A decentralized macro and micro gene-by-environment interaction analysis of substance use behavior and its brain biomarkers

物质使用行为及其大脑生物标志物的分散宏观和微观基因与环境相互作用分析

• 批准号: 10197867
• 负责人: VINCE D CALHOUN
• 金额: $ 54.27万
• 依托单位: GEORGIA STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10197867
• 关键词: Address; Adolescence; Adolescent; Age; Anxiety; Behavior; Behavioral; Behavioral Genetics; Biological Markers; Brain; Brain imaging; Brain scan; China; Climate; Communities; Complex; Computer software; Consumption; Country; Data; Data Analyses; Data Protection; Decentralization; Development; Disease; Dropout; Economic Burden; Environment; Environmental Risk Factor; Epidemiology; Ethnic group; Europe; Family Study; Genes; Genetic; Genomics; Genotype; Heritability; Household; Image; Income; India; Individual; Informatics; International; Intervention; Learning; Legal; Magnetic Resonance Imaging; Measures; Mental Depression; Modeling; Movement; Neurobiology; Neurons; Neurosciences; Outcome; Participant; Physiological; Population; Population Density; Privacy; Quality Control; Race; Regulation; Risk; Scanning; Site; Smoking; Smoking Behavior; Source Code; Structure; System; Time; Twin Studies; Update; Urbanization; Visualization; Work; adolescent substance use; base; cloud based; cognitive development; cohort; computerized tools; cost; data access; data exchange; drinking; drinking behavior; early life stress; epidemiologic data; gene environment interaction; genomic data; human subject; imaging genetics; insight; neuroimaging; open data; open source; peer; prevent; psychiatric symptom; relating to nervous system; rural area; sex; statistics; substance use; tool; tool development; urban area; virtual

Project Summary/Abstract Substance use behavior (smoking/drinking) is well known to be the result of a complex interplay between genetics and environmental components. However macro- and micro-environmental effects on smoking and drinking behavior are still poorly understood and there is little work evaluating the relationship of such factors to neurobiological changes measured via brain imaging. The data are available though. There is a great amount of internationally collected imaging and genomics data, however much of this data is not being analyzed due to privacy issues or other factors which prevent sharing of the raw data. To address this barrier, we propose to leverage and extend a software platform which enables decentralized analysis of data (e.g. sharing without sharing). We will use this platform to perform an analysis which pools together data from the 10,000 participant longitudinal US-based adolescent brain cognitive development (ABCD) study with an international cohort of more than 20,000 participants from Europe, China, and India called the global imaging genetics of adolescents (GIGA). Our focus will be on assessing macro- and micro-environmental and heritability factors associated with sub- stance use behavior as well as their neuronal biomarkers in the context of factors such as cultural acceptance, urbanization, annual household income, and climate. Results will provide new insights into the factors contrib- uting to substance use behavior. We also propose to develop a persistent decentralized analysis nodes for the GIGA sites using our Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC) to allow for re-analyses of these large cohorts as well as pooling with one’s local data without requiring collocation of the data at the same site. 3

项目总结/摘要 众所周知，物质使用行为（吸烟/饮酒）是以下因素之间复杂相互作用的结果： 遗传学和环境因素。然而，宏观和微观环境对吸烟的影响， 饮酒行为仍然知之甚少，很少有工作评估这些因素与 通过脑成像测量的神经生物学变化。但数据是可用的。有大量的 国际上收集的成像和基因组学数据，但由于 隐私问题或其他阻止共享原始数据的因素。为了克服这一障碍，我们建议 利用和扩展软件平台，使数据的分散分析成为可能（例如， 共享）。我们将使用这个平台进行分析，将来自10，000名参与者的数据汇总在一起 一项基于美国的青少年大脑认知发展（ABCD）纵向研究， 来自欧洲、中国和印度的20，000多名参与者称为全球青少年遗传学成像（GIGA）。 我们的重点将是评估宏观和微观环境和遗传因素与亚 立场使用行为以及他们的神经元生物标志物的背景下，如文化接受， 城市化、家庭年收入和气候。结果将提供新的见解的因素贡献- 与药物使用行为有关。我们还建议开发一个持久的分散的分析节点， GIGA网站使用我们的协作信息学和神经成像套件工具包匿名计算 （COCONUAC），以便重新分析这些大型队列，并与当地数据合并， 需要在同一站点上配置数据。 3