Predicting AUD development, risk and resilience phenotypes through integration of multi-modal COGA data

项目摘要 酒精使用障碍(AUD)是美国和世界面临的重大公共卫生挑战。在全国调查中 关于药物使用与健康(2018年)，1,440万18岁及以上的成年人患有AUD。其中包括920万名男性 以及530万女性。此外，在2014年，因酒后驾车死亡的人数为9967人。 美国。尽管它很重要，但还没有做太多的研究来确定易感的生物 可能导致澳元发展的因素。虽然预测模型在以下方面取得了成功 区分患有AUD和健康对照的个体，模型提前识别个体 容易发生AUD，以及指示AUD易感性的生物标志物仍不清楚。至 为了解决这一问题，欧洲裔美国人(EA)和美国人酒精中毒遗传学(COGA)合作研究 非裔美国人(AA)招募了8-68岁的受试者，对他们进行纵向跟踪和评估 30多年了。受试者还被评估了电生理(EEG)、单核苷酸 多态(SNP)、心理社会和精神病学评估以及人口统计学调查问卷。的目标是 我们建议的研究是对COGA丰富的多模式纵向数据进行二次分析，以开发 预测模型，可以在个人实际患上AUD之前准确预测AUD的易感性 无序。机器学习(ML)方法特别有希望解决这个问题。在过去的几年里 十年来，ML方法应用于复杂生物医学数据的性能通常优于经典回归 方法，这表明对遗传、生物和心理社会数据的多维建模可能是最好的 反映AUD的潜在病理生理学。因此，在这个项目中，我们将利用创新的ML方法， 特别是基于深度学习和集成学习的方法，以及丰富的COGA数据来开发多模式 对这种疾病易感性的预测模型。此外，大多数AUD预测建模 在EA人群中进行了研究，需要在代表性不足的群体中进行更多的研究， 包括再生障碍性贫血和女性，让精准医疗的好处可以惠及所有人群。因此，我们 我们将在按年龄、性别和血统(AA、EA)分层的小组中进行预测建模分析。我们 还将在独立的验证集中严格评估开发的预测模型，分层 以同样的标准。最后，我们将对模型采用系统的解释策略来识别脑电， 遗传(SNP，多基因风险评分)、心理社会、精神病学和人口统计学特征的贡献最大 强烈到准确的澳元预测。在本文件中概述的面向二次分析的工作结束时 建议，我们希望已经确定了一个准确的、可推广的脆弱性多模式预测模型 到澳元，以及与这一严重疾病相关的已识别特征。我们的工作很可能会 有助于更深入地了解这一重大公共卫生挑战及其个性化 诊断和治疗。

Project Summary Alcohol use disorder (AUD) is a major public health challenge in the USA and the world. In the National Survey on Drug Use and Health (2018), 14.4 million adults aged 18 and older had AUD. This included 9.2 million men and 5.3 million women. Furthermore, in 2014, alcohol-impaired driving fatalities accounted for 9,967 deaths in the USA. Despite its importance, not much research has been done to identify the predisposing biological factors that may lead to the development of AUD. While predictive models have been successful in distinguishing between individuals with AUD and healthy controls, models identifying in advance if an individual is prone to develop AUD, as well as the biomarkers indicating a predisposition for AUD, are still unclear. To address this, the Collaborative Study of the Genetics of Alcoholism (COGA) of European American (EA) and African American (AA) has recruited subjects aged 8-68, who are longitudinally followed and evaluated for AUD over 30 years. The subjects were also assessed in terms of electrophysiology (EEG), single-nucleotide polymorphisms (SNP), psychosocial and psychiatry evaluation and demographic questionnaires. The goal of our proposed study is to conduct secondary analyses of COGA’s rich multimodal longitudinal data to develop predictive models that can accurately predict vulnerability to AUD before an individual actually develops the disorder. Machine learning (ML) methods hold particular promise to address this problem. Over the last decade, ML methods applied to complex biomedical data have generally outperformed classical regression approaches, suggesting that multi-dimensional modeling of genetic, biological and psychosocial data may best reflect the underlying pathophysiology of AUD. Thus, in this project, we will leverage innovative ML methods, especially those based on deep and ensemble learning, and the rich COGA data to develop multi-modal predictive models of vulnerability to the disorder. Furthermore, the majority of the AUD predictive modeling work has been conducted in EA populations, necessitating increased studies among underrepresented groups, including AA and females, so that the benefits of precision medicine can reach all populations. Therefore, we will conduct our predictive modeling analyses in subgroups stratified by age, sex, and ancestry (AA, EA). We will also rigorously evaluate the developed predictive models in an independent validation set, stratified based on the same criteria. Finally, we will employ systematic interpretation strategies for the models to identify EEG, genetic (SNP, polygenic risk scores), psychosocial, psychiatric and demographic features that contribute most strongly to accurate AUD prediction. At the conclusion of the secondary analysis-oriented work outlined in this proposal, we expect to have identified an accurate, generalizable multi-modal predictive model of vulnerability to AUD, as well as identified features that are associated with this serious disorder. Our work is likely to contribute to a deeper understanding of this major public health challenge, as well as its personalized diagnosis and treatment.