# SAIGE-GPU: accelerating genome- and phenome-wide association studies using GPUs **Authors:** Alex Rodriguez, Youngdae Kim, Tarak Nath Nandi, Karl Keat, Rachit Kumar, Mitchell Conery, Rohan Bhukar, Molei Liu, John Hessington, Ketan Maheshwari, Sumitra Muralidhar, Sumitra Muralidhar, Jennifer Moser, Jennifer E Deen, Philip S Tsao, Sumitra Muralidhar, J Michael Gaziano, Elizabeth Hauser, Amy Kilbourne, Michael Matheny, Dave Oslin, J Michael Gaziano, Jessica V Brewer, Mary T Brophy, Kelly Cho, Lori Churby, Scott L DuVall, Saiju Pyarajan, Luis E Selva, Shahpoor (Alex) Shayan, Stacey B Whitbourne, J Michael Gaziano, Brady Stephens, Todd Connor, Themistocles L Assimes, Adriana Hung, Henry Kranzler, Samuel Aguayo, Sunil Ahuja, Kathrina Alexander, Xiao M Androulakis, Prakash Balasubramanian, Zuhair Ballas, Elizabeth S Bast, Jean Beckham, Sujata Bhushan, Edward Boyko, David Cohen, Louis Dellitalia, Gerald Wayne Dryden, L Christine Faulk, Joseph Fayad, Daryl Fujii, Saib Gappy, Frank Gesek, Michael Godschalk, Jennifer Greco, Todd W Gress, Samir Gupta, Salvador Gutierrez, Mark Hamner, John Harley, Daniel J Hogan, Adriana Hung, Robin Hurley, Pran Iruvanti, Frank Jacono, Darshana Jhala, Seema Joshi, Scott Kinlay, Michael Landry, Peter Liang, Suthat Liangpunsakul, Jack Lichy, Tze Shien Lo, C Scott Mahan, Ronnie Marrache, Stephen Mastorides, Kristin Mattocks, Paul Meyer, Jonathan Moorman, Providencia Morales, Timothy Morgan, Maureen Murdoch, Eknath Naik, James Norton, Olaoluwa Okusaga, Michael K Ong, Kris Ann Oursler, Ismene Petrakis, Samuel Poon, Amneet S Rai, Michael Rauchman, Richard Servatius, Satish Sharma, River Smith, Peruvemba Sriram, Patrick Strollo, Neeraj Tandon, Philip Tsao, Gerardo Villareal, Jessica Walsh, John Wells, Jeffrey Whittle, Mary Whooley, Peter Wilson, Junzhe Xu, Shing Shing Yeh, Andrew W Yen, Edmon Begoli, Georgia Tourassi, Sumitra Muralidhar, Pradeep Natarajan, Benjamin F Voight, Kelly Cho, John Michael Gaziano, Scott M Damrauer, Katherine P Liao, Wei Zhou, Jennifer E Huffman, Anurag Verma, Ravi K Madduri PMC · DOI: 10.1093/bioinformatics/btag032 · 2026-01-22 ## TL;DR This paper introduces SAIGE-GPU, a faster version of a popular GWAS tool that uses GPUs to speed up large-scale genetic studies. ## Contribution The novel use of GPU acceleration in SAIGE for mixed-model GWAS computations enables practical phenome-wide studies. ## Key findings - SAIGE-GPU achieved a 5-fold speedup in mixed model fitting for large biobank datasets. - The method supports efficient analysis of diverse and admixed populations using GPU-optimized kernels. - Multi-core and multi-trait parallelization further improved performance on cloud platforms. ## Abstract Genome-wide association studies (GWAS) at biobank scale are computationally intensive, especially for admixed populations requiring robust statistical models. SAIGE is a widely used method for generalized linear mixed-model GWAS but is limited by its CPU-based implementation, making phenome-wide association studies impractical for many research groups. We developed SAIGE-GPU, a GPU-accelerated version of SAIGE that replaces CPU-intensive matrix operations with GPU-optimized kernels. The core innovation is distributing genetic relationship matrix calculations across GPUs and communication layers. Applied to 2068 phenotypes from 635 969 participants in the Million Veteran Program, including diverse and admixed populations, SAIGE-GPU achieved a 5-fold speedup in mixed model fitting on supercomputing infrastructure and cloud platforms. We further optimized the variant association testing step through multi-core and multi-trait parallelization. Deployed on Google Cloud Platform and Azure, the method provided substantial cost and time savings. Source code and binaries are available for download at https://github.com/saigegit/SAIGE/tree/SAIGE-GPU-1.3.3. A code snapshot is archived at Zenodo for reproducibility (DOI: [10.5281/zenodo.17642591]). SAIGE-GPU is available in a containerized format for use across HPC and cloud environments and is implemented in R/C++ and runs on Linux systems. ## Full-text entities - **Species:** Homo sapiens (human, species) [taxon 9606] ## Figures 1 figure with captions in the complete paper: https://tomesphere.com/paper/PMC12960912/full.md --- Source: https://tomesphere.com/paper/PMC12960912