The coloc app assesses whether two association signals in the same genomic region are consistent with a shared causal variant. It runs approximate Bayes factor colocalisation analysis using the coloc R package.
Use this app to compare two GWAS or molecular-trait association datasets at the same locus, for example a disease GWAS and an expression quantitative trait locus (eQTL) study.
Before you begin
You will need:
- Two summary-statistics tables covering the same genomic region
- The corresponding Sequoia study ID for each table
- A project in which to save the analysis and output
The app uses the selected study IDs to retrieve relevant study information, including trait type and sample size. It can also use gnomAD v4.1 genome and exome data to annotate missing effect allele frequency information.
Name your job
Enter a descriptive Prefix. You may also add an optional Suffix to distinguish related analyses.
[Screenshot 1: Highlight the Prefix and Suffix fields.]
For example:
- Prefix:
CAD_eQTL_coloc - Suffix:
APOE
Select the first study
Select the Sequoia study corresponding to your first summary-statistics table in studyId1.
[Screenshot 2: Red box around the studyId1 dropdown.]
This study ID must correspond to the data uploaded or selected as table1.
Select the second study
Select the Sequoia study corresponding to your second summary-statistics table in studyId2.
[Screenshot 3: Red box around the studyId2 dropdown.]
This study ID must correspond to the data uploaded or selected as table2.
Ensure that table1 is paired with studyId1 and table2 is paired with studyId2. Incorrect pairing may lead to incorrect trait metadata or analysis results.
Select a project
Choose the destination project in the project_id dropdown.
[Screenshot 4: Red box around the project_id dropdown and the Research Hub → Projects note.]
If needed, create a project in Research Hub → Projects before launching the app.
Provide the first summary-statistics table
Use table1 to provide the first dataset.
[Screenshot 5: Highlight the table1 section, including Upload New, Select Existing, and From Jobs Output.]
You can add a table in one of three ways:
- Upload New — Upload a file from your computer.
- Select Existing — Choose a file already stored in the portal.
- From Jobs Output — Use a file produced by a previous Sequoia job.
Provide the second summary-statistics table
Use table2 to provide the second dataset in the same way.
[Screenshot 6: Highlight the table2 section and its upload area.]
The two tables should represent comparable data from the same genomic locus and should contain variants that can be aligned across both datasets.
Launch the analysis
Review the two study-table pairings and selected project, then select Launch App.
[Screenshot 7: Red box and arrow pointing to “Launch App”.]
When the analysis is complete, access the results from the selected project or the job results area.
Interpreting the results
Colocalisation results estimate the relative evidence for several possible explanations for the observed signals, including:
- Neither trait having a regional association
- An association with only the first trait
- An association with only the second trait
- Two distinct association signals
- A shared association signal
Evidence supporting a shared signal suggests that the two traits may be influenced by the same underlying variant in that region. It does not, on its own, prove causality or identify the biological mechanism.
Tips
- Compare traits at the same genomic locus; genome-wide files should be restricted to the region of interest before analysis where appropriate.
- Use harmonised, consistently formatted summary statistics where possible.
- Ensure the selected study IDs accurately describe the corresponding input tables.
- Review allele alignment and variant overlap when results are unexpected or no colocalisation signal is returned.