SINGLE-CELL MULTI-OMIC APPROACHES TO MECHANISTICALLY CHARACTERIZE PSYCHIATRIC DISORDER RISK LOCI IN THE HUMAN BRAIN

项目总结 大规模全基因组关联研究已经确定了几个到数百个风险基因座 对于每一种主要的神经精神障碍。后GWAS时代的主要挑战之一是 确定因果变量，并剖析每个风险基因的调控机制。分析了几个问题 精神疾病的病因遗传机制被高度异质性的大脑结构搞混了 和细胞类型。我们假设大脑区域和细胞类型有选择地易受精神障碍的影响 而特定细胞类型的基因调控是这种选择性的基础。在这个拟议的项目中，我们的目标是确定 具有高空间分辨率的个体遗传变异相对于大脑区域和 单元类型。为此，我们将生成一个独特的染色质单核联合图谱数据集 10个成人脑区的构象和DNA甲基化(sn-m3c-seq)，允许特定类型的细胞 识别调控元件、增强子-基因环和将非编码变异体连接到它们的调控 目标。为了进一步确定特定细胞类型的基因表达调控的遗传机制，我们将 开发尖端统计方法并将其应用于现有和新产生的种群单核 人类大脑皮层和海马体的rna-seq数据集。我们将发展特定于背景的遗传学 (内容)区分特定于组织或细胞类型的基因与组织共享的基因成分 表达调控。我们还将应用最近开发的群体等位基因特异图谱(等离子)。 它整合了QTL和等位基因特异的QTL，用于调控变异精细定位。为了验证我们的发现，我们将 使用两种高通量CRISPR干扰实验确定非编码变体的功能 和精确的变量替换实验，以及应用正交统计方法将 变异体对疾病因果关系的功能特性。我们提议的项目整合了多种方法 包括单细胞多组学、统计精细图谱和基因工程，并可能提供新的 对精神障碍的遗传机制的洞察。

PROJECT SUMMARY Large-scale genome-wide association studies (GWAS) have identified between a handful to hundreds of risk loci for each major type of neuropsychiatric disorders. One of the main challenges for the post-GWAS era is to determine the causal variants and dissect the regulatory mechanism in each of the risk loci. The analysis of causal genetic mechanisms for psychiatric diseases is confounded by the highly heterogeneous brain structures and cell types. We hypothesize that brain regions and cell types are selectively vulnerable to mental disorders and cell-type-specific gene regulation underlies such selectivity. In this proposed project, we aim to determine the causal probability of individual genetic variants with high spatial resolution with respect to brain regions and cell types. To this end, we will generate a unique dataset of single-nucleus joint profiling of chromatin conformation and DNA methylation (sn-m3C-seq) for 10 adult brain regions, allowing the cell-type-specific identification of regulatory elements, enhancer-gene looping and linking non-coding variants to their regulatory target. To further identify the genetic mechanisms for cell-type-specific regulation of gene expression, we will develop and apply cutting-edge statistical methods to existing and newly generated population single-nucleus RNA-seq datasets for the human brain cortex and hippocampus. We will develop CONtexT spEcific geNeTics (CONTENT) to distinguish tissue- or cell-type-specific from the tissue-shared genetic component of gene expression regulation. We will also apply the recently developed PopuLation Allele-Specific MApping (PLASMA) that integrates QTL and allele-specific QTL for regulatory variant fine-mapping. To validate our findings, we will experimentally determine the function of non-coding variants using both high-throughput CRISPR interference and precise variant replacement experiments, as well as apply orthogonal statistical approaches to link the functional properties of variants to disease causality. Our proposed project integrates diverse approaches including single-cell multi-omics, statistical fine-mapping, and genetic engineering and will likely provide new insights into the genetic mechanism of mental disorders.