DOI: 10.18129/B9.bioc.SAIGEgds    

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

Bioconductor version: Release (3.12)

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 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 original SAIGE R package (v0.29.4.4 for single variant tests, Zhou et al. 2018). SAIGEgds also implements some of the SPAtest functions in C to speed up the calculation of Saddlepoint approximation. Benchmarks show that SAIGEgds is 5 to 6 times faster than the original 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>

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biocViews Genetics, Software, StatisticalMethod
Version 1.4.2
In Bioconductor since BioC 3.10 (R-3.6) (1.5 years)
License GPL-3
Depends R (>= 3.5.0), gdsfmt(>= 1.20.0), SeqArray(>= 1.30.0), Rcpp
Imports methods, stats, utils, RcppParallel, SPAtest (>= 3.0.0)
LinkingTo Rcpp, RcppArmadillo, RcppParallel (>= 5.0.0)
Suggests parallel, crayon, RUnit, knitr, markdown, rmarkdown, BiocGenerics, SNPRelate
SystemRequirements C++11, GNU make
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