SAIGEgds-package {SAIGEgds} | R Documentation

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

### Description

Scalable and accurate implementation of generalized mixed mode with the support of Genomic Data Structure (GDS) files and highly optimized C++ implementation. It is designed for single variant tests in large-scale phenome-wide association studies (PheWAS) with millions of variants and hundreds of thousands of samples, e.g., UK Biobank genotype data, controlling for case-control imbalance and sample structure in single variant association studies.

The implementation of SAIGEgds is based on the original SAIGE R package (v0.29.4.4) [Zhou et al. 2018] https://github.com/weizhouUMICH/SAIGE/releases/tag/v0.29.4.4. All of the calculation with single-precision floating-point numbers in SAIGE are replaced by the double-precision calculation in SAIGEgds. SAIGEgds also implements some of the SPAtest functions in C to speed up the calculation of Saddlepoint Approximation.

### Details

Package: | SAIGEgds
Type: | Package
License: | GPL version 3

### Author(s)

Xiuwen Zheng xiuwen.zheng@abbvie.com, Wei Zhou (the original author of the SAIGE R package, https://github.com/weizhouUMICH/SAIGE)

### References

Zheng X, Davis J.Wade. SAIGEgds – an efficient statistical tool for large-scale PheWAS with mixed models. *Bioinformatics* (2020). DOI: 10.1093/bioinformatics/btaa731.

Zhou W, Nielsen JB, Fritsche LG, Dey R, Gabrielsen ME, Wolford BN, LeFaive J, VandeHaar P, Gagliano SA, Gifford A, Bastarache LA, Wei WQ, Denny JC, Lin M, Hveem K, Kang HM, Abecasis GR, Willer CJ, Lee S. Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. *Nat Genet* (2018). Sep;50(9):1335-1341.

Zheng X, Gogarten S, Lawrence M, Stilp A, Conomos M, Weir BS, Laurie C, Levine D. SeqArray – A storage-efficient high-performance data format for WGS variant calls. *Bioinformatics* (2017). DOI: 10.1093/bioinformatics/btx145.

### Examples

    # open the GDS file
    fn <- system.file("extdata", "grm1k_10k_snp.gds", package="SAIGEgds")
    gdsfile <- seqOpen(fn)

    # load phenotype
    phenofn <- system.file("extdata", "pheno.txt.gz", package="SAIGEgds")
    pheno <- read.table(phenofn, header=TRUE, as.is=TRUE)
    head(pheno)

    # fit the null model
    glmm <- seqFitNullGLMM_SPA(y ~ x1 + x2, pheno, gdsfile, trait.type="binary")

    # p-value calculation
    assoc <- seqAssocGLMM_SPA(gdsfile, glmm, mac=10)

    head(assoc)

    # close the GDS file
    seqClose(gdsfile)

[Package SAIGEgds version 1.4.0 Index]