ADSP Functional Genomics Consortium (FunGen-AD)
FunGen-xQTL resource catalog
FunGen-xQTL fine-maps molecular QTL across eight molecular modalities, from chromatin and RNA to protein and metabolites, in brain and immune cells, and reuses the resulting credible sets and posterior effect estimates to interpret AD GWAS loci. This catalog describes each dataset and where to get it. To look up associations by variant, gene or region, use the NIAGADS xQTL Atlas.
Counts are participants. One participant can contribute several tissues, cell types and assays. The dataset and participant counts are those reported for the FunGen-xQTL study; the catalog lists 73 pages, including AD GWAS and reference data.
Find a dataset
Opens the data catalog, where results can be narrowed by cohort, modality, tissue, status and access.
By cohort
- ROSMAP 38 Religious Orders Study and Rush Memory and Aging Project · cohort page
- MSBB 9 Mount Sinai Brain Bank · cohort page
- Knight-ADRC 11 Knight Alzheimer Disease Research Center · cohort page
- MiGA 3 Microglia Genomic Atlas · cohort page
- STARNET 3 Cardiovascular tissues, macrophages · cohort page
- MetaBrain 2 External bulk brain eQTL replication resource · external site ↗
By QTL modality
- eQTL 11 Gene expression · overview
- sQTL 6 Alternative splicing · overview
- mQTL 3 DNA methylation · overview
- caQTL 1 Chromatin accessibility · overview
- haQTL 1 Histone acetylation · overview
- metQTL 2 Metabolite abundance · overview
- pQTL 3 Protein abundance · overview
- glycoQTL 1 Glycoprotein abundance · overview
AD GWAS studies
- Bellenguez et al. 20222022 summary statistics and 2026 main, no-biobank and no-proxy analyses
- Wightman et al. 2021Summary statistics
- Kunkle et al. 2019Summary statistics
- Jansen et al. 2019Summary statistics
- ADGC imputation protocolAdapted from the ADGC GWAS QC protocol
The study uses eight AD GWAS datasets from the four studies above. The AD GWAS overview lists all eight.
Counts are catalog pages. Cohort counts include QTL results and the omics data behind them; modality counts are QTL result pages.
Where the data are hosted
How to get the data| Resource | Host | Identifier |
|---|---|---|
| Variant and gene summaries | Synapse | syn69865684 |
| xQTL models | Synapse | syn69670588 |
| Raw QTL data | Synapse | syn69670632 |
| Reference files | Synapse | syn69670634 |
| xQTL summary statistics and fine-mapping | NIAGADS DSS · open | NG00184.v1 |
| xQTL Atlas · query and genome browser | NIAGADS | xqtl.niagads.org |
Access differs by resource. The AD Knowledge Portal folders are controlled access: you need a Synapse account and data access requested on the AD Knowledge Portal. NG00184.v1 on NIAGADS DSS is open, and the xQTL Atlas is a public query site. Each dataset page states the access for its own files.
About FunGen-AD
The Alzheimer’s Disease Sequencing Project Functional Genomics Consortium (FunGen-AD) is a collaborative initiative aimed at understanding the molecular mechanisms underlying Alzheimer’s disease through multi-omic and genetic screening approaches.
Available Resources
FunGen-xQTL
The FunGen-xQTL project provides comprehensive molecular quantitative trait loci (QTL) analyses across multiple molecular phenotypes, along with integrative analysis for neurodegenerative disorders particularly Alzheimer’s disease. All datasets are hosted on the AD Knowledge Portal: variant & gene summaries (syn69865684), xQTL models (syn69670588), raw QTL data (syn69670632), reference files (syn69670634).
- xQTL Analysis and xQTL-GWAS Integration Results along with File Format Description
- Study Cohorts - Information about participating cohorts including ROSMAP, Knight ADRC, MSBB, MiGA, STARNET, and MetaBrain
- GWAS Summary Statistics - Alzheimer’s disease GWAS data from major studies (Bellenguez 2022, Kunkle 2019, Wightman 2021, Jansen 2019; the 2026 update adds the Bellenguez main, no-biobank and no-proxy analyses) and integrated AD fine-mapping and colocalization analyses
- Molecular Phenotypes - Multi-omic data including:
- Gene expression (bulk RNA-seq: ROSMAP DLPFC/PCC/AC/microglia/monocyte, MSBB, Knight ADRC, MetaBrain, MiGA, STARNET)
- Alternative splicing (bulk RNA-seq and single-nucleus with ISSAC method)
- Proteomics including glycoproteomics (brain)
- DNA methylation
- Histone modification (H3K9ac ChIP-seq)
- Chromatin accessibility (snATAC-seq)
- Metabolomics (brain)
- Single-nucleus RNA-seq (ROSMAP DLPFC: CUIMC1, MIT, and mega cohorts)
- Genotype (WGS) and covariates
- xQTL Data - Molecular QTL associations organized by modality: eQTL, sQTL, pQTL, glycoQTL, mQTL, haQTL, caQTL, metQTL, plus ROSMAP transcriptomic pattern QTLs (tpQTL) and trans-xQTLs
- FGMB Atlas - Multi-context regulome-wide association study (RWAS) prediction model resources, including AD Knowledge Portal accessions for models, gene-level association results, causal fine-mapping outputs.
- AD Loci Integration Summary - The 195-locus AD GWAS × xQTL summary (October 2026 release) on the AD Knowledge Portal (syn69865823), also browsable in the interactive xQTL-AD-loci-explorer
- Reference Data - ADSP-based LD reference panels (16,571 European ancestry samples), including an LD sketch panel, and other analytical resources
Software resources implementing the xQTL analysis are also available:
- xQTL Analysis Protocol - Standardized computational protocols for QTL mapping, fine-mapping, colocalization, and integrative analyses, with tutorials and an xQTL Analysis Workflow Builder that picks the modules and commands for your data
- xQTL Companion Statistical Methods Implementation - Fine-mapping, enrichment, colocalization, TWAS, and Mendelian randomization tools
Data Access
The AD Knowledge Portal folders below are controlled access (you need a Synapse account and approved data access on the AD Knowledge Portal):
- AD Knowledge Portal — variant & gene summaries (syn69865684)
- AD Knowledge Portal — xQTL models (syn69670588)
- AD Knowledge Portal — raw QTL data (syn69670632)
- AD Knowledge Portal — reference files (syn69670634)
- NIAGADS (genomics data). The harmonized xQTL summary statistics in NG00184.v1 on NIAGADS DSS are open access.
This resource is actively maintained by the FunGen-AD Analysis Working Group and reflects data generated through October 2026