This study presents a comprehensive single-cell genomic resource, brainSCOPE, derived from 388 human brains, containing over 2.8 million nuclei. The resource integrates single-nuclei RNA sequencing (snRNA-seq), single-nuclei ATAC sequencing (snATAC-seq), and single-nuclei multi-omics data, enabling the analysis of 28 distinct brain cell types. The study identifies over 550,000 cell-type-specific regulatory elements and 1.4 million single-cell expression quantitative trait loci (eQTLs), which are used to construct cell-type regulatory and cell-to-cell communication networks. These networks reveal cellular changes associated with aging and neuropsychiatric disorders. An integrative model is developed to impute single-cell expression and simulate gene expression perturbations, prioritizing 250 disease-risk genes and drug targets with associated cell types. The study also explores transcriptome and epigenome variation across the cohort at the single-cell level, identifying genes with high cell-type variability and low inter-individual variability, such as CNR1. It finds that genes with lower expression variability are more sequence-conserved, while some highly conserved genes exhibit high expression variance. The study identifies cell-type-specific eQTLs, revealing that many are unique to specific cell types, with a significant portion overlapping with bulk eQTLs. The study further constructs gene regulatory networks (GRNs) for each cell type, showing complex network rewiring across cell types and highlighting cell-type-specific functions. A cell-to-cell communication network is constructed using ligand-receptor pairs, revealing distinct signaling pathways among excitatory, inhibitory, and glial cell types. The study finds that neuropsychiatric disorders significantly alter these communication patterns, with schizophrenia and bipolar disorder showing notable changes. The study also assesses cell-type-specific transcriptomic and epigenetic changes in aging, identifying genes that are upregulated in older individuals and showing strong predictive value for age based on transcriptomic data. An integrative model, LNCTP, is developed to impute gene expression and prioritize disease genes across cell types. The model shows high accuracy in imputing cell-type-specific gene expression and identifies genes and pathways contributing to specific phenotypes. The model prioritizes genes associated with neuropsychiatric disorders, including TCF4, LINGO2, and ANKHD1, highlighting their roles in specific cell types. The study also simulates perturbations of prioritized genes and drug targets, showing their effects on gene expression and trait propensity. The results are validated using CRISPR perturbations, showing strong correlations with LNCTP-predicted effects. The brainSCOPE resource provides a comprehensive single-cell functional genomics resource for investigating brain disorders, enabling the identification of cell-type-specific regulatory elements, eQTLs, and gene regulatory networks. The resource includes visualizer tools for various data types and offers insightsThis study presents a comprehensive single-cell genomic resource, brainSCOPE, derived from 388 human brains, containing over 2.8 million nuclei. The resource integrates single-nuclei RNA sequencing (snRNA-seq), single-nuclei ATAC sequencing (snATAC-seq), and single-nuclei multi-omics data, enabling the analysis of 28 distinct brain cell types. The study identifies over 550,000 cell-type-specific regulatory elements and 1.4 million single-cell expression quantitative trait loci (eQTLs), which are used to construct cell-type regulatory and cell-to-cell communication networks. These networks reveal cellular changes associated with aging and neuropsychiatric disorders. An integrative model is developed to impute single-cell expression and simulate gene expression perturbations, prioritizing 250 disease-risk genes and drug targets with associated cell types. The study also explores transcriptome and epigenome variation across the cohort at the single-cell level, identifying genes with high cell-type variability and low inter-individual variability, such as CNR1. It finds that genes with lower expression variability are more sequence-conserved, while some highly conserved genes exhibit high expression variance. The study identifies cell-type-specific eQTLs, revealing that many are unique to specific cell types, with a significant portion overlapping with bulk eQTLs. The study further constructs gene regulatory networks (GRNs) for each cell type, showing complex network rewiring across cell types and highlighting cell-type-specific functions. A cell-to-cell communication network is constructed using ligand-receptor pairs, revealing distinct signaling pathways among excitatory, inhibitory, and glial cell types. The study finds that neuropsychiatric disorders significantly alter these communication patterns, with schizophrenia and bipolar disorder showing notable changes. The study also assesses cell-type-specific transcriptomic and epigenetic changes in aging, identifying genes that are upregulated in older individuals and showing strong predictive value for age based on transcriptomic data. An integrative model, LNCTP, is developed to impute gene expression and prioritize disease genes across cell types. The model shows high accuracy in imputing cell-type-specific gene expression and identifies genes and pathways contributing to specific phenotypes. The model prioritizes genes associated with neuropsychiatric disorders, including TCF4, LINGO2, and ANKHD1, highlighting their roles in specific cell types. The study also simulates perturbations of prioritized genes and drug targets, showing their effects on gene expression and trait propensity. The results are validated using CRISPR perturbations, showing strong correlations with LNCTP-predicted effects. The brainSCOPE resource provides a comprehensive single-cell functional genomics resource for investigating brain disorders, enabling the identification of cell-type-specific regulatory elements, eQTLs, and gene regulatory networks. The resource includes visualizer tools for various data types and offers insights