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

描述（由申请人提供）：由于人类基因组表征技术的发展步伐加快，临床人群中大规模基因组研究的限速步骤现在是表型分析。对于神经精神（NP）疾病尤其如此，其中表型复杂，缺乏生物标志物，并且难以直接获得感兴趣的原代细胞类型。很明显，罕见和常见的遗传变异都有助于疾病风险，并且这种风险跨越了精神病学的传统诊断界限。利用一个已经建立的大型NP生物库，可以大大加快理解疾病易感基因的交叉紊乱作用机制的进展。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。在这样做的过程中，它将加速临床和细胞表型的研究，以了解单和多位点/多基因关联。目标1：通过解析电子健康记录，采用认知和其他RDoC表型的金标准评估，调整和扩展最大的NP细胞生物库之一。目标二：定义NP疾病的全基因组多维功能基因组学（MFG）景观，其中每个诱导神经元（IN）的转录组签名（RNA-seq）代表临床特征个体。投影提供了从分子到表型表征的映射，以及目标3中使用的健康/神经典型状态的方向性。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使IN转录组特征在MFG中向健康方向转移，然后基于MFG中测量的扰动更新模型。目标4：选择患者样本以更详细地研究表观遗传（DNA甲基化、组蛋白标记和RNA编辑）和转录控制，特别是关于与许多NP疾病有关的活性依赖性变化。目的5：在这里，我们评估全基因组特征对临床表型的影响程度，并评估哪一种更稳健。

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.