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

单细胞多组学方法对人脑中精神疾病风险位点进行机械表征

基本信息

批准号：
10407453
负责人：
Chongyuan Luo
金额：
$ 74万
依托单位：
UNIVERSITY OF CALIFORNIA LOS ANGELES
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-05-18 至 2026-02-28
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10407453
关键词：
Adopted Adult Alleles BRAIN initiative Biological Assay Brain Brain region CRISPR interference CRISPR/Cas technology Cell Line Cell Nucleus Cells Censuses Chromatin Clustered Regularly Interspaced Short Palindromic Repeats Complex Coupled DNA Methylation DNA Sequence DNA methylation profiling Data Set Disease Enhancers Etiology Gene Expression Regulation Genes Genetic Genetic Engineering Genomic Segment Goals Hippocampus (Brain)Human Individual Joints Link Maps Measurement Mediating Mental disorders Methods Methylation Molecular Molecular Conformation Phenotype Population Probability Property Protein Isoforms Quantitative Trait Loci Regulation Regulatory Element Resolution Risk Schizophrenia Small Nuclear RNA Statistical Methods Structure Technology Testing Tissues Transcription Alteration Transcriptional Regulation Untranslated RNA Variant autism spectrum disorder brain cell causal variant cell type disorder risk epigenomics experimental study genetic variant genome wide association study genome-wide insight multiple omics nerve stem cell neuropsychiatric disorder novel pleiotropism polygenic risk score psychiatric genomics repaired risk variant trait transcriptome transcriptome sequencing transcriptomics

项目摘要

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.

项目概要大规模全基因组关联研究 (GWAS) 已识别出少数到数百个风险位点针对每种主要类型的神经精神疾病。后 GWAS 时代的主要挑战之一是确定因果变异并剖析每个风险位点的调节机制。的分析高度异质的大脑结构混淆了精神疾病的因果遗传机制和细胞类型。我们假设大脑区域和细胞类型选择性地容易受到精神疾病的影响细胞类型特异性基因调控是这种选择性的基础。在这个拟议的项目中，我们的目标是确定与大脑区域有关的具有高空间分辨率的个体遗传变异的因果概率细胞类型。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-m3C-seq)，允许细胞类型特异性识别调控元件、增强子基因循环以及将非编码变体与其调控元件连接起来目标。为了进一步确定细胞类型特异性基因表达调控的遗传机制，我们将开发并将尖端统计方法应用于现有和新产生的单核群体人脑皮质和海马体的 RNA-seq 数据集。我们将开发上下文特定遗传学（内容）区分组织或细胞类型特异性与组织共享的基因遗传成分表达调节。我们还将应用最近开发的群体等位基因特异性映射 (PLASMA) 它整合了 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，我们将使用高通量 CRISPR 干扰实验确定非编码变体的功能和精确的变体替换实验，以及应用正交统计方法来链接变异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、统计精细图谱和基因工程，并且可能会提供新的深入了解精神障碍的遗传机制。