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.

项目摘要/摘要与与酒精使用障碍有关的宽度流行和毁灭性结果形成鲜明对比的是当前可用的治疗方案仅适度有效。 AUD演示文稿中的较大异质性可能会混淆病因和个性化的治疗选择。在这个五年的K01中，指导了研究科学家奖励应用，我建议表征AUD生物型的行为，分子和遗传相关性（由生物学确定的亚型）在整个生命周期中。为此，我将应用半监督的机器学习来自最大可用AUD和酒精使用数据集的结构性脑成像数据的算法童年至老年（总n = 61,428）。我将研究这些生物型在整个生命周期的稳定性，包括在没有大量酒精饮酒的未经物质的儿童和成年人中基于神经行为的基于阶段的成瘾结构（即冲动，负面影响，认知）和酒精参与轨迹。然后，我将对AUD生物型进行全基因组的关联研究，以否定它们的遗传结构，遗传相关性和可能被利用用于药物的潜在分子途径重新定位。该赠款在半监督机器学习中发展了我的技能，AUD异质演示，多元基因组方法和多摩变分析方法。这些技能将充当计划的R01提交的背景，该提交将在大规模数据分析中利用我的背景在物质使用研究的生物学方式上翻译。