Genetics of Severe Mental Illness

项目摘要/摘要 这项拟议的项目旨在利用遗传学来帮助开发一种对严重精神疾病进行分类的方法。 (SMI)比目前用于研究和临床的系统具有更强的科学基础 练习一下。一个多世纪以来，这些分类系统一直将SMI的大部分分为两类 诊断类别：精神障碍(包括精神分裂症[SCZ])和情绪障碍(包括 双相情感障碍[BP]和严重抑郁障碍[MDD])。然而，症状学的重叠 情绪和精神障碍，以及越来越多的证据表明这些类别之间的遗传相关性， 证明它们不能准确地代表SMI的生物学基础。有人提议， 基于症状级别和维度(量化)信息的框架，例如NIMH Research 域标准(RDoC)将更好地反映遗传对SMI的贡献，因此将提供 为他们的分类提供了更有用的框架。然而，支持这一假设的证据仍然存在。 稀疏，在很大程度上是因为我们缺乏正确的数据集来测试它。 在这个项目中，我们将生成一个独特的SMI数据集，使用电子健康记录来确定个人 他们在一家精神病院接受过住院治疗，这家精神病院为100万人提供服务 哥伦比亚卡尔达斯州的居民。我们将调查的所有个人都是 “Paisa”，一个基因和文化上相同的群体，构成了这个地区的大多数 哥伦比亚。通过招募8,000名参与者，涵盖所有严重的情绪和精神障碍(AS 以及2,000个人口统计学上匹配的对照)；对这10,000个个体进行统一的表型分析 使用诊断和定量评估；以及全基因组基因分型，我们将建立维度 索引SMI核心赤字的表型以及引用多个RDoC域的表型。然后我们将进行 症状水平和数量表型的遗传分析，评估它们与已知SMI基因座的关系 以及代表共同遗传变异对这些疾病的总体贡献的多基因风险评分(PR) 精神疾病；精神病学基因组联合会(PGC)的SCZ、BP和MDD工作组将为我们提供 每次诊断都有最新的基因数据。此外，我们还将进行全基因组联合 数量性状分析，包括已在其他研究中评估的性状的荟萃分析 人口。我们还将贡献我们的数据(包括我们可以获得的额外6,000个Paisa的基因类型 对照)对PGC工作组的病例对照元分析，有助于其多样性 通过添加来自以前代表不足的(西班牙裔)人口的大量样本来收集数据。 。

PROJECT SUMMARY/ABSTRACT This proposed project aims to use genetics to help develop an approach for classifying severe mental illness (SMI) that has a stronger scientific foundation than the systems currently used in both research and clinical practice. These classification systems have, for more than a century, divided the bulk of SMI into dichotomous diagnostic categories: psychotic disorders (including schizophrenia [SCZ]) and mood disorders (including bipolar disorder [BP] and major depressive disorder [MDD]). However the overlap of symptomatology across mood and psychotic disorders, and growing evidence for the genetic correlation between these categories, demonstrate that they imprecisely represent the biological underpinning of SMI. It has been proposed that frameworks based on symptom-level and dimensional (quantitative) information, such as the NIMH Research Domain Criteria (RDoC), would better reflect the genetic contribution to SMI and would therefore provide a more useful framework for their classification. However the evidence supporting this hypothesis remains sparse, in large part because we lack the right datasets to test it. In this project we will generate a unique SMI dataset, using electronic health records to ascertain individuals who have received inpatient treatment at a single psychiatric hospital that serves the entire 1 million inhabitants of the state of Caldas, Colombia. All of the individuals whom we will investigate are members of the “Paisa”, a genetically and culturally homogeneous population that comprises the majority in this region of Colombia. By recruiting 8,000 participants across the full range of severe mood and psychotic disorders (as well as 2,000 demographically-matched controls); performing uniform phenotyping of these 10,000 individuals using diagnostic and quantitative assessments; and genome wide genotyping, we will establish dimensional phenotypes that index core deficits of SMI and that reference multiple RDoC domains. We will then conduct genetic analyses of symptom-level and quantitative phenotypes, evaluating their relationship to known SMI loci and to polygenic risk scores (PRS) that represent the overall contribution of common genetic variation to these disorders; the SCZ, BP, and MDD workgroups of the Psychiatric Genomics Consortium (PGC) will provide us with up-to-date genetic data for each diagnosis. Additionally, we will conduct genome wide association analyses of the quantitative traits, including meta-analyses for traits that have been assessed in other study populations. We will also contribute our data (including genotypes available to us for an additional 6,000 Paisa controls) to the case-control meta-analyses of the PGC workgroups, contributing to the diversity of their datasets by adding a substantial number of samples from a previously underrepresented (Hispanic) population. .