SAIGE-GPU - Accelerating Genome- and Phenome-Wide Association Studies using GPUs.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41572430", "kind": "science-record", "revision": 3, "content_hash": "sha256:a63b04d6962422e594cdd88412b01e29fcaf707488ca1c78618fb772b4feae7a", "created_at": "2026-11-01T20:31:32Z", "updated_at": "2026-11-01T22:15:21Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "SAIGE-GPU - Accelerating Genome- and Phenome-Wide Association Studies using GPUs.", "doi": "10.1093/bioinformatics/btag032", "pmid": "41572430", "authors": ["Alex Rodriguez", "Youngdae Kim", "Tarak Nath Nandi", "Karl Keat", "Rachit Kumar", "Mitchell Conery", "Rohan Bhukar", "Molei Liu", "John Hessington", "Ketan Maheshwari", "Edmon Begoli", "Georgia Tourassi", "Sumitra Muralidhar", "Pradeep Natarajan", "Benjamin F Voight", "Kelly Cho", "J Michael Gaziano", "Scott M Damrauer", "Katherine P Liao", "Wei Zhou", "Jennifer E Huffman", "Anurag Verma", "Ravi K Madduri"], "journal": "Bioinformatics (Oxford, England)", "year": 2026, "abstract": "MOTIVATION: 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.\nRESULTS: 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 2,068 phenotypes from 635,969 participants in the Million Veteran Program (MVP), 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.\nAVAILABILITY: 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.\nSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", "claims": [{"id": "claim-method", "statement": "SAIGE fits a GLMM for each phenotype (Y) to account for fixed covariates (X), genotypes (G) and sample relatedness through random genetic effects (b).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41572430", "locator": "sections[3] \"2.1 Optimizations for SAIGE linear mixed model fitting (Step 1)\" paragraph 2", "quote": "SAIGE fits a GLMM for each phenotype (Y) to account for fixed covariates (X), genotypes (G) and sample relatedness through random genetic effects (b)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The tool is available in both source and containerized form for deployment on GPU-enabled systems. Source code and documentation are accessible at: https://github.com/saigegit/SAIGE/tree/SAIGE-GPU-1.3.3 and https://exascale-genomics.github.io/SAIGE-GPU. A code snapshot is archived at Zenodo for reproducibility (DOI: [10.5281/zenodo.17642591]).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41572430", "locator": "sections[13] \"4 Conclusion\" paragraph 3", "quote": "The tool is available in both source and containerized form for deployment on GPU-enabled systems. Source code and documentation are accessible at: https://github.com/saigegit/SAIGE/tree/SAIGE-GPU-1.3.3 and https://exascale-genomics.github.io/SAIGE-GPU. 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No tolerance is published because the paper states none."}], "gaps": ["method.regression_modelling_mixed_effects"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "axes": {"method": {"tier": "R1", "expressible": false, "required_operations": [], "missing_operations": ["regression_modelling_mixed_effects"], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: mixed_effects."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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