SAIGEgds: Scalable Implementation of Generalized mixed models in PheWAS using GDS files

GNU General Public License, GPLv3

Features

Scalable 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]. It is implemented with optimized C++ codes taking advantage of sparse structure of genotypes. 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.

Benchmarks using the UK Biobank White British genotype data (N=430K) with coronary heart disease and simulated cases, show that SAIGEgds is 5 to 6 times faster than the SAIGE R package in the steps of fitting null models and p-value calculations. When used in conjunction with high-performance computing (HPC) clusters and/or cloud resources, SAIGEgds provides an efficient analysis pipeline for biobank-scale PheWAS.

Bioconductor:

Release Version: v2.2.0 (http://www.bioconductor.org/packages/SAIGEgds)

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Package Maintainer

Xiuwen Zheng

Installation

• Requires R (≥ v3.5.0), gdsfmt (≥ v1.20.0), SeqArray (≥ v1.32.0)

• Recommend GNU GCC (≥ v6.0), requiring C++11

• Bioconductor repository R if (!requireNamespace("BiocManager", quietly=TRUE)) install.packages("BiocManager") BiocManager::install("SAIGEgds") The BiocManager::install() approach may require that you build from source, i.e. make and compilers must be installed on your system -- see the R FAQ for your operating system; you may also need to install dependencies manually.

• Development version from Github (for developers/testers only) R library("devtools") install_github("AbbVie-ComputationalGenomics/SAIGEgds")

Package vignette

If the package is installed from Bioconductor repository or package rebuilding, users can start R and enter to view documentation: R browseVignettes("SAIGEgds")

Examples

```R library(SeqArray) library(SAIGEgds)

open the GDS file for genetic relationship matrix (GRM)

grmfn <- system.file("extdata", "grm1k10ksnp.gds", package="SAIGEgds") (grmgds <- seqOpen(grm_fn))

load phenotype

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

sample.id y yy x1 x2

1 s1 0 4.5542 1.5118 1

2 s2 0 3.7941 0.3898 1

3 s3 0 5.0411 -0.6212 1

...

fit the null model

glmm <- seqFitNullGLMMSPA(y ~ x1 + x2, pheno, grmgds, trait.type="binary", sample.col="sample.id", num.thread=2)

SAIGE association analysis:

Filtering variants:

[==================================================] 100%, completed, 0s

Fit the null model: y ~ x1 + x2 + var(GRM)

# of samples: 1,000

# of variants: 9,976

using 2 threads

...

close the file

seqClose(grm_gds)

open the GDS file for association testing

genofn <- system.file("extdata", "assoc100snp.gds", package="SAIGEgds") (genogds <- seqOpen(genofn))

File: assoc_100snp.gds (10.5K)

+ [ ] *

|--+ description [ ] *

|--+ sample.id { Str8 1000 LZMA_ra(12.6%), 625B }

|--+ variant.id { Int32 100 LZMA_ra(48.5%), 201B } *

...

p-value calculation

assoc <- seqAssocGLMMSPA(genogds, glmm, mac=10, parallel=2)

SAIGE association analysis:

# of samples: 1,000

# of variants: 100

MAF threshold: NaN

MAC threshold: 10

missing threshold for variants: 0.1

p-value threshold for SPA adjustment: 0.05

variance ratio for approximation: 0.9391186

# of processes: 2

[==================================================] 100%, completed, 0s

# of variants after filtering by MAF, MAC and missing thresholds: 38

Done.

head(assoc)

id chr pos rs.id ref alt AF.alt mac num beta SE pval pval.noadj converged

1 4 1 4 rs4 A C 0.0100 20 1000 -0.074992 0.791685 0.924533 0.924533 TRUE

2 12 1 12 rs12 A C 0.0150 30 1000 -0.091001 0.657140 0.889861 0.889861 TRUE

3 14 1 14 rs14 A C 0.0375 75 1000 -0.075455 0.434152 0.862023 0.862023 TRUE

...

close the file

seqClose(geno_gds) ```

Citations

Zheng X, Davis J.Wade. SAIGEgds -- an efficient statistical tool for large-scale PheWAS with mixed models. Bioinformatics (2021). Mar;37(5):728-730. DOI: 10.1093/bioinformatics