quickdraws 0.1.0

Quickdraws is a software tool for performing Genome-Wide Association Studies (GWAS)

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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 either:

1. on Linux, a cuda-enabled version of pytorch
2. on macOS, the latest nightly build of pytorch, which can leverage the MPS backend

Use the pytorch configuration helper to find suitable installation instruction for your system. The code snippets below will probably work for most systems, and should install quickdraws in approximately 10 minutes:

Linux

pip install torch --index-url https://download.pytorch.org/whl/cu118
pip install quickdraws

macOS

pip install --pre torch --index-url https://download.pytorch.org/whl/nightly/cpu
pip install quickdraws

Running example

Once you install quickdraws, three executables should be available in your path:

1. convert-to-hdf5
2. quickdraws-step-1
3. 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://palamaralab.github.io/software/quickdraws/manual/

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 ([e-mail])

Citation

Loya et al., "A scalable variational inference approach for increased mixed-model association power" under review

Release Notes

v0.1.0 (2024-10-15)

First public release to accompany the paper (see citation above).