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: 8818537
• 负责人: Jinbo Bi
• 金额: $ 28.8万
• 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/8818537
• 关键词: Accounting; 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 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很大进展，但在物质依赖（SD）和其他精神疾病的表型细化方面取得的进展相当少。根据《精神疾病诊断与统计手册》（DSM）的定义，SD在临床和病因学上具有异质性。DSM定义的性状对于基因发现工作来说不是最佳的，这大大限制了我们对SD遗传病因的理解。因此，药物使用、相关行为和共现表型的同质亚型的分化可以提高对作为SD和其他复杂性状风险基础的遗传变异的鉴定。现有的方法不足以解决这一任务。最复杂的亚型分型方法可以对疾病的临床特征进行无监督聚类分析或潜在类别分析。在没有理论指导的情况下，盲目的聚类或潜在类分析可能导致在遗传分析中没有多大用处的亚型。在这个项目中，我们将开发新的统计方法来定量分型SD性状。使用来自可卡因，阿片类药物和酒精依赖的基于家族和病例对照遗传研究（包括全基因组关联研究（GWAS））的> 11，000名相同评估的受试者的数据，我们将确定在遗传性方面优化的临床亚型。所有受试者进行了彻底的表型使用多诊断工具，包括3000个项目，产生可靠的人口统计学，医疗，物质使用，物质相关的措施，和DSM诊断的所有主要物质使用和精神疾病。大多数受试者还接受了GWAS。我们的初步结果支持这一假设，即仔细分型的物质使用和相关行为，提高了遗传变异的检测，有助于成瘾相关表型的风险，并没有检测到使用标准的诊断方法。本研究的主要目的是：（1）利用生物信息学方法，从传统的狭义遗传力和最近定义的基于SNP的遗传力中推导出高度可遗传的数量性状;（2）综合分析表型特征和遗传标记的方法，以鉴定表型和遗传上同质的亚型;和（3）对于亚型分析更有效的遗传关联方法。将使用多个独立样本验证推导的亚型及其相关性结果。该项目的一个重要的次要目标是开发和传播经验证的方法和软件，通过PI的网站供公众使用。总之，该项目的目标是显着的潜力，以提高发现的遗传变异，有助于SD的风险使用新的方法验证的跨学科研究小组。这些方法，一旦应用于了解SD的病因，可能适合扩展到其他复杂的表型。