
==== Front
bioRxiv
BIORXIV
bioRxiv
Cold Spring Harbor Laboratory

10.1101/2024.05.17.594583
preprint
2
Article
Accelerating Genome- and Phenome-Wide Association Studies using GPUs – A case study using data from the Million Veteran Program
Rodriguez Alex
Kim Youngdae
Nandi Tarak Nath
Keat Karl
Kumar Rachit http://orcid.org/0000-0002-7736-3307

Bhukar Rohan
Conery Mitchell
Liu Molei
Hessington John
Maheshwari Ketan
Schmidt Drew
VA Million Veteran Program
Begoli Edmon
Tourassi Georgia
Muralidhar Sumitra
Natarajan Pradeep
Voight Benjamin F http://orcid.org/0000-0002-6205-9994

Cho Kelly
Gaziano J Michael
Damrauer Scott M
Liao Katherine P
Zhou Wei
Huffman Jennifer E
Verma Anurag
Madduri Ravi K
22 5 2024
2024.05.17.594583http://biorxiv.org/lookup/doi/10.1101/2024.05.17.594583
nihpp-2024.05.17.594583.pdf
Abstract

The expansion of biobanks has significantly propelled genomic discoveries yet the sheer scale of data within these repositories poses formidable computational hurdles, particularly in handling extensive matrix operations required by prevailing statistical frameworks. In this work, we introduce computational optimizations to the SAIGE (Scalable and Accurate Implementation of Generalized Mixed Model) algorithm, notably employing a GPU-based distributed computing approach to tackle these challenges. We applied these optimizations to conduct a large-scale genome-wide association study (GWAS) across 2,068 phenotypes derived from electronic health records of 635,969 diverse participants from the Veterans Affairs (VA) Million Veteran Program (MVP). Our strategies enabled scaling up the analysis to over 6,000 nodes on the Department of Energy (DOE) Oak Ridge Leadership Computing Facility (OLCF) Summit High-Performance Computer (HPC), resulting in a 20-fold acceleration compared to the baseline model. We also provide a Docker container with our optimizations that was successfully used on multiple cloud infrastructures on UK Biobank and All of Us datasets where we showed significant time and cost benefits over the baseline SAIGE model.
==== Body
pmc
