Alzheimer's Disease and Related Dementias (AD/ADRD) progressively impair essential cognitive and mental functions, resulting in significant emotional, physical, and financial burdens. Genome-wide association studies have identified thousands of loci contributing to the risk of more than a hundred serious mental and neurological disorders (SMND), including AD/ADRD and others such as schizophrenia, Parkinson’s disease and multiple sclerosis. Integrating risk loci with quantitative trait loci (QTLs) for molecular traits, such as gene expression and epigenome regulation, in human brain tissue, has been widely adopted as an analytical strategy to nominate causal mechanisms for AD/ADRD and SMNDs. So far, large-scale integrative analyses using this approach have utilized homogenate brain tissue, which is composed of multiple cell types, and therefore cell-type-specific QTLs are not fully captured. This is an important limitation given that risk variants for AD/ADRD and SMNDs act through cell-type-specific biological mechanisms. Initial efforts have included cell-type-specific QTL analysis in the human brain by utilizing single-cell data, but the sample size of such studies is hindered by the increased experimental costs. To overcome these limitations, we propose to establish the brain single-cell xQTL (BrainCellQTL) Consortium that brings together existing resources of more than 10,000 single-cell libraries from more than 3,000 unique brain donors, as well as expertise in single-cell biology, neuroscience, statistical genetics and machine learning, to facilitate the harmonization of brain single-cell data, QTL generation and data sharing with the scientific community. Activities will be organized around the Synapse Data Platform by Sage Bionetworks, an NIH-Designated Generalist Repository that supports dozens of research consortia, focused on neurodegeneration, neuropsychiatric disease, cancer, rare disease, and other research areas. The Synapse Data Platform will be utilized to receive data, validate it against metadata and data standards, and harmonize them for downstream analysis. To increase the reproducibility and transparency of BrainCellQTL consortium research, we will use CAVATICA by Seven Bridges for data processing and analysis. CAVATICA is a secure, scalable, and extendable data commons platform that empowers collaboration and scientific analysis. Upon successful completion of the proposed research, we expect to construct a cell type-specific QTL atlas for the human brain, which we will use to derive genetically driven gene dysregulation in AD/ADRD and SMNDs, thus, enabling us to: (1) increase our mechanistic understanding of dysfunction in AD/ADRD and SMNDs; (2) better prioritize significant genes and molecular pathways for future mechanistic studies; (3) provide a valuable resource that can be applied in ongoing and future genome-wide association studies; (4) provide preprocessed and harmonized single-nucleus brain omics data that can be ut...