gpu-coloc 0.1.2

Ultra-fast GPU-enabled Bayesian colocalisation

gpu-coloc

gpu-coloc is a GPU-accelerated Bayesian colocalization implementation (COLOC), delivering identical results to R's coloc.bf_bf approximately 1000 times faster.

Citation

If you use gpu-coloc, please cite: (citation placeholder)

Installation

Install via pip (Python ≥3.12):

pip install gpu-coloc

Verify Installation

To confirm installation, clone this repository:

git clone https://github.com/mjesse-github/gpu-coloc
cd gpu-coloc
bash test.sh

This creates an example/ directory containing an example_results.tsv file.

Workflow

Note: Paths assume gpu-coloc is in the working directory; adjust paths if necessary.

Variant Naming Convention

Variants must follow the naming format: chr[chromosome]_[position]_[ref]_[alt]. Ensure renaming is completed before Step 1. Use chromosome X, not 23.

1. Prepare Signals and Summary Files

• Signal Files: Save signals as [signal].pickle, containing variants and their log Bayes Factors (lbf).

Example format:

variant	chrX_153412224_C_A	chrX_153412528_C_T	...
lbf	-0.060991	-1.508802	...

• Summary File: Tab-separated, structured as:

signal	chromosome	location_min	location_max	signal_strength	lead_variant
QTD000141_ENSG00000013563_L1	X	153412224	155341332	12.1069377174147	chrX_154403855_T_G
...

Naming examples:

• Summary: gwas_summary.tsv
• Signals directory: gwas_signals/[signal].pickle

See scripts in summary_and_signals_examples/ for reference; modifications may be necessary.

2. Format Data

gpu-coloc --format --input [path_to_signals] --input_summary [summary_file] --output [output_folder]

3. Run Colocalization

gpu-coloc coloc.py --run --dir1 [formatted_dataset_1] --dir2 [formatted_dataset_2] --results [results_output] --p12 1e-6 --H4 0.8