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

项目摘要 大规模的全基因组关联研究（GWAS）已经确定了少数到数百个风险位点 每一种主要的神经精神疾病。后GWAS时代的主要挑战之一是 确定致病变异并剖析每个风险位点的调节机制。分析 精神疾病的因果遗传机制被高度异质的大脑结构所混淆， 和细胞类型。我们假设大脑区域和细胞类型有选择性地易受精神障碍的影响 并且细胞类型特异性基因调节是这种选择性的基础。在这个项目中，我们的目标是确定 个体遗传变异的因果概率相对于大脑区域具有高空间分辨率， 细胞类型。为此，我们将生成一个独特的染色质单核联合分析数据集 10个成人大脑区域的构象和DNA甲基化（sn-m3 C-seq），允许细胞类型特异性 调控元件的鉴定、增强子-基因成环和将非编码变体与其调控元件连接 目标为了进一步确定基因表达的细胞类型特异性调控的遗传机制，我们将 发展和应用先进的统计方法，以现有的和新产生的人口单核 人类大脑皮层和海马体的RNA-seq数据集。我们将开发CONtexT特定的geNetics （内容）区分组织或细胞类型特异性基因和组织共有的基因 表达调控我们还将应用最近开发的群体等位基因特异性映射（PLASMA） 整合QTL和等位基因特异性QTL用于调控变异精细定位。为了验证我们的发现，我们将 实验上使用高通量CRISPR干扰和 和精确的变体替换实验，以及应用正交统计方法将 变体的功能特性与疾病因果关系。我们提出的项目整合了多种方法 包括单细胞多组学，统计精细绘图和基因工程，并可能提供新的 对精神疾病遗传机制的深入了解。

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.