Colocalization

Overview

The colocalization module allows you to visualize using a Manhattan plot all genes tested for a pair of GWAS and eQTL datasets (e.g. age-related macular degeneration GWAS vs all eQTLs in the brain).

Usage

Follow these to generate a colocalization Manhattan plot.

1. First, select a GWAS study. Note: you can type a few letters to search for the study.

2. Once a study has been selected, the Trait drop-down menu will update according to the study. Select the desired trait. Most GWAS only tested one trait whereas some GWAS tested multiple traits. For instance, GWAS_Age_Related_Macular_Degeneration_Fritsche_2013 tested 3 traits: Advanced-vs-Controls, GeographicAtropy-vs-Controls, and Neovascular-vs-Controls.

3. Select the desired eQTL study.

4. Click on Plot colocalization.

5. A Manhattan plot will appear to show all genes tested for the selected pair of GWAS and eQTL. This plot is interactive. You can drag horizontally and double-click to zoom in, double-click to zoom out, and click on a data point to select a gene.

6. Click to select a gene. A table below the plot will show basic information about the gene, and its colocalization probability calculated by FINEMAP and eCAVIAR. FINEMAP was used for the fine-mapping step for each association dataset separately, and the resulting posterior probabilities were used to compute a colocalization posterior probability (CLPP) as described in the eCAVIAR method (see the eCAVIAR paper for more details). As a rule of thumb, eCAVIAR authors recommend colocalization probability > 0.01 as the default cutoff.

7. Click on Plot LocusCompare to see the LocusCompare plot for the selected gene.

If you would like to know more about the LocusCompare plots, head on over to the LocusCompare Plot page.