Using the Genetic Architecture of Substance Use Disorders to Advance Gene Identification and Understanding of Pathways of Risk

项目摘要 该项目有两个相辅相成的目标：(1)促进发现与 使用新的多变量基因组技术的物质使用障碍，以及(2)表征 与不同纵向样本中已识别的变异相关的风险，以便了解 与已识别的变异相关的表型谱，跨发育阶段，以及 与环境相结合。这些领域中的每一个都代表了使用基因 改善物质使用障碍(SUDS)预防、干预和治疗的数据， 并将为我们进入个性化医疗时代奠定基础。人类基因 物质使用障碍的鉴定工作落后于精神病学的其他领域，部分原因是 由于现有SUD案例的样本大小有限。然而，最近的荟萃分析 与物质相关的表型，如开始年龄、消费和问题，揭示 物质使用结果的显著遗传相关性，以及与许多其他 精神和行为特征。这些发现映射到双胞胎数据上，表明 物质使用障碍和其他外化特征之间的遗传相关性。这个项目 将(目标1)应用新的多变量遗传方法，以利用 物质使用表型和相关特征，以提高检测常见变异的能力 与物质使用结果相关联，并描述潜在的途径 基因变异起作用。这些已识别的基因变异的生物信息学特征将 洞察潜在的生物风险途径和表型特征 由此产生的多基因风险评分将帮助我们了解风险是如何在发育和 与环境相结合。我们将把多变量分析的结果应用于两个 补充性纵向数据集，由基于总体的高风险样本组成， 为了(目标2a)绘制与遗传风险分数相关的行为表型图 在目标1中确定的青春期和成年初期；(目标2b)测试 特定于性别和种族/民族背景的风险；以及(目标2c)遗传风险适度测试 受关键环境因素影响。总之，这些分析将促进我们对如何 基因变异会增加物质使用障碍的风险。

Project Summary This project has two complementary goals: (1) to advance discovery of genes involved in substance use disorders using new multivariate genomic techniques, and (2) to characterize 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 represents critical steps in using genetic data to improve prevention, intervention, and treatment for substance use disorders (SUDs), and will lay the foundation as we move into an era of personalized medicine. Human gene identification efforts for substance use disorders lag behind other areas of psychiatry, in part due to constrained sample sizes of available SUD cases. However, recent meta-analyses of substance-related phenotypes such as age of initiation, consumption and problems, reveal significant genetic correlations across substance use outcomes, as well as with numerous other psychiatric and behavioral traits. These findings map onto twin data demonstrating significant genetic correlations across substance use disorders and other externalizing traits. This project will (Aim 1) apply new multivariate genetic methods to capitalize on genetic sharing between substance use phenotypes and related traits in order to boost power to detect common variants associated with substance use outcomes, and to characterize the latent pathways by which genetic variants operate. Bioinformatic characterization of these identified genetic variants will provide insight into underlying biological risk pathways, and phenotypic characterization of resultant polygenic risk scores will help us understand how risk unfolds across development and in conjunction with the environment. We will apply results from the multivariate analyses to two complementary longitudinal datasets, consisting of a population-based and high-risk sample, 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. Jointly, these analyses will advance our understanding of how genetic variation contributes to risk for substance use disorders.