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：
9924477
负责人：
Schahram Akbarian
金额：
$ 72.22万
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/9924477
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 Histone Acetylation Human Individual Information Networks Maps Mediating Mental disorders Methods Midbrain structure Modeling Molecular Conformation Moods National Institute of Mental Health Neurobiology Neuroglia Neurons Nuclear RNA Organoids Pathology Pathway interactions Phenotype Policies Prefrontal Cortex Prosencephalon Psychotic Disorders 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 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.

项目摘要在映射转录组及其表观基因组决定因素方面取得了巨大进展通过心理代码联盟的努力。但是，几乎一无所有，或者很少有基因组知道脑干单胺能神经元的调节及其上升的预测到前脑，电路批判性地参与情绪和精神病谱系障碍和滥用药物的病理生理学疾病等。我们项目的目的是构建转录组和表观基因组（包括3D 中脑多巴胺能神经元的基因组/染色体构象）及其周围的非 - 神经元细胞，并评估与复杂精神疾病的已知遗传危险因素的关系，包括与药物滥用的精神病合并症。我们将应用综合方法进行风险的功能分析遗传变异和网络，包括但不限于贝叶斯网络重建和预测变异因果关系算法，以鉴定精神分裂症和双相病理病理的关键驱动因素和药物成瘾合并症。这些方法将同时整合数据的多个不同维度：DNA 变异，RNA表达，染色质可及性，基因组的3D结构，已知途径和基因临床表型数据背景下的网络信息。项目的基本数据来源来自目前关于人类中脑功能上的研究的研究 Psychencode Consortia（整个基因组测序和皮质功能OMICS数据），精神病学基因组学联盟和百万退伍军人项目（遗传变异和疾病表型）。百万退伍军人项目（MVP）已收集了约70万个人的基因分型和表型数据，包括 50,000名退伍军人的亚组，被诊断为SCZ和BD，并诊断出一组较大的个体其他神经精神特征（复发性抑郁症，自杀和滥用毒品）。我们将成为新的来自成人中脑以及网络和预测的生成转录组和表观基因组数据集模型，根据NIMH数据共享政策可用于研究社区。