Elucidating phenotype and etiology of substance use disorders via integrative analysis of multi-dimensional datasets

通过多维数据集的综合分析阐明物质使用障碍的表型和病因

• 批准号: 10579580
• 负责人: JIANGWEN SUN
• 金额: $ 46.24万
• 依托单位: OLD DOMINION UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10579580
• 关键词: Address; Architecture; Binding; Biological Neural Networks; Biological Process; Biology; Brain region; Chromatin; Clinical; Cocaine Dependence; Cocaine use disorder; DNA Methylation; Data; Data Analyses; Data Set; Derivation procedure; Diagnosis; Diagnostic; Dimensions; Disease; Disease Management; Environment; Environmental Risk Factor; Etiology; Event; Genes; Genetic; Genetic Variation; Genetic study; Genomic Segment; Genotype; Heritability; Heterogeneity; Individual; Intervention; Joints; Measures; Mental Health; Methods; Modeling; Nature; Neurons; Outcome; Phenotype; Prevention; Sample Size; Sampling Studies; Severities; Source; Statistical Methods; Substance Use Disorder; Training; Translations; Twin Multiple Birth; Variant; addiction; cell type; database of Genotypes and Phenotypes; deep neural network; diagnostic criteria; disorder subtype; functional genomics; gene environment interaction; genetic association; genetic risk factor; genetic variant; genome wide association study; histone modification; improved; indexing; insight; large datasets; machine learning method; multi-task learning; multidimensional data; novel; opioid use disorder; predictive modeling; repository; response; substance use treatment; trait; transcription factor

Project Summary Substance use disorders (SUDs) have heterogeneous clinical manifestations and environmental and genetic risk factors intertwined etiology, demanding phenotype refinement and etiology elucidation for precise prevention, diagnosis, and treatment. Many genome-wide association studies (GWASs) have been carried out in recent years, aiming to discover the genetic risk factors of various forms of SUDs, such as cocaine and opioid use disorders. The high level of heterogeneity in both clinical presentations and etiology of SUDs compromises the effort for their genetic association discovery. As a result, the identified associations only explain a very small portion of the estimated heritability in twin-based studies, implying that the majority is still in the wild. In existing association studies, a heterogeneous composite trait (e.g., cocaine dependence diagnosis and diagnostic criteria count) was often used as the outcome variable and the specific set of phenotypes associated genetic variants is unclarified. Furthermore, the lack of mechanistic understanding of the identified associations hampers the translation of these discoveries into actionable targets to improve the disease management. In response to these challenges, novel machine learning methods will be developed enabling the integrative analysis of data from multiple dimensions, including phenotype, environment, genotype, and functional genomics. The developed methods will be employed to mine a large dataset aggregated for genetic study of SUDs and data available from multiple repositories, such as dbGap, UKBiobank, Roadmap, ENCODE, and NCBI GEO, aiming at 1) deriving severity indices of SUDs that have maximum heritability estimate, 2) identifying novel genetic risk factors for SUDs, 3) unraveling the association between heterogeneous clinical presentations and genetic variations in candidate genomic regions, and 4) elucidating the functional impact of genetic variants associated with SUDs and producing actionable findings. In Aim #1, a machine learning method for deriving severity indices by heritable component analysis taking into account gene-environment interplay will be developed and used to derive severity indices of SUDs, followed by GWASs. In Aim #2, a multi-view clustering framework that accounts for gene- environment interplay will be developed and used to elucidate SUD phenotypes associated with genetic variations in candidate genomic regions, followed by GWASs. In Aim #3, deep neural networks with novel architectures will be trained under a novel multi-task learning framework to predict functional genomic events in varying cell types from a wide range of brain regions and used to elucidate the functional impact of the genetic variants discovered by GWASs.

项目摘要 物质使用障碍(Suds)具有不同的临床表现以及环境和遗传风险。 病因学因素相互交织，需要表型精细化和病因学阐明才能精确预防， 诊断和治疗。近年来进行了许多全基因组关联研究(GWAS)， 旨在发现各种形式肥皂水的遗传风险因素，如可卡因和阿片类药物使用障碍。 SUDS的临床表现和病因学的高度异质性损害了 他们的基因关联发现。因此，已确定的关联只解释了很小一部分 在基于双胞胎的研究中估计的遗传力，意味着大多数仍然处于野生状态。在现有的 相关性研究，一种不同的综合特征(例如，可卡因依赖的诊断和诊断标准 COUNT)通常用作结果变量，与遗传变异相关的特定表型集是 不清楚。此外，对已确定的联系缺乏机械性的理解妨碍了 将这些发现转化为可操作的目标，以改善疾病管理。在回应这些问题时 挑战，将开发新的机器学习方法，从而能够对来自 多维度，包括表型、环境、基因和功能基因组学。已开发的 将使用各种方法来挖掘为SUD基因研究而收集的大型数据集和可从 多个存储库，如DBGaP、UK Biobank、Roadmap、ENCODE和NCBI GEO，旨在1)派生 具有最大遗传力估计的SODS严重程度指数，2)识别新的遗传风险因素 SODS，3)揭示不同临床表现和遗传变异之间的联系。 候选基因组区域，以及4)阐明与SODS和SODS相关的遗传变异对功能的影响 产生可操作的调查结果。在目标#1中，一种机器学习方法，用于根据可遗传因素导出严重性指数 将发展考虑基因-环境相互作用的成分分析，并用于得出严重性 肥皂水指数，其次是GWAS指数。在Aim#2中，一种多视图聚类框架，它解释了基因- 环境互作将被开发并用于阐明与遗传变异相关的sud表型。 在候选基因组区域，其次是GWASs。在目标#3中，具有新体系结构的深度神经网络 将在一种新的多任务学习框架下进行训练，以预测不同细胞中的功能基因组事件 来自广泛大脑区域的类型，并用于阐明遗传变异对功能的影响 被GWAS发现。