bioRxivBioinformaticsPreprint
SAIGE-BRUSH: an efficient, user-friendly and low cost cloud implementation for genome-wide association studies
Tonya M. BrunettiiD, Nikita V. PozdeyeviD, Michelle DayaiD, Kathleen C. BarnesiD, Nicholas M. RafaelsiD, Christopher R. GignouxiD
Abstract
SAIGE-Biobank Re-Usable SAIGE Helper (SAIGE-BRUSH) allows users with little computational expertise to utilize SAIGE for GWAS with parallelization and data collection on biobank data sets. This implementation requires no installation and has additional features not programmed within the original SAIGE framework, such as concurrency, reproducibility, reusability, scalability, association analysis results filtering and output plots. This is all achieved without writing any code from the user. This implementation is currently being utilized by the Biobank at the Colorado Center for Personalized Medicine (CCPM) on Google Cloud but is flexible for a number of architectures available to genetic analysts. Availability: This open source implementation is freely available at https://github.com/tbrunetti/SAIGE-BRUSH and is licensed under the MIT License. Contact: Chris Gignoux at chris.gignoux@cuanschutz.edu & Nick Rafaels at nicholas.rafaels@cuanschutz.edu Supplemental Material: For detailed user documentation, please visit https://saige-brush.readthedocs.io/en/latest/
- Citations
- 0
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- SAGA (Simplified Association Genomewide Analyses): a user-friendly Pipeline to Democratize Genome-Wide Association Studies
Basilio Cieza, Neetesh Pandey, … Giuseppe Tosto·bioRxiv·Aug 25, 2025
- H3AGWAS : A portable workflow for Genome Wide Association Studies
Jean‐Tristan Brandenburg, Lindsay V. Clark, … Scott Hazelhurst·BMC BioinformaticsPeer-reviewed·Nov 19, 2022
- Analyzing bivariate cross-trait genetic architecture in GWAS summary statistics with the BIGA cloud computing platform
Yujue Li, Fei Xue, … Bingxin Zhao·bioRxiv·Apr 28, 2023
- Performing highly parallelized and reproducible GWAS analysis on biobank-scale data
Sebastian Schönherr, Johanna F. Schachtl‐Riess, … Lukas Forer·NAR Genomics and BioinformaticsPeer-reviewed·Jan 5, 2024
- A GWAS platform built on iPlant cyber-infrastructure
Liya Wang, Doreen Ware, … Lincoln Stein·bioRxiv·Feb 20, 2014
- gwaRs: an R shiny web application for visualizing genome-wide association studies data
Lethukuthula L. Nkambule·bioRxiv·Apr 17, 2020