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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要物质使用障碍 (SUD) 具有异质性的临床表现以及环境和遗传风险病因学因素相互交织，需要表型细化和病因学阐明以进行精确预防，诊断、治疗。近年来开展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，例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治疗的努力他们的遗传关联发现。因此，所确定的关联仅解释了一小部分基于双胞胎的研究中估计的遗传力，这意味着大多数仍处于野生状态。在现有的关联研究，异质复合特征（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，与遗传变异相关的一组特定表型是未澄清。此外，缺乏对已确定的关联的机械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，将开发新颖的机器学习方法，从而能够对来自的数据进行综合分析多个维度，包括表型、环境、基因型和功能基因组学。所开发的将采用方法来挖掘用于 SUD 遗传研究的大型数据集和可从多个存储库，例如 dbGap、UKBiobank、Roadmap、ENCODE 和 NCBI GEO，旨在 1) 导出具有最大遗传力估计的 SUD 的严重性指数，2) 识别新的遗传风险因素 SUD，3）揭示异质性临床表现与遗传变异之间的关联候选基因组区域，4) 阐明与 SUD 相关的遗传变异的功能影响和产生可操作的发现。在目标#1中，一种通过遗传推导严重性指数的机器学习方法将开发考虑基因与环境相互作用的成分分析并用于得出严重程度 SUD 指数，其次是 GWAS。在目标 #2 中，一个多视图聚类框架可以解释基因- 将开发环境相互作用并用于阐明与遗传变异相关的 SUD 表型候选基因组区域，其次是 GWAS。在目标#3中，具有新颖架构的深度神经网络将在新型多任务学习框架下接受训练，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，用于阐明遗传变异的功能影响由 GWAS 发现。