{ "_id": "6a22aba70ef520ea2f58bcc0", "Package": "SAIGEgds", "Type": "Package", "Title": "Scalable Implementation of Generalized mixed models using GDS\nfiles in Phenome-Wide Association Studies", "Version": "2.13.0", "Date": "2026-04-23", "Authors@R": "c(person(\"Xiuwen\", \"Zheng\", role=c(\"aut\", \"cre\"),\nemail=\"xiuwen.zheng@abbvie.com\", comment=c(ORCID=\"0000-0002-1390-0708\")),\nperson(\"Wei\", \"Zhou\", role=\"ctb\",\ncomment=\"the original author of the SAIGE R package\"),\nperson(\"J. Wade\", \"Davis\", role=\"ctb\"))", "Description": "Scalable implementation of generalized mixed models with\nhighly optimized C++ implementation and integration with\nGenomic Data Structure (GDS) files. It is designed for single\nvariant tests and set-based aggregate tests in large-scale\nPhenome-wide Association Studies (PheWAS) with millions of\nvariants and samples, controlling for sample structure and\ncase-control imbalance. The implementation is based on the\nSAIGE R package (v0.45, Zhou et al. 2018 and Zhou et al. 2020),\nand it is extended to include the state-of-the-art ACAT-O\nset-based tests. Benchmarks show that SAIGEgds is significantly\nfaster than the SAIGE R package. Optional OpenCL-based GPU\nacceleration is supported for the GRM cross-product computation\nin null model fitting and for GRM construction.", "License": "GPL-3", "SystemRequirements": "GNU make", "VignetteBuilder": "knitr", "ByteCompile": "TRUE", "URL": "https://github.com/AbbVie-ComputationalGenomics/SAIGEgds", "biocViews": "Software, Genetics, StatisticalMethod, GenomeWideAssociation", "Config/pak/sysreqs": "make zlib1g-dev", "Repository": "https://bioc.r-universe.dev", "Date/Publication": "2026-04-28 12:51:06 UTC", "RemoteUrl": "https://github.com/bioc/SAIGEgds", "RemoteRef": "HEAD", "RemoteSha": "e2f4a5f55cb001e1471fae0fb6fb2fd09d41826c", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-06-05 10:48:32 UTC", "User": "root" }, "Author": "Xiuwen Zheng [aut, cre] (ORCID:\n),\nWei Zhou [ctb] (the original author of the SAIGE R package),\nJ. 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