Overview

About the NIAGADS xQTL Portal

The NIAGADS xQTL Portal provides access to harmonized molecular quantitative trait locus (QTL) results generated from human brain and related multi-omic datasets. The portal is designed to help researchers explore how genetic variation may influence molecular activity across genes, variants, genomic regions, molecular assays, brain regions, and cell-type contexts.

This resource is intended for researchers who want to move from a genetic finding, such as a GWAS locus or variant, to interpretable functional genomic evidence. Users can search by gene, variant, or genomic region; compare associations across QTL types and datasets; view selected results in genomic context; and download summary statistics and metadata for downstream analysis.

The portal integrates xQTL results from major aging-brain and Alzheimer’s disease-relevant studies, including ROSMAP, MSBB, Knight-ADRC, MiGA, and related single-nucleus resources. Results are organized into a consistent framework to make cross-dataset exploration easier while preserving the context needed for careful interpretation.

Team and contributors

This resource is maintained by NIAGADS and the ADSP Functional Genomics Consortium xQTL team in collaboration with contributing investigators, data generators, analysts, and infrastructure teams. We are grateful to the study participants and their families, whose contributions made these datasets possible.

• NIAGADS
• FunGen-xQTL WG
• Fun-Gen collaborators

Read the paper

This work is described in-depth in a medRxiv manuscript:
A TAD-informed aging-brain xQTL atlas of multi-modal and cell-type-resolved regulatory variation. DOI: 10.64898/2026.05.21.26353713

What is an xQTL?

An xQTL, or molecular quantitative trait locus, is a statistical association between a genetic variant and a measured molecular trait. In this portal, the “x” refers to different molecular phenotypes, including gene expression, RNA splicing, DNA methylation, histone acetylation, protein abundance, and single-nucleus cell-type-specific expression.

For example, a variant associated with Alzheimer’s disease risk may also be associated with altered expression of a nearby gene, altered splicing of a transcript, differential methylation at a CpG site, altered histone acetylation at a regulatory element, or altered protein abundance. These associations can help prioritize candidate genes, molecular mechanisms, brain regions, and cell types for follow-up study.

An xQTL association should not be interpreted as proof of causality by itself. Association signals can reflect linkage disequilibrium, shared regulatory architecture, correlated molecular traits, study-specific effects, or unmeasured confounding. Portal results are best used for hypothesis generation and should be integrated with GWAS fine-mapping, colocalization, functional annotations, experimental evidence, and biological knowledge.

What can I do with this portal?

The portal supports several common research tasks:

• Search a GWAS variant to determine whether that variant, or variants in the same region, are associated with molecular traits in human brain datasets.
• Search a gene to identify variants associated with that gene or related molecular features across datasets, tissues, cell types, and QTL types.
• Search a genomic region to examine local regulatory evidence across multiple omics types.
• Compare results across QTL types, including eQTLs, sQTLs, mQTLs, haQTLs, pQTLs, and single-nucleus eQTLs.
• View selected results in a genome browser to inspect genomic context, nearby genes, regulatory elements, and QTL tracks.
• Download significant associations, fine-mapped associations, metadata, and larger QTL files for downstream analysis.

The portal is designed to make xQTL results easier to discover, but interpretation still requires attention to dataset, assay, molecular feature, brain region, cell type, ancestry, statistical model, and multiple-testing correction.

Data in the portal

Current release summary

The current release includes harmonized xQTL results across multiple human brain and related datasets.

These counts summarize the scope of the release. Exact sample sizes and available molecular traits may differ by dataset, assay, QTL type, brain region, and cell-type context.

Study design at a glance

The portal combines results from multiple studies and molecular assays (see study-level summary table below). Because each association is context-specific, users should interpret results with the study design in mind.

Users should consult dataset metadata before comparing effects across studies. Differences in assay type, sample composition, brain region, ancestry, disease status, cell-type composition, covariates, and molecular feature definitions can influence whether a signal is detected and how it should be interpreted.

xQTL types in this Atlas

The xQTL Atlas includes multiple classes of molecular QTLs. Each QTL type tests for associations between genetic variants and a specific molecular measurement.

