Neuropsychiatric Genome-Scale and RDOC Individualized Domains (N-GRID)

神经精神基因组规模和 RDOC 个体化域 (N-GRID)

• 批准号: 8698507
• 负责人: ISAAC S. KOHANE
• 金额: $ 337.53万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-19 至 2019-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8698507
• 关键词: Agonist; Alcohol or Other Drugs use; Anxiety Disorders; Biological; Biological Markers; Categories; Cells; Cellular Neurobiology; Clinical; Clinical Trials; Code; Cognition; Cognitive; Complex; Computer Simulation; Consent Forms; DNA Methylation; DSM-IV; Dependency; Diagnosis; Diagnostic; Dimensions; Disease; Electronic Health Record; Emerging Technologies; Enrollment; Epigenetic Process; Equipment and supply inventories; Fibroblasts; Fruit; Functional RNA; Gene Expression; Gene Expression Profile; Genes; Genetic Transcription; Genetic Variation; Genome; Genomics; Goals; Gold; Growth; Healthcare Systems; Histones; Human Genome; Human Genome Project; Individual; Informatics; Investigation; Laboratories; Maps; Measurement; Measures; Methodology; Modeling; Molecular; Moods; National Institute of Mental Health; Natural Language Processing; Neurons; Pathway interactions; Patients; Pharmaceutical Preparations; Phase; Phenotype; Population; Principal Investigator; Prosencephalon; Psychiatry; Public Domains; RNA; RNA Editing; RNA Splicing; Resources; Risk; Running; Sampling; Scheme; Small RNA; Statistical Models; Stress; Symptoms; Testing; Transcript; Transcriptional Regulation; Update; Visit; Vocabulary; base; biobank; cell type; clinical phenotype; clinically relevant; design; disorder risk; functional genomics; genome sequencing; genome-wide; high risk; induced pluripotent stem cell; insight; interest; neuropsychiatry; novel; outcome forecast; prognostic; public health relevance; relating to nervous system; research study; response; technology development; transcriptome sequencing; transcriptomics

DESCRIPTION (provided by applicant): As a result of the accelerated pace of development of technologies for characterizing the human genome, the rate-limiting step for large scale genomic investigation in clinical populations is now phenotyping. This is particularly the case for neuropsychiatric (NP) illness, where phenotypes are complex, biomarkers are lacking, and the primary cell types of interest are difficult to access directly. It has become apparent that both rare and common genetic variation contributes to disease risk and that this risk crosses traditional diagnostic boundaries in psychiatry. Taking advantage of a large, already-established NP biobank could dramatically accelerate progress toward understanding the cross-disorder mechanism of action of disease liability genes. This study proposes novel applications of emerging technologies in informatics and cellular neurobiology to eliminate this phenotyping bottleneck. In doing so, it will accelerate investigation of clinical and cellular phenotypes for understanding single and multilocus/polygenic associations. Aim 1: Adapt and expand one of the largest NP cellular biobanks by parsing electronic health records with gold-standard assessment of cognition and other RDoC phenotypes. Aim 2: Define the genome-wide multidimensional functional genomics (MFG) landscape in NP disease into which the transcriptomic signature (RNA-seq) of each induced neuron (IN) representing a clinically characterized individual is projected. The projection provides the mapping from molecular to phenotypic characterization and a directionality towards healthful/neurotypical states used in Aim 3. Aim 3: Develop a probabilistic model of gene expression dependencies that will predict which small molecular perturbations are likely to shift the IN transcriptomic signature in a healthful direction in the MFG and to then update the model based on measured perturbations in the MFG. Aim 4: Select patient samples to study in greater detail for epigenetic (DNA methylation, histone marks and RNA editing) and transcriptional control particularly with regard to activity dependent changes that have been implicated in many NP diseases. Aim 5: Here we assess just how well the clinical phenotypes are informed by the genome-wide characterizations and assess which is more robust.

描述（由申请人提供）：由于技术来表征人类基因组的加速发展，临床人群中大规模基因组研究的速率限制步骤现在正在表型。尤其是 神经精神病（NP）疾病，表型很复杂，缺乏生物标志物，并且很难直接获得感兴趣的主要细胞类型。显然，罕见和常见的遗传变异都会导致疾病风险，并且这种风险跨越了精神病学的传统诊断界。利用大型，已经建立的NP生物库可以极大地加速进步，以理解疾病责任基因作用的跨界机理。这项研究提出了新兴技术在信息学和细胞神经生物学中的新应用，以消除这种表型瓶颈。在此过程中，它将加速研究临床和细胞表型，以了解单一和多焦点/多基因关联。 AIM 1：通过对认知和其他RDOC表型的金标准评估来解析电子健康记录，适应和扩展最大的NP细胞生物库之一。目标2：定义NP疾病中全基因组多维功能基因组学（MFG）景观，该疾病的每个诱导神经元（in）的转录组特征（RNA-SEQ）预测为临床表征的个体。该投影提供了从分子到表型表征的映射以及针对目标3中使用的健康/神经型状态的方向性。目标3：开发基因表达依赖性的概率模型，该模型将预测哪些小分子扰动可能会在MFG和MFG中更新MFG中的健康方向转移到perturs中的较小的转录方向。 AIM 4：选择患者样本以更详细地研究表观遗传学（DNA甲基化，组蛋白标记和RNA编辑）和转录控制，尤其是在活动依赖性变化方面，这与许多NP疾病有关。目的5：在这里我们评估了临床表型通过全基因组表征的信息，并评估哪个更健壮。