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.

药物使用和成瘾是受两个遗传影响的复杂的生物心理社会疾病 和环境因素。成瘾遗传学研究的关键挑战是了解如何 多种遗传变异通过影响中心而交互影响成瘾性状 神经系统。为了应对这一挑战，我们提出了大规模调解分析 识别与成瘾相关的基因脑电路途径的框架，使用尼古丁成瘾为 靶向疾病，尽管该平台将很容易适用于其他与成瘾有关的 疾病和表型。我们将充分利用复杂和互动的相互依存 成像基因数据和执行多元统计推断之间的关系 随着同时增加统计功率并降低误报率。结果将 精确地识别多个遗传变异，这些变体可以互动地改变大脑功能和 结构电路，然后影响尼古丁成瘾。我们将进一步补充 带有深度学习算法的调解结果，以研究遗传变异的非线性以及 交互式协调以影响尼古丁成瘾并解释表型方差。 将开发基于网络拓扑的新型卷积和集合功能以实现 使用基因组 - 连接组途径的成瘾性状的最佳预测准确性。所有模型 发现将通过多个独立的大型数据集仔细验证 尼古丁成瘾的成像基因研究，以确保我们的可复制性和可靠性 从此框架得出的发现。我们计划生产免费的且用户友好的 结合中介分析框架和深度学习算法的软件 复杂的整体基因组 - 成瘾遗传学研究的连接组分析。