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: 9810163
• 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/9810163
• 关键词: Address; Alcohol or Other Drugs use; 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; 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.

物质使用和成瘾是一种复杂的生物-心理-社会障碍，受遗传因素的影响