SAIGE-GPU: accelerating genome- and phenome-wide association studies using GPUs

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.

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.

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