Quickdraws is a software tool for performing Genome-Wide Association Studies (GWAS)
Project description
Quickdraws
Quickdraws relies on cuda-enabled pytorch for speed, and it is expected to work on most cuda-compatible Linux systems.
Installation
It is strongly recommended to either set up a python virtual environment, or a conda environment:
Python virtual environment
python -m venv venv
source venv/bin/activate
pip install --upgrade pip setuptools wheel
Conda environment
conda create -n quickdraws python=3.11 -y
conda activate quickdraws
pip install --upgrade pip setuptools wheel
Install pytorch and quickdraws
It is necessary for anything bigger than toy examples to use a cuda-enabled version of pytorch. Use the pytorch configuration helper to find suitable installation instruction for your system. The code snippet below will probably work for most systems:
pip install torch --index-url https://download.pytorch.org/whl/cu118
pip install quickdraws
Running example
Once you install quickdraws
, three executables should be available in your path:
convert-to-hdf5
quickdraws-step-1
quickdraws-step-2
.
Clone the Git repository to access the example data and script demonstrating how these can be used:
git clone https://github.com/PalamaraLab/quickdraws.git
cd quickdraws
bash run_example.sh
Local development
To make changes to the quickdraws sourcecode, obtain the repository and install it using poetry. Assuming you have poetry installed:
git clone https://github.com/PalamaraLab/quickdraws.git
cd quickdraws
poetry install
Documentation
See https://github.com/PalamaraLab/quickdraws/wiki/Quickdraws-GWAS-Software-Documentation
Summary Statistics for some UKB traits
See https://www.stats.ox.ac.uk/publication-data/sge/ppg/quickdraws/
Contact information
For any technical issues please contact Hrushikesh Loya (loya@stats.ox.ac.uk)
Citation
Loya et al., "A scalable variational inference approach for increased mixed-model association power" under review
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