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Integrated Multiscale Networks in Schizophrenia

精神分裂症的综合多尺度网络

基本信息

批准号：
9101498
负责人：
ERIC E SCHADT
金额：
$ 48.81万
依托单位：
ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-05-01 至 2021-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9101498
关键词：
Area Binding Proteins Biological Biology Bipolar Disorder Brain region Candidate Disease Gene Cell Culture Techniques Cell model Cells Cerebral cortex Chemistry Collaborations Computer Simulation Computing Methodologies DNA Data Data Set Data Sources Development Dimensions Disease Epigenetic Process Etiology FDA approved Family Functional disorder Future Genes Genetic Genetic Transcription Goals Human Human Genetics Information Networks Knowledge Learning Link Maps Medicine Mental disorders Methods Modeling Molecular Morbidity - disease rate Neuroglia Neurons Outcome Pathogenesis Pathology Pathway Analysis Pathway interactions Pharmaceutical Preparations Phenotype Preclinical Drug Evaluation Proteins RNA RNA Editing Recording of previous events Regulatory Element Research Research Personnel Sampling Schizophrenia Scientist Structure System Therapeutic Therapeutic Intervention Tissues Twin Multiple Birth United States Validation Variant base clinical phenotype cohort computer based statistical methods cost disorder control drug use screening epigenome flexibility genome sequencing genome wide association study genomic data improved induced pluripotent stem cell learning network mortality network models neuroepithelium neuropsychiatry novel prediction algorithm protein protein interaction public health relevance reconstruction response screening small molecule stem cell biology transcriptome transcriptome sequencing validation studies whole genome

项目摘要

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.