Not all QTL types use the same feature identifiers. For eQTLs and snuc-eQTLs, the molecular feature is often a gene. For sQTLs, mQTLs, haQTLs, and pQTLs, the molecular feature may be a splice event, CpG site, histone acetylation peak, protein, peptide, or other assay-specific feature. Users should consult the result columns and metadata to determine exactly what molecular trait was tested.

Studies and cohorts in this Atlas

This Atlas includes 7 cohorts, all described in the table below. Note that the three single-nucleus studies are all ROSMAP derived, but have slight differences in individuals included.

Types of associations in this Atlas

Following xQTL calling or identification of fine-mapped associations, filtering takes place to prioritize associations with statistical significance. These types of filters, and which ones are kept in the Atlas, are explained below.

Note: while fine-mapping results may be referred to by their corresponding xQTL-type, e.g., eQTL fine-mapping results, these fine-mapping results are not downstream of xQTL calling and are produced totally independently from the xQTL calling. This is only to indicate that the same molecular traits are being analyzed.

Why harmonization matters

Brain functional genomics datasets are often generated by different groups using different assays, file formats, metadata conventions, genome annotations, statistical models, and analysis pipelines. Without harmonization, it can be difficult to search, compare, or reuse results across datasets.

The NIAGADS xQTL Portal reduces this friction by organizing association results, metadata, annotations, and downloadable files into a consistent framework. Harmonization is intended to make results easier to find and compare, but it does not remove all study-specific differences.

All data provided in the xQTL atlas has been harmonized:

Release notes

V1.0 — First version of NIAGADS xQTL data released on 06/18/2026 (NIAGADS DSS NG00184).

Tutorials and how-to

How to search the portal

Users can search the portal by gene, variant, or genomic region.

Example searches include:

• Gene: BIN1
• Variant: rs6733839
• Variant ID: chr2:127135234:C:T
• Region: chr2:127048026-127107288

Search behavior should be interpreted using the following rules:

Search results may include associations from multiple datasets, brain regions, cell types, QTL types, and molecular features. Users should filter or stratify results by context before drawing biological conclusions.

Important questions to consider when reviewing search results:

• Is the queried variant itself associated with the molecular trait, or is the signal driven by nearby variants in linkage disequilibrium?
• Is the molecular feature a gene, splice event, CpG site, histone acetylation peak, protein, or other assay-specific feature?
• Which dataset, brain region, and cell-type context support the association?
• Is the association significant after the appropriate multiple-testing correction?
• Does the effect direction align with the biological hypothesis?
• Is there fine-mapping evidence supporting a causal signal?

How to use the genome browser

The genome browser can be used to inspect xQTL results in genomic context. This is useful for understanding whether a signal overlaps nearby genes, regulatory annotations, chromatin features, or other QTL tracks.

When using the genome browser, users should consider:

• Whether the displayed region contains multiple genes or regulatory elements.
• Whether multiple QTL types support the same candidate gene or feature.
• Whether association peaks align across datasets, tissues, or cell types.
• Whether the queried variant is the strongest association signal or one of many linked variants.
• Whether fine-mapped variants fall within plausible regulatory annotations.

Browser views are useful for visual interpretation, but downstream analyses should rely on downloaded summary statistics and metadata.

How to download data

The portal supports several download routes depending on your needs.

Downloaded summary files may include fields such as rsID, variantId, targetGene, #chrom, chromStart, chromEnd, Z, beta, p-value, FDR, and other summary statistics. Full tracks and larger association files are available through the NIAGADS DSS Open Access Data Portal.

Because full association files can be large, users should check file size, compression format, indexing, genome build, and README documentation before large-scale downstream analysis.

Understanding results

How to interpret a result

Each xQTL result represents a statistical association between a genetic variant and a molecular trait in a specific context. That context may include the dataset, QTL type, assay, brain region, cell type, molecular feature, statistical model, and multiple-testing correction.

A typical result should be interpreted using the following fields.

Effect direction and allele convention

All effect statistics (beta, Z-score) are reported for non-reference / alternative allele.

