This project aims to develop a fast GWAS data analysis pipeline incorporating GPU acceleration and Machine learning.
Our high level goals for the hackathon include -
- Recreating the Hail GWAS sample using RAPIDS APIs
- Wrapping RAPIDS GWAS functions into high level APIs within gpu-gwas framework
- (if time permits) Scaling to larger VCF dataset (e.g. chr22)
Working doc for our team is here
pip install -e requirements.txt
To test proper setup, please run the following from the root folder of the repo
python gpugwas_test.py
The gpugwas package is broken up into multiple independent modules that deal with different components
of the GWAS pipeline. The modules are all located under the gpugwas folder.
gpugwas.io- This module contains I/O related functions such as loading a VCF/annotation file into a CUDA dataframe.gpugwas.algorithms- This module contains ML algorithm implementations in CUDA typically used in GWAS (e.g. linear regression, logistic regression, etc).gpugwas.viz- This module contains functions used in visualizing the GWAS model outputs (manhattan plots, q-q plots, etc)
An example use case of the pipeline is available in workflow.py.
To execute, please run
python workflow.py