Project 4 - Human studies to identify genes and characterize risk pathways involved in alcohol related outcomes

项目 4 - 人体研究，以确定基因并描述与酒精相关结果相关的风险途径

• 批准号: 10633320
• 负责人: DANIELLE M DICK
• 金额: $ 18.54万
• 依托单位: VIRGINIA COMMONWEALTH UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-05 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10633320
• 关键词: Adolescence; Affect; Alcohol abuse; Alcohol consumption; Alcohol dependence; Alcohols; Animal Model; Area; Behavior; Behavioral; Bioinformatics; Biological; Complex; Consumption; Data; Data Set; Detection; Development; Diagnosis; Environment; Environmental Risk Factor; Epidemiology; Ethnic Origin; Foundations; Genes; Genetic; Genetic Risk; Genetic Techniques; Genomics; Goals; Health; Human; Individual; Intervention; Investigation; Maps; Mediating; Mental disorders; Meta-Analysis; Methods; Multivariate Analysis; Nature; Outcome; Pathway interactions; Phenotype; Prevention; Process; Psychiatry; Race; Risk; Rodent; Running; Sample Size; Sampling; Signal Transduction; Structural Models; Structure; Techniques; Testing; Translating; Twin Multiple Birth; Variant; addiction; alcohol abuse therapy; alcohol behavior; alcohol related problem; alcohol research; alcohol use disorder; behavioral phenotyping; clinical diagnosis; emerging adulthood; gene discovery; gene network; genetic architecture; genetic association; genetic variant; genome wide association study; genome-wide; high risk; human data; improved; longitudinal dataset; model organism; novel; personalized medicine; polygenic risk score; population based; sex; social; trait

Project Summary – Project 4 Project 4 of the VCU Alcohol Research Center will utilize human data to accomplish two complementary goals: (1) advancing discovery of genes involved in alcohol-related outcomes using new multivariate genomic techniques, and (2) characterizing the risk associated with identified variants in diverse longitudinal samples, in order to understand the spectrum of phenotypes associated with identified variants, across development, and in conjunction with the environment. Each of these areas represent critical steps in using genetic data to improve prevention, intervention, and treatment for alcohol use disorders (AUDs), and will lay the foundation as we move into an era of personalized medicine. Human gene identification efforts for alcohol use disorders lag behind other areas of psychiatry, in part due to constrained sample sizes of available AUD cases. However, recent meta-analyses of consumption and AUD reveal significant genetic correlations with numerous other psychiatric and behavioral traits, as well as social and demographic outcomes, and other biomedical phenotypes. Project 4 will (Aim 1) apply new multivariate genetic methods to capitalize on genetic sharing between alcohol use phenotypes and other psychiatric and behavioral traits in order to boost power to detect common variants associated with alcohol use outcomes, and to characterize the latent pathways by which genetic variants operate. Bioinformatic characterization of these identified genetic variant results through the Bioinformatics and Analytics (BIA) core will help elucidate underlying biological risk pathways. We will then apply results from these multivariate analyses to three complementary longitudinal datasets, consisting of both population-based and high-risk samples, in order to (Aim 2a): map the behavioral phenotypes associated with the genetic risk scores identified in Aim 1 across adolescence and emerging adulthood; (Aim 2b) test for pathways of risk specific to sex and racial/ethnic background; and (Aim 2c) test for moderation of genetic risk by key environmental factors. The project will interface with the other ARC components in multiple ways: results from the gene discovery analyses (Aim 1) will be integrated with model organism results and expression data (Projects 1-3) in the BAI Core to create refined polygenic risk scores for further study in Aim 2. Project 5 will refine structural models using twin and epidemiological samples to further characterize the multivariate nature of genetic influences on alcohol-related outcomes, to iteratively inform and extend the multivariate analyses performed in Aim 1. Further, the genetic variants identified in Aim 1 can be advanced for further study in animal models via the Rodent Behavior Core.

项目总结 – 项目 4 VCU 酒精研究中心的项目 4 将利用人类数据来实现两个互补的目标： (1) 使用新的多变量基因组推进与酒精相关结果相关的基因的发现 技术，以及（2）表征与不同纵向样本中已识别变异相关的风险， 为了了解与已识别的变异相关的表型谱，跨越发育，以及 这些领域中的每一个都代表了利用遗传数据来实现目标的关键步骤。 改善酒精使用障碍（AUD）的预防、干预和治疗，并将为以下方面奠定基础： 我们进入了个性化医疗时代。针对酒精使用障碍的人类基因识别工作滞后。 落后于精神病学的其他领域，部分原因是可用 AUD 病例的样本量有限。 最近对消费和澳元的荟萃分析揭示了与许多其他因素的显着遗传相关性 精神和行为特征，以及社会和人口统计结果，以及其他生物医学 项目 4 将（目标 1）应用新的多变量遗传方法来利用遗传共享。 酒精使用表型与其他精神和行为特征之间的关系，以提高检测能力 与饮酒结果相关的常见变异，并描述其潜在途径 遗传变异通过生物信息学表征这些已识别的遗传变异结果。 生物信息学和分析 (BIA) 核心将有助于阐明潜在的生物风险途径。 将这些多变量分析的结果应用于三个互补的纵向数据集，其中包括 基于人群的高风险样本，以便（目标 2a）：绘制与以下相关的行为表型 目标 1 中确定的青春期和成年初期（目标 2b）测试中确定的遗传风险评分； 特定于性别和种族/民族背景的风险途径；以及（目标 2c）遗传风险调节测试 该项目将以多种方式与其他 ARC 组件交互： 基因发现分析（目标 1）的结果将与模式生物结果整合， BAI 核心中的表达数据（项目 1-3）可创建精细的多基因风险评分，用于目标 2 中的进一步研究。 项目 5 将使用双胞胎和流行病学样本来完善结构模型，以进一步表征 遗传对酒精相关结果影响的多变量性质，迭代地告知和扩展 目标 1 中进行的多变量分析。此外，目标 1 中确定的遗传变异可以进一步用于 通过啮齿动物行为核心对动物模型进行进一步研究。