Positive beta values indicate that the effect allele is associated with higher measured abundance or activity of the molecular trait. Negative beta values indicate that the effect allele is associated with lower measured abundance or activity. The effect allele is defined as non-reference/alternative allele.

For example, if a variant has a positive eQTL beta for BIN1, that means the specified effect allele is associated with increased BIN1 expression in the reported dataset and context. If the same variant has a negative beta in another brain region or cell type, the difference may reflect tissue specificity, cell-type specificity, or sampling variation and assay differences.

Statistical significance

The portal reports statistical evidence such as p-values, FDR values, HMT significance, and fine-mapping results. These statistics answer related but distinct questions.

• A p-value describes evidence for association in a specific test.
• FDR describes significance after per-target multiple-testing correction
• HMT significance reflects a hierarchical multiple-testing procedure.
• Fine-mapping statistics prioritize variants that may explain an association signal within a locus.

FDR was calculated for each target separately, e.g. for each gene for eQTLs, each splicing event for sQTLs, or each methylation site for mQTLs.

Recommended interpretation workflow

A typical workflow for interpreting a GWAS locus is:

1. Search the lead GWAS variant or associated genomic region.
2. Identify significant xQTL associations in the region.
3. Check whether associations point to one or more plausible genes or molecular features.
4. Review the QTL type, dataset, brain region, cell type, and molecular assay.
5. Examine effect direction and allele convention.
6. Evaluate statistical support using p-value, FDR, HMT significance, and fine-mapping evidence.
7. Compare evidence across datasets and molecular phenotypes.
8. Use the genome browser to inspect regional context.
9. Download summary statistics and metadata for formal downstream analysis.
10. Integrate results with GWAS fine-mapping, LD, colocalization, functional annotation, and experimental evidence.

Limitations

The portal is designed to support interpretation and hypothesis generation, but users should keep several limitations in mind.

• xQTL associations do not prove that a variant causally regulates a molecular trait.
• A significant xQTL and a disease association in the same region may reflect different causal variants unless supported by colocalization or fine-mapping evidence.
• Effect sizes may not be directly comparable across datasets, assays, QTL types, tissues, or cell types.
• Signals can be influenced by sample size, ancestry, disease composition, technical covariates, brain region, postmortem factors, and cell-type composition.
• Bulk-tissue QTLs may reflect mixtures of cell types.
• Single-nucleus QTLs may be cell-type-specific but can have smaller sample sizes or different statistical power.
• Variant identifiers, genome builds, and allele conventions must be checked before integrating portal results with external GWAS or QTL resources.
• Multiple-testing correction should be interpreted according to the documented correction scope.
• Fine-mapping results prioritize likely causal variants but do not provide experimental proof.

Users should integrate portal results with independent genetic, molecular, clinical, and experimental evidence before making strong claims about disease mechanisms.

File formats and metadata

The portal provides metadata in JSON and TSV formats, along with variant-, gene-, and region-based summary TSVs. Full association files and track-style files are available through NIAGADS DSS (NG00184).

Search result TSVs

The TSV obtained from the Downloads subtab after performing a Search has the core statistic results as well as some contextual metadata. These are explained below in order of appearance in these TSVs.

* indicates statistic calculated as part of xQTL calling. All fine-mapping statistics are annotated with corresponding xQTL result, so p in a fine-mapping summary TSV refers to its corresponding xQTL association, not a p-value from the fine-mapping analysis.

Comprehensive Atlas metadata

The comprehensive metadata found at the bottom of Downloads has the following fields:

Citation and help

Citation and acknowledgment

When using this portal or its data, please cite:

A TAD-informed aging-brain xQTL atlas of multi-modal and cell-type-resolved regulatory variation. DOI: 10.64898/2026.05.21.26353713

Users should also include the required NIAGADS dataset acknowledgment language in publication acknowledgment sections and follow the acknowledgment instructions associated with the relevant NIAGADS dataset record.

Contact

For questions, data issues, or documentation suggestions, please contact help@niagads.org.

When reporting an issue, please include the query used, the QTL type, dataset, browser URL or downloaded file name, and a short description of the unexpected result.