Integrated Multiscale Networks in Schizophrenia

 描述(申请人提供)：我们正在提交“精神分裂症的综合多尺度网络”，以回应RFA-MH-16-300。精神分裂症(SCZ)是一种破坏性很大的神经精神疾病，具有相当高的发病率、死亡率以及个人和社会代价。遗传因素通过家族和双胞胎数据被强烈地牵连，最近通过全基因组关联研究和测序研究被直接牵连。我们项目的主要目标是开发和应用先进的集成方法来进行网络的计算和功能分析，包括但不限于贝叶斯网络重建和各种因果关系的预测算法，以确定SCZ病理的关键驱动因素，以便进行潜在的治疗干预。为了实现这一点，在目标1中，我们将通过应用一种新的自上而下和自下而上或假设驱动的概率因果网络方法来构建单组织和多组织概率因果网络，并结合先前的信息对网络的关键驱动因素、新的路径和来自CommonMind联盟的SCZ病理数据的新机制进行测序。在目标2中，我们将使用在目标1中导出的网络模型，以改进预测SCZ网络，该网络可用于识别在治疗筛查中有用的SCZ相关转录特征。最后，在目标3中，我们将 使用改进的RNA和细胞模型来验证网络模型、关键驱动因素并调查它们的表型效应。

 DESCRIPTION (provided by applicant): We are submitting "Integrated Multiscale Networks in Schizophrenia" in response to RFA-MH-16-300. Schizophrenia (SCZ) is a generally devastating neuropsychiatric illness with considerable morbidity, mortality, and personal and societal cost. Genetic factors have been strongly implicated via family and twin data, and more recently directly through genome-wide association studies (GWAS) and sequencing studies. The primary objective of our project is to develop and apply advanced integrative methods for computational and functional analysis of networks, including but not limited to Bayesian network reconstruction and prediction algorithms of variant causality to identify key drivers of SCZ pathology for potential therapeutic intervention. To achieve this in Aim 1 we will construct single tissue and multi- tissue probabilistic causal network by applying a novel top-down and bottom-up or hypothesis-driven probabilistic causal network approaches in RNA sequencing key drivers of networks, novel pathways, and new mechanisms in SCZ pathology data from the CommonMind consortium, incorporating prior information. In Aim 2 we will use network models derived in Aim 1 in order to improve the predictive SCZ networks that could be used to identify SCZ-relevant transcription-based features that can be useful in therapeutic screening. Finally, in Aim 3 we will use modified RNA and cellular models to validate the network models, key drivers and investigate their phenotype effects.