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

使用发育成像和基因组方法对 AUD 生物型进行大规模多变量推导和验证

基本信息

批准号：
10429020
负责人：
Alexander S Hatoum
金额：
$ 17.94万
依托单位：
WASHINGTON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-20 至 2027-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10429020
关键词：
Adolescent Adopted Adult Age Alcohol abuse Alcohol consumption American Architecture Base of the Brain Behavior Behavioral Behavioral Genetics Bioinformatics Biological Biological Assay Biology Brain Brain imaging Child Childhood Cognition Collaborations Data Data Analyses Data Set Derivation procedure Development Disease Equation Etiology Genes Genetic Genomic approach Genomics Goals Grant Heavy Drinking Heterogeneity Human Impulsivity Individual Lead Life Longevity Machine Learning Magnetic Resonance Imaging Mediating Mental Depression Mental disorders Mentors Meta-Analysis Methods Minority Modality Modeling Molecular Molecular Genetics Naltrexone Neurosciences Opioid Receptor Outcome PDE4B Pathway interactions Patients Pharmaceutical Preparations Pharmacological Treatment Pharmacology Phenotype Prevalence Prevention Psychopathology Relapse Research Research Scientist Award Risk Risk Factors Risk-Taking Schizophrenia Science Silicon Dioxide Symptoms Training Translating Treatment Effectiveness Validation Variant addiction alcohol availability alcohol involvement alcohol risk alcohol use disorder base behavioral phenotyping biobank biological heterogeneity biopsychosocial cognitive development connectome craving drinking behavior drug development drug repurposing executive function experience genetic architecture genome wide association study genome-wide genomic data genomic signature human old age (65+)imaging approach improved in silico individualized medicine insight large scale data longitudinal analysis machine learning algorithm multiple omics negative affect neurobehavioral neurogenetics neuroimaging novel person centered personalized medicine precision drugs psychologic psychosocial skills statistics substance use supervised learning therapy development training project trait

项目摘要

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.

项目概要/摘要与酒精使用障碍的广泛流行和破坏性后果形成鲜明对比，目前可用的治疗方案仅具有中等效果。AUD 表现存在很大的异质性。在这个为期 5 年的 K01 指导研究科学家中，可能会混淆病因学和个体化治疗方案。在奖项申请中，我建议描述 AUD 生物型的行为、分子和遗传相关性（由生物学决定的亚型）在整个生命周期中，我将应用半监督机器学习。算法从最大的可用澳元和酒精使用数据集中构建大脑成像数据我将检查这些生物型在整个生命周期中的稳定性，包括从童年到老年（总计 n=61,428）。在未接触过物质的儿童和酗酒的成人中，以及它们与基于神经行为阶段的成瘾（即冲动、负面情绪、认知）和酒精的构建然后我将对 AUD 生物型进行全基因组关联研究来阐明。它们的遗传结构、遗传相关性以及可用于药物的潜在分子途径这笔赠款培养了我在半监督机器学习、AUD 异构方面的技能。演示、多变量全基因组方法和多组学分析方法将。作为计划的 R01 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跨生物学模式转化物质使用研究。