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

物质使用和成瘾是复杂的生物心理社会障碍，受遗传因素的影响， 和环境因素。成瘾遗传学研究的一个关键挑战是了解如何 多种遗传变异通过影响中枢神经系统， 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 一个框架，以确定成瘾相关的基因-大脑回路通路，使用尼古丁成瘾作为 目标障碍，虽然该平台将很容易适用于其他成瘾相关的 疾病和表型。我们将充分利用复杂和相互依存的 图像-遗传学数据之间的关系，并执行多变量统计推断 同时增加了统计功效并降低了假阳性率。结果将 精确地识别多组遗传变异，这些变异相互作用地改变大脑功能， 结构电路，然后影响尼古丁成瘾。我们会进一步补充 使用深度学习算法来研究遗传变异如何非线性地 交互协调影响尼古丁成瘾和解释表型方差。 将开发基于卷积和池化功能的新型网络拓扑，以实现 使用基因组-连接体途径的成瘾特征的最佳预测准确性。所有型号 研究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，以确保我们的研究的可复制性和可靠性。 从这个框架得出的结论。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， 复杂的全基因组-连接体分析用于成瘾遗传学研究。

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.