A Multivariate Mediation and Deep Learning Framework for Genome-Connectome -Substance Use Research

基因组-连接组-药物使用研究的多元中介和深度学习框架

• 批准号: 10242826
• 负责人: Shuo Chen
• 金额: $ 46.35万
• 依托单位: UNIVERSITY OF MARYLAND BALTIMORE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10242826
• 关键词: Address; Brain; Complex; Data; Data Set; Disease; Economic Burden; Ensure; Environmental Risk Factor; Family; Genes; Genetic; Genetic Research; Genetic study; Genome; Individual; Lead; Mediation; Modeling; Neuraxis; Nicotine Dependence; Pathway interactions; Phenotype; Prevention; Research; Sampling; Structure; Substance Addiction; addiction; base; biopsychosocial; brain circuitry; connectome; deep learning; deep learning algorithm; effective therapy; genetic variant; health economics; imaging genetics; improved; nicotine use; novel; public health priorities; substance use; trait; user friendly software; whole genome

Substance use and addiction are complex biopsychosocial disorders influenced by both genetic and environmental factors. A key challenge in addiction genetics research is to understand how multiple genetic variants interactively influence addiction traits through impacting the central nervous system. To address this challenge, we propose a large-scale mediation analysis framework to identify addiction-related gene-brain circuitry pathways, using nicotine addiction as the targeted disorder, although the platform will be readily applicable for other addiction-related disorders and phenotypes. We will fully leverage the complex and interactive interdependent relationships between the imaging-genetics data and perform multivariate statistical inference with simultaneously increased statistical power and reduce false positive rates. The results will precisely identify multiple sets of genetic variants that interactively alter brain functional and structural circuitries, and then influence nicotine addiction. We will further supplement the mediation results with deep learning algorithms to study how genetic variants non-linearly and interactively coordinate to influence nicotine addiction and explain the phenotypic variance. Novel network topology based convolutional and pooling functions will be developed to achieve optimal prediction accuracy of addiction traits using genome-connectome pathways. All models and findings will be carefully validated through multiple independent large-sample data sets of imaging-genetics studies for nicotine addiction for ensuring the replicability and reliability of our findings derived from this framework. We plan to produce a freely available and user-friendly software incorporating the mediation analysis framework and deep learning algorithms enabling the complex whole genome - connectome analysis for addiction genetics research.

物质使用和成瘾是受基因影响的复杂的生物、心理和社会疾病 以及环境因素。成瘾遗传学研究的一个关键挑战是了解 多个遗传变异通过影响中枢神经系统来交互影响成瘾特征 神经系统。为了应对这一挑战，我们提出了一个大规模的调解分析 识别成瘾相关基因-大脑回路通路的框架，将尼古丁成瘾作为 尽管该平台很容易适用于其他与成瘾有关的疾病，但仍有针对性的障碍 疾病和表型。我们将充分利用复杂和互动的相互依存 影像遗传学数据与多变量统计推断之间的关系 同时增加了统计能力，降低了假阳性率。结果将会是 精确识别多组基因变异，这些变异可以交互改变大脑功能和 结构回路，然后影响尼古丁成瘾。我们将进一步补充 使用深度学习算法研究遗传变异如何非线性和 交互协调影响尼古丁成瘾并解释表型差异。 将开发基于卷积和池化功能的新型网络拓扑来实现 用基因组连接组途径预测成瘾特征的最佳准确度。所有模型 调查结果将通过多个独立的大样本数据集进行仔细验证 尼古丁成瘾的成像遗传学研究，以确保我们的可重复性和可靠性 从这一框架中得出的结论。我们计划制作一个免费提供和用户友好的 整合了中介分析框架和深度学习算法的软件，支持 用于成瘾遗传学研究的复杂全基因组连接组分析。