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

• 批准号: 10688177
• 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
• 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/10688177
• 关键词: Adolescent; Adopted; Adult; Age; Alcohol abuse; Alcohol consumption; American; Architecture; Behavior; Behavioral; 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; Life; Longevity; Machine Learning; Magnetic Resonance Imaging; Mediating; Mental Depression; Mental disorders; Mentors; Meta-Analysis; Methods; Minority; Modality; Modeling; Molecular; Naltrexone; Neurosciences; Opioid Receptor; Outcome; PDE4B; Pathway interactions; Patients; Pharmaceutical Preparations; Pharmacological Treatment; Phenotype; Positioning Attribute; Prevalence; Prevention; Psychopathology; Relapse; Research; Research Scientist Award; Risk; Risk Factors; Risk Taking; Schizophrenia; Science; Silicon Dioxide; Surgical Disarticulation; Symptoms; Training; Translating; Treatment Effectiveness; Validation; Variant; addiction; alcohol involvement; alcohol risk; alcohol use disorder; behavioral phenotyping; biobank; biological heterogeneity; biopsychosocial; biotypes; brain based; 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; pharmacologic; precision drugs; psychologic; psychosocial; skills; statistics; substance use; supervised learning; therapy development; trait

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。

项目成果

Is there a causal relationship between executive function and liability to mental health and substance use? A Mendelian randomization approach.

• DOI: 10.1098/rsos.220631
• 发表时间: 2022-12
• 期刊: ROYAL SOCIETY OPEN SCIENCE
• 影响因子: 3.5
• 作者: Burton, Sabrina M., I;Sallis, Hannah M.;Hatoum, Alexander S.;Munafo, Marcus R.;Reed, Zoe E.
• 通讯作者: Reed, Zoe E.

The Etiology of Executive Functioning Is Nature and Nurture.

执行功能的病因是先天和后天。

• DOI: 10.1016/j.biopsych.2023.05.009
• 发表时间: 2023
• 期刊: Biological psychiatry
• 影响因子: 10.6
• 作者: Hatoum,AlexanderS