Package: SAIGEgds 2.5.0

Xiuwen Zheng

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

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.

Authors:Xiuwen Zheng [aut, cre], Wei Zhou [ctb], J. Wade Davis [ctb]

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SAIGEgds/json (API)
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Bug tracker:https://github.com/abbvie-computationalgenomics/saigegds/issues

On BioConductor:SAIGEgds-2.5.0(bioc 3.20)SAIGEgds-2.4.0(bioc 3.19)

bioconductor-package

13 exports 1.08 score 33 dependencies 1 mentions

Last updated 2 months agofrom:fb07dcb226

Exports:glmmHeritabilitypACATpACAT2seqAssocGLMM_ACAT_OseqAssocGLMM_ACAT_VseqAssocGLMM_BurdenseqAssocGLMM_SKATseqAssocGLMM_SPAseqFitDenseGRMseqFitLDpruningseqFitNullGLMM_SPAseqFitSparseGRMseqSAIGE_LoadPval

Dependencies:askpassBiocGenericsBiostringsCompQuadFormcrayoncurlDBIgdsfmtGenomeInfoDbGenomeInfoDbDataGenomicRangeshttrIRangesjsonlitelatticeMatrixmimeminqamitoolsnumDerivopensslR6RcppRcppArmadilloRcppParallelS4VectorsSeqArraysurveysurvivalsysUCSC.utilsXVectorzlibbioc