pathQTL: Integrative Multi-Omics Causal Inference of Molecular Mechanisms Leading to Neuropsychiatric Illness

项目摘要 许多常见的遗传变异影响神经精神疾病的风险（例如，精神分裂症， 严重抑郁症和阿尔茨海默氏病）最近被确定和复制，提供了一个 这些疾病的原因。神经精神遗传学的关键下一步是从 基因组中的风险位点，以了解这种遗传变异如何影响分子，细胞， 导致复杂的紊乱许多数据集，包括我们自己实验室生成的数据集， 已经在多个生物学水平上建立了基因型和人脑特征之间的直接联系（分子： 染色质可及性，表达;细胞：形态学;回路：大体脑结构），称为数量性状 基因座（QTL）。在这里，我们将整合多个生物学水平的QTL，以统计优先级， 遗传变异造成复杂神经精神疾病风险的因果途径。因果 建模远远超出了以前的共同本地化工作，因为它允许优先考虑昂贵的功能， 验证实验的细胞或分子的变化是一个原因的障碍，而不是那些， 是疾病的后果或独立于疾病。它还允许推断关键实验 参数包括细胞类型和发育时间段。最后，因果推理，当结合在一起， 多个生物学水平和多个疾病风险基因座允许评估生物学水平上的会聚， 水平、细胞类型或发育时间段，这是治疗靶向的关键信息。我们将 利用贝叶斯概率网络和因果推理的计算和统计框架 在一个利用关联汇总统计的新框架中， 同一个人几乎总是不可行的。随后，我们将通过实验验证分子 预测我们的模型使用表观遗传工程在原代人类神经祖细胞，反过来， 修改计算模型。优先考虑疾病相关变体的因果分子途径，以及 识别相关的细胞类型和发育阶段将提高验证的成功率 实验和阐明神经精神疾病的机制，在公正的方式。

Project Summary A multitude of common genetic variants influencing risk for neuropsychiatric disorders (e.g., schizophrenia, major depressive disorder, and Alzheimer’s disease) have recently been identified and replicated, providing a foothold into the causes of these disorders. The critical next step in neuropsychiatric genetics is to move from a risk locus in the genome to an understanding of how this genetic variation influences molecules, cells, and circuits of the brain, leading to complex disorders. Many datasets, including those generated by our own labs, have established direct links between genotype and human brain traits at multiple levels of biology (molecular: chromatin accessibility, expression; cellular: morphology; circuit: gross brain structure), termed quantitative trait loci (QTLs). Here, we will integrate QTLs across multiple levels of biology in order to statistically prioritize causal pathways by which genetic variation creates risk for complex neuropsychiatric disorders. Causal modeling goes well beyond previous co-localization work, as it allows the prioritization of expensive functional validation experiments for cellular or molecular changes that are a cause of the disorder, rather than those that are a consequence or independent of the disorder. It additionally allows inference of key experimental parameters including cell-type and developmental time period. Finally, causal inference when combined across multiple levels of biology and multiple disorder risk loci allows for assessment of convergence at a biological level, cell-type, or developmental time period, which is critical information for therapeutic targeting. We will leverage the computational and statistical frameworks of Bayesian probabilistic networks and causal inference in a new framework that utilizes association summary statistics, as well-powered multi-level data collected on the same individuals is almost always infeasible. Subsequently, we will experimentally validate the molecular predictions of our model using epigenetic engineering in primary human neural progenitor cells, and in turn revising the computational models. Prioritizing causal molecular pathways of disorder associated variants, and identifying the relevant cell-type and developmental stage will increase the success rate of validation experiments and shed light on mechanisms of neuropsychiatric disorders in an unbiased manner.