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（SUD）具有异质性临床表现以及环境和遗传风险 病因因素交织在一起，需要表型细化和病因学阐明以进行精确预防， 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SUD的遗传风险因素，如可卡因和阿片类药物使用障碍。 SUD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，所确定的关联只能解释一小部分 基于双胞胎的研究中估计的遗传力，这意味着大多数仍然在野外。在现有 关联研究，异质复合性状（例如，可卡因依赖诊断和诊断标准 计数）经常被用作结果变量，并且与遗传变异相关的表型的特定集合被 未澄清。此外，缺乏对所确定的协会的机械性理解阻碍了 将这些发现转化为可操作的目标，以改善疾病管理。针对这些 挑战，将开发新的机器学习方法，使数据的综合分析， 多维度，包括表型、环境、基因型和功能基因组学。发达 方法将被用来挖掘一个大的数据集聚合的遗传研究的SUD和数据可从 多个存储库，如dbGap，UKBiobank，Roadmap，ENCODE和NCBI GEO，旨在1）导出 具有最大遗传力估计值的SUD严重程度指数，2）确定新的遗传风险因素， SUD，3）解开异质性临床表现和遗传变异之间的关联， 候选基因组区域，以及4）阐明与SUD相关的遗传变体的功能影响， 产生可操作的结果。在目标#1中，提出了一种通过遗传因素推导严重性指数的机器学习方法。 将开发考虑基因-环境相互作用的成分分析，并用于推导严重程度 指数的SUD，其次是GWAS。在目标#2中，一个多视图聚类框架，该框架考虑了基因- 环境相互作用将被开发和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GWAS。在目标#3中，具有新架构的深度神经网络 将在一个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。 从广泛的大脑区域的类型，并用于阐明遗传变异的功能影响 被GWAS发现