Marta;Muntane, Gerard;Soler Artigas, Maria
• 通讯作者: Soler Artigas, Maria

Accelerating Opioid Use Disorders Research by Integrating Multiple Data Modalities.

通过整合多种数据模式加速阿片类药物使用障碍研究。

• DOI: 10.1159/000525079
• 发表时间: 2022
• 期刊: Complex psychiatry
• 作者: Bianchi,SevimB;Jeffery,AlvinD;Samuels,DavidC;Schirle,Lori;Palmer,AbrahamA;Sanchez-Roige,Sandra
• 通讯作者: Sanchez-Roige,Sandra

Detecting Problematic Opioid Use in the Electronic Health Record: Automation of the Addiction Behaviors Checklist in a Chronic Pain Population.

检测电子健康记录中存在问题的阿片类药物使用：慢性疼痛人群成瘾行为检查表的自动化。

• DOI: 10.1101/2023.06.08.23290894
• 发表时间: 2023
• 期刊: medRxiv : the preprint server for health sciences
• 作者: Chatham,AngusH;Bradley,EliD;Schirle,Lori;Sanchez-Roige,Sandra;Samuels,DavidC;Jeffery,AlvinD
• 通讯作者: Jeffery,AlvinD

Substance use and common contributors to morbidity: A genetics perspective.

• DOI: 10.1016/j.ebiom.2022.104212
• 发表时间: 2022-09
• 期刊: EBIOMEDICINE
• 影响因子: 11.1
• 作者: Sanchez-Roige, Sandra;Kember, Rachel L.;Agrawal, Arpana
• 通讯作者: Agrawal, Arpana