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

项目摘要/摘要 众所周知，物质使用行为(吸烟/饮酒)是下列因素相互作用的结果 遗传学和环境成分。然而，宏观和微观环境对吸烟和 人们对饮酒行为的了解仍然很少，而且很少有工作评估这些因素与 通过脑成像测量神经生物学变化。不过，这些数据是可用的。有大量的 国际收集的成像和基因组学数据，然而，由于以下原因，这些数据中的许多没有被分析 隐私问题或其他阻止共享原始数据的因素。为解决这一障碍，我们建议 利用并扩展可实现数据分散分析的软件平台(例如，无需 共享)。我们将使用此平台执行分析，将10,000名参与者的数据汇集在一起 美国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多名参与者被称为全球青少年成像遗传学(GIGA)。 我们的重点将是评估宏观和微观环境以及与次要环境相关的遗传性因素 在文化接受等因素的背景下，立场使用行为及其神经元生物标志物， 城市化、家庭年收入和气候。这些结果将为我们提供对影响人类健康的因素的新见解。 倾向于物质使用行为。我们还建议开发一个持久的去中心化的分析节点 使用我们用于匿名计算的协作信息学和神经成像套件工具包的千兆网站 (COINSTAC)允许对这些大型队列进行重新分析，并在没有 需要将数据配置在同一站点。 3.

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