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Scalable Implementation of Generalized mixed models using GDS files in Phenome-Wide Association Studies

Bioconductor version: Release (3.19)

Scalable implementation of generalized mixed models with highly optimized C++ implementation and integration with Genomic Data Structure (GDS) files. It is designed for single variant tests and set-based aggregate tests in large-scale Phenome-wide Association Studies (PheWAS) with millions of variants and samples, controlling for sample structure and case-control imbalance. The implementation is based on the SAIGE R package (v0.45, Zhou et al. 2018 and Zhou et al. 2020), and it is extended to include the state-of-the-art ACAT-O set-based tests. Benchmarks show that SAIGEgds is significantly faster than the SAIGE R package.

Author: Xiuwen Zheng [aut, cre] , Wei Zhou [ctb] (the original author of the SAIGE R package), J. Wade Davis [ctb]

Maintainer: Xiuwen Zheng <xiuwen.zheng at>

Citation (from within R, enter citation("SAIGEgds")):


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SAIGEgds Tutorial (single variant tests) HTML R Script
Reference Manual PDF


biocViews Genetics, GenomeWideAssociation, Software, StatisticalMethod
Version 2.4.0
In Bioconductor since BioC 3.10 (R-3.6) (4.5 years)
License GPL-3
Depends R (>= 3.5.0), gdsfmt(>= 1.28.0), SeqArray(>= 1.42.3), Rcpp
Imports methods, stats, utils, Matrix, RcppParallel, CompQuadForm, survey
System Requirements C++11, GNU make
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Suggests parallel, markdown, rmarkdown, crayon, SNPRelate, RUnit, knitr, ggmanh, BiocGenerics
Linking To Rcpp, RcppArmadillo, RcppParallel (>= 5.0.0)
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Source Package SAIGEgds_2.4.0.tar.gz
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