Quantitative methods to subtype drug dependence and detect novel genetic variants

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改进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在临床和病因学上是异质性的。dsm定义的特征对于基因寻找工作来说不是最佳的，这极大地限制了我们对SD遗传病因的理解。因此，对药物使用、相关行为和共同发生的表型的同质亚型的分化可以提高对SD和其他复杂性状风险的遗传变异的识别。现有的方法不足以处理这一任务。可用的最复杂的亚型方法执行无监督聚类分析或疾病临床特征的潜在类分析。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什么用处的亚型。在本项目中，我们将开发新的统计方法来定量分型SD性状。利用从可卡因、阿片类药物和酒精依赖的基于家庭和病例对照的遗传研究（包括全基因组关联研究（GWAS））中汇总的bbb11000名相同评估受试者的数据，我们将确定在遗传力方面优化的临床亚型。所有受试者都使用包含3000个项目的多诊断工具进行了彻底的表型分析，产生了可靠的人口统计学、医学、物质使用和物质相关测量，并对所有主要物质使用和精神障碍进行了DSM诊断。大多数受试者还接受了GWAS。我们的初步结果支持了这样的假设，即对物质使用和相关行为进行仔细的分型可以提高对导致成瘾相关表型风险的遗传变异的检测，而这些遗传变异是使用标准诊断方法无法检测到的。本研究的主要目的是：(1)利用生物信息学方法推导传统狭义遗传力和新近定义的基于snp的遗传力具有高度遗传性的数量性状；(2)综合方法，联合分析表型特征和遗传标记，鉴定表型和遗传均属均匀的亚型；(3)对亚型分析更有效的遗传关联方法。衍生的亚型及其关联发现将使用多个独立样本进行验证。该项目的另一个重要目标是开发和传播经过验证的方法和软件，以供公众使用。总之，该项目的目标具有重要意义，因为他们有可能利用跨学科研究团队验证的新方法，加强发现导致SD风险的遗传变异。这些方法一旦应用于了解SD的病因，可能适用于扩展到其他复杂表型。

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.