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巨大进展，但在物质依赖（SD）和其他精神疾病表型的细化方面取得的进展却相当有限。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寻找工作来说并不是最佳的，这极大地限制了我们对 SD 遗传病因学的理解。因此，区分吸毒的同质亚型、相关行为和同时发生的表型可以提高对导致 SD 和其他复杂性状风险的遗传变异的识别。现有方法不足以完成这项任务。最复杂的亚型分型方法可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，盲目聚类或潜在类别分析可能会导致亚型在遗传分析中几乎没有用处。在这个项目中，我们将开发新的统计方法来定量地对 SD 性状进行分类。利用来自可卡因、阿片类药物和酒精依赖的基于家庭和病例对照遗传学研究（包括全基因组关联研究 (GWAS)）汇总的超过 11,000 名经过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。所有受试者均使用包含 3000 个项目的多诊断仪器进行了彻底的表型分析，得出可靠的人口统计、医疗、物质使用和物质相关测量，以及所有主要物质使用和精神疾病的 DSM 诊断。大多数受试者还接受了 GWAS。我们的初步结果支持这样的假设：对物质使用和相关行为进行仔细的亚型分类可以增强对导致成瘾相关表型风险的遗传变异的检测，而使用标准诊断方法无法检测到这些遗传变异。本研究的主要目的是开发：（1）生物信息学方法来推导在传统狭义遗传力和最近定义的基于 SNP 的遗传力方面具有高度遗传性的数量性状； (2) 联合分析表型特征和遗传标记的综合方法，以确定表型和遗传同质的亚型； (3)对于亚型分析更有效的遗传关联方法。派生的亚型及其关联结果将使用多个独立样本进行验证。该项目的一个重要的次要目标是通过 PI 网站开发和传播经过验证的方法和软件以供公众使用。总之，该项目的目标是利用跨学科研究团队验证的新方法，增强发现导致 SD 风险的遗传变异的潜力。这些方法一旦应用于了解 SD 的病因学，可能适合扩展到其他复杂的表型。