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Functional genomic resource and integrative model of dopaminergic circuitry associated with psychiatric disease

与精神疾病相关的多巴胺能回路的功能基因组资源和整合模型

基本信息

批准号：
10360606
负责人：
Schahram Akbarian
金额：
$ 66.03万
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：
2019
资助国家：
美国
起止时间：
2019-05-01 至 2024-02-29
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10360606
关键词：
3-Dimensional ATAC-seq Adult Architecture Autopsy Bayesian Network Bipolar Disorder Brain Brain Stem Cell Culture Techniques Cell Nucleus Cerebral cortex ChIP-seq Chromatin Cognitive Collection Communities Complex DNA Data Data Set Depression and Suicide Diagnosis Dimensions Disease Drug Addiction Drug abuse Enhancers Etiology Forebrain Development Functional disorder Gene Expression Genes Genetic Genetic Diseases Genetic Risk Genetic Transcription Genetic Variation Genome Genomics Genotype Goals Greek Guide RNA Hi-C Histone Acetylation Human Individual Information Networks Maps Mediating Mental disorders Methods Midbrain structure Modeling Molecular Conformation Moods NR4A2 gene National Institute of Mental Health Neurobiology Neuroglia Neurons Nuclear RNA Organoids Pathology Pathway interactions Persons Phenotype Policies Prefrontal Cortex Prosencephalon Psychoses Public Health Regulation Research Resources Sampling Schizophrenia Source Subgroup Substance abuse problem Substantia nigra structure System Testing Tissues United States Validation Variant Ventral Tegmental Area Veterans Work base case control cell type clinical phenotype cohort comorbidity cost data sharing differential expression disease phenotype dopaminergic neuron epigenome epigenomics functional genomics gene network genetic risk factor genome sequencing induced pluripotent stem cell network models neuropsychiatry phenotypic data prediction algorithm predictive modeling promoter psychiatric genomics reconstruction recurrent depression three dimensional structure trait transcriptome transcriptome sequencing whole genome

项目摘要

PROJECT SUMMARY Great progress has been made in mapping the transcriptome and some its epigenomic determinants of the adult and developing human cerebral cortex (including alterations in common psychiatric disease) through the efforts of the PsychENCODE consortium. However, next to nothing, or very little, is known about genomic regulation in brainstem monoaminergic neurons and their ascending projections into the forebrain, a circuitry critically involved in the pathophysiology of mood and psychosis spectrum disorders and substance abuse disorders, among others. The goal of our project is to construct transcriptome and epigenome (incl. 3D genome/chromosomal conformation) maps for midbrain dopaminergic neurons and for their surrounding non- neuronal cells, and to assess the relationship to known genetic risk factors for complex mental illness, including psychosis with substance abuse co-morbidity. We will apply integrative methods for functional analysis of risk genetic variation and networks, including but not limited to Bayesian network reconstruction and prediction algorithms of variant causality to identify key drivers of schizophrenia and bipolar disease pathology, and drug addiction co-morbidity. These methods will simultaneously integrate multiple different dimensions of data: DNA variation, RNA expression, chromatin accessibility, 3D structure of the genome, known pathway and gene network information in the context of clinical phenotype data. The fundamental source of data for the project comes from the current studies on human midbrain functional omics, and the CommonMinds and PsychENCODE consortia (whole genome sequencing and cortical functional omics data), the Psychiatric Genomics Consortium and the Million Veterans Project (genetic variation and disease phenotypes). The Million Veterans Project (MVP) has collected genotyping and phenotypic data from ~700,000 individuals, including a subgroup of 50,000 veterans diagnosed with SCZ and BD and a larger group of individuals diagnosed with other neuropsychiatric traits (recurrent depression, suicide and substance abuse). We will make our newly generated transcriptome and epigenome datasets from adult midbrain, as well as the network and predictive models, available to the research community in accordance with NIMH data sharing policies.

项目总结