Complex clinical, neurobiological, and molecular signatures of the longitudinal course of psychosis: leveraging comprehensive phenotyping, novel machine learning, and (epi)genomic approaches

精神病学课程PC1项目的总体目标是确定主要精神疾病纵向过程的临床、神经生物学和分子遗传学特征。由于它是基于临床研究小组241(KFO 241)的成功工作，因此主要重点将放在精神分裂症(SZ)和双相情感障碍(BD)上。为了更好地解释精神病学表型之间的病因重叠，我们现在还将包括严重抑郁障碍(MDD)。在3年的时间里，我们将建立一个由1700名患者(700名SZ，700名BD，300名MDD)和500名对照组成的深层表型队列，对这些患者进行广泛的表型和生物库(血液、DNA、RNA、血浆、血清)至少4个时间点(0、6、12、18个月)。这一独特的资源是通过KFO 241在德国范围内的招聘基础设施实现的，因此将构成整个心理课程以人为中心的研究的核心资源。我们将把先进的机器学习方法应用于这些队列中的基线和纵向表型信息，以定义新的目标表型、过程和结果，以便进一步的分子和统计分析。我们将评估个体化诊断分类和结果预测的可行性。大量的数据将为这些新颖的分析提供信息，例如社会人口学、精神病理学、神经认知和基因组数据。我们项目的机器学习部分还将帮助定义处于紊乱过程(最差与非常有利过程)极端的患者。我们将通过全基因组测序来询问这两个组的完整基因组背景，以确定决定过程和结果的罕见变异的潜在参与。最后，利用尖端的microRNANome测序，我们建议建立调控元件作为SZ病程的新生物标记物。因此，就整体而言，我们的项目是心理课程中的核心项目之一，因为它为该联盟的临床研究奠定了基础，并描绘了新颖的、数据驱动的课程表型，这些表型被馈送到PC1和心理课程中的其他分子分析中。

The overall goal of the PC1 project of PsyCourse is the identification of clinical, neurobiological, and molecular genetic signatures of the longitudinal course of major psychiatric disorders. As it is based upon the successful work of the Clinical Research Group 241 (KFO 241), a major focus will lie on schizophrenia (SZ) and bipolar disorder (BD). To better account for the etiological overlap between psychiatric phenotypes, we will now also include major depressive disorder (MDD). Over the course of 3 years, we will establish a deep-phenotyped cohort of 1,700 patients (700 SZ, 700 BD, 300 MDD) and 500 controls, for whom extensive phenotyping and biobanking (blood, DNA, RNA, plasma, serum) at at least 4 time points (0,6,12,18 months) will be available. This unique resource that has been made possible through KFO 241`s German-wide recruitment infrastructure will thus constitute the core resource for human-centered research across PsyCourse. We will apply advanced machine learning methods to the baseline and longitudinal phenotypic information in these cohorts to define novel target phenotypes of course and outcome for further molecular and statistical analyses. We will assess the feasibility of an individualized diagnostic classification and outcome prediction. A vast array of data will inform these novel analyses, such as socio-demographic, psychopathological, neurocognitive and genomic data. The machine-learning part of our project will also help define patients at the extreme ends of the course of disorder (worst vs. very favorable course). We will interrogate the complete genomic background of these two groups by whole-genomic sequencing to establish a potential involvement of rare variants determining course and outcome. Finally, using cutting-edge microRNAnome sequencing, we propose to establish regulatory elements as novel biomarkers for course in SZ. Thus, in its entirety, our project is one of the core projects within PsyCourse as it lays the foundation for the clinical research of the consortium and as it delineates novel, data-driven course phenotypes feeding into molecular analyses within PC1 and others within PsyCourse.