This is the development version of SAIGEgds; for the stable release version, see SAIGEgds.
Scalable Implementation of Generalized mixed models using GDS files in Phenome-Wide Association StudiesBioconductor version: Development (3.22)
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] ORCID: 0000-0002-1390-0708 , Wei Zhou [ctb] (the original author of the SAIGE R package), J. Wade Davis [ctb]
Maintainer: Xiuwen Zheng <xiuwen.zheng at abbvie.com>
Citation (from within R, entercitation("SAIGEgds")): Installation
To install this package, start R (version "4.5") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
# The following initializes usage of Bioc devel
BiocManager::install(version='devel')
BiocManager::install("SAIGEgds")
For older versions of R, please refer to the appropriate Bioconductor release.
DocumentationTo view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("SAIGEgds")Follow Installation instructions to use this package in your R session.
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