Quantitative methods to subtype drug dependence and detect novel genetic variants

定量方法对药物依赖性进行分型并检测新的遗传变异

• 批准号: 9186998
• 负责人: Jinbo Bi
• 金额: $ 21.75万
• 依托单位: UNIVERSITY OF CONNECTICUT STORRS
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-02-01 至 2018-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9186998
• 关键词: Alcohol dependence; Alcohol or Other Drugs use; Alcoholism; Area; Behavior; Bioinformatics; Categories; Clinical; Cluster Analysis; Cocaine Dependence; Communities; Complex; Computer software; Computers; DSM-IV; Data; Data Aggregation; Data Set; Derivation procedure; Detection; Diagnosis; Diagnostic; Diagnostic and Statistical Manual of Mental Disorders; Disease; Drug Addiction; Drug usage; Etiology; Evaluation; Exhibits; Family; Foundations; Genes; Genetic; Genetic Enhancement; Genetic Markers; Genetic Predisposition to Disease; Genetic Research; Genetic Variation; Genetic study; Genome; Genomic Segment; Genotype; Heritability; Heterogeneity; Interdisciplinary Study; Interview; Lead; Measures; Medical; Mental disorders; Methods; Modeling; Molecular Genetics; Nuclear Family; Opiate Addiction; Phenotype; Property; Recruitment Activity; Research; Risk; Role; Sampling; Single Nucleotide Polymorphism; Statistical Methods; Statistical Models; Structure; Substance Addiction; Techniques; Variant; addiction; base; blind; case control; cohort; disease phenotype; genetic analysis; genetic association; genetic pedigree; genetic variant; genome wide association study; genome-wide; improved; insight; instrument; novel; novel strategies; public health relevance; tool; trait; user-friendly; web site

DESCRIPTION (provided by applicant): Despite great progress in molecular genetic methods, considerably less progress has been made in the refinement of phenotypes for substance dependence (SD) and other psychiatric disorders. SD, as defined by the Diagnostic and Statistical Manual of Mental Disorders (DSM), is clinically and etiologically heterogeneous. The DSM-defined traits are not optimal for gene finding efforts, which has substantially limited our understanding of the genetic etiology of SD. Thus, the differentiation of homogeneous subtypes of drug use, related behaviors, and co-occurring phenotypes could improve the identification of genetic variation that underlies the risk for SD and other complex traits. Existing methods are not adequate to tackle this task. The most sophisticated subtyping methods available perform unsupervised cluster analysis or latent class analysis of a disorder's clinical features. Without theoretical guidance, blind cluster or latent class analysis can lead to subtypes of little utilityin genetic analysis. In this project, we will develop novel statistical methods to subtype SD traits quantitatively. Using data from >11,000 identically assessed subjects aggregated from family-based and case-control genetic studies (including genome-wide association studies (GWAS)) of cocaine, opioid and alcohol dependence, we will identify clinical subtypes that are optimized with respect to heritability. All subjects underwent thorough phenotyping using a poly-diagnostic instrument that includes 3000 items, yielding reliable demographic, medical, substance use, and substance-related measures, and DSM diagnoses of all major substance use and psychiatric disorders. A majority of the subjects also underwent GWAS. Our preliminary results support the hypothesis that careful subtyping of substance use and related behaviors enhances the detection of genetic variants that contribute to the risk of addiction-related phenotypes and are not detected using a standard diagnostic approach. The primary aims of the proposed research are to develop: (1) bioinformatics methods to derive quantitative traits that are highly heritable n terms of traditional narrow-sense heritability and recently-defined SNP-based heritability; (2) integrative methods to jointly analyze phenotypic features and genetic markers to identify subtypes that are homogeneous phenotypically and genetically; and (3) genetic association approaches that are more efficient for subtype analysis. The derived subtypes and their association findings will be validated using multiple independent samples. An important secondary aim of the project is to develop and disseminate validated methods and software for public use through the PI's website. In summary, the objectives of the project are significant in their potential to enhance the discovery of genetic variants that contribute to the risk of SD usin novel methods validated by the interdisciplinary research team. These methods, once applied to understanding the etiology of SD, may be suitable for extension to other complex phenotypes.

描述（由申请人提供）：尽管分子遗传学方法取得了长足的进步，但在对物质依赖（SD）和其他精神疾病的表型的改进中取得了较少的进步。 SD在临床和病因上是精神障碍（DSM）的诊断和统计手册所定义的。 DSM定义的特征对于寻找基因的努力并不是最佳选择，这在很大程度上限制了我们对SD遗传病因的理解。因此，药物使用，相关行为和同时存在的表型的均质亚型的分化可以改善遗传变异的鉴定，这是SD和其他复杂性状的风险。现有方法不足以应对这项任务。可用的最复杂的亚型方法对疾病的临床特征进行无监督的聚类分析或潜在类别分析。如果没有理论指导，盲簇或潜在类别分析可以导致几乎没有效用的遗传分析的亚型。在这个项目中，我们将开发新颖的统计方法，以定量为亚型SD特征。使用来自可卡因，阿片类药物和酒精依赖性的11,000名相同评估的相同评估的受试者的数据（包括全基因组和酒精依赖性基因组关联研究（GWAS）），我们将确定针对遗传性优化的临床亚型。所有受试者都使用包括3000个项目的多诊断仪器进行了彻底的表型，从而产生可靠的人口统计学，医疗，药物使用和与物质相关的措施，以及所有主要药物使用和精神疾病的DSM诊断。大多数受试者也接受了GWAS。我们的初步结果支持以下假设：对物质使用和相关行为的仔细亚型增强了有助于成瘾相关表型风险的遗传变异的检测，并且未使用标准诊断方法检测到。拟议研究的主要目的是发展：（1）得出定量性状的生物信息学方法，这些方法是传统窄义遗传力和最近定义的基于SNP的遗传力的高度遗传性n术语； （2）共同分析表型特征和遗传标记的整合方法，以鉴定表型和遗传上均匀的亚型； （3）对亚型分析更有效的遗传关联方法。派生的亚型及其关联发现将使用多个独立样本进行验证。该项目的重要次要目的是开发和传播经过验证的方法和软件，以通过PI的网站公开使用。总而言之，该项目的目标对于增强发现遗传变异的潜力至关重要，这些变异有助于跨学科研究团队验证的SD USIN新颖方法的风险。这些方法曾经应用于理解SD的病因，可能适合扩展到其他复杂表型。