LocusFocus: A Colocalization Tool to Prioritize Genes and Tissues from GWAS and eQTL Integration

Select coordinate system

Select the human coordinate system your files are stored in.

hg19 is GRCh37
hg38 is GRCh38

Upload GWAS Data

Marker Column Name:

Use marker ID column to infer variant position and alleles

Chromosome Column Name:

Position Column Name:

Reference Allele Column Name:

Alternate Allele Column Name:

Add COLOC2 method (additional fields required)

P-value Column Name:

Coordinates (max: 2 Mbp):

Lead Marker Name:

Select Simple Sum Colocalization Region

Leave blank for default (+/- 0.1Mbp from the lead SNP)

Coordinates:

By convention, colocalization methods default to testing colocalization +/- 0.1 Mbp of the top variant in the primary (e.g. GWAS) dataset.

You can override this behaviour by entering the subregion to test colocalization on.

Linkage Disequilibrium

Select an LD population to use from the 1000 Genomes dataset OR upload an LD matrix file.

LD Population

Upload LD Matrix

For the most accurate results, the LD (r²) matrix for your primary (e.g. GWAS) dataset is recommended to be uploaded as a .ld square matrix file. If the .ld file is unavailable, you may choose one of the publicly-available 1000 Genomes population datasets.

EUR: European
NFE: Non-Finnish European
AFR: African
EAS: East Asian
SAS: South Asian
AMR: Ad Mixed American

Note: For Hg38 X chromosome datasets, we will only use female sample data from 1000 Genomes to calculate LD.

Select Secondary Datasets

At least one secondary dataset must be uploaded to perform colocalization analysis.

Select GTEx eQTL Data to Render

Please allow sufficient time for analysis if selecting many tissues and genes.
Analyses may take >30 minutes when selecting many tissue-gene pairs.

Select GTEx (V7) Tissues

Select Genes (enter coordinates above to populate)

Upload Secondary Datasets

For convenience, we are making available eQTL datasets from the GTEx project for use as secondary datasets.

LocusFocus, however, can perform more custom colocalization analyses utilizing other secondary (e.g. eQTL, mQTL, other phenotypes) datasets provided by the user.

Custom secondary datasets may be uploaded after conversion to HTML format as described above or in the documentation.

Refer to the documentation on how to generate the HTML file.
You may use the merge_and_convert_to_html.py script, or merge_and_convert_to_html_coloc2.py.
You may use provided sample datasets as a guide to formatting your files.

Stage one set-based p-value threshold

For the Simple Sum method, a first-stage set-based Bonferroni p-value threshold is used for the set of secondary datasets with alpha 0.05 (0.05 divided by the number of secondary datasets)

Enter a value if you would like to override the default threshold.

LocusFocus

Colocalization Testing Across Datasets

Version release: 1.6.0 alpha