Mass Multivariate Derivation and Validation of AUD Biotypes using Developmental Imaging and Genomic Approaches

项目总结/摘要 与酒精使用障碍的广泛流行和毁灭性后果形成鲜明对比， 目前可用的治疗选择仅是中等有效的。澳元呈现的巨大异质性 可能混淆病因和个体化治疗选择。在这5年K 01指导研究科学家 我的获奖申请，我建议描述行为，分子和遗传相关的AUD生物型 （由生物学决定的亚型）。为此，我将应用半监督机器学习 算法的结构脑成像数据从最大的可用澳元和酒精使用数据集， 儿童期至老年期（总计n= 61，428）。我将研究这些生物型在整个生命周期中的稳定性，包括 在未使用药物的儿童和重度酒精使用的成人中， 基于神经行为阶段的成瘾结构（即，冲动、负面情绪、认知）和酒精 参与轨迹。然后，我将对AUD生物型进行全基因组关联研究， 它们的遗传结构、遗传相关性和可能用于药物治疗的潜在分子途径 重新定位这项资助发展了我在半监督机器学习，AUD异构 演示文稿，多变量全基因组方法和多组学分析方法。这些技能将 作为计划中的R 01提交的背景，该提交将利用我在大规模数据分析方面的背景 在物质使用研究中跨生物模式转换。

PROJECT SUMMARY/ABSTRACT In stark contrast to the widespread prevalence and devastating outcomes associated with alcohol use disorder, currently available treatment options are only moderately effective. The large heterogeneity in AUD presentations may obfuscate etiology and individualized treatment options. In this 5-year K01 mentored research scientist award application, I propose to characterize the behavioral, molecular, and genetic correlates of AUD biotypes (subtypes determined by biology) across the lifespan. To this end, I will apply semi-supervised machine learning algorithms to structural brain imaging data from the largest available AUD and alcohol use datasets from childhood to old age (total n=61,428). I will examine the stability of these biotypes across the lifespan, including among substance-naïve children and adults with heavy alcohol use, as well as their correlates with neurobehavioral stage-based constructs of addiction (i.e., impulsivity, negative affect, cognition) and with alcohol involvement trajectories. I will then conduct a genome-wide association study of AUD biotypes to disarticulate their genetic architecture, genetic correlates, and potential molecular pathways that may be leveraged for drug repositioning. This grant develops my skillsets in semi-supervised machine learning, AUD heterogeneous presentations, multivariate genome-wide methods, and multi-omic analytic approaches. These skill sets will serve as a backdrop for a planned R01 submission that will leverage my background in large-scale data analysis to translate across biological modalities in substance use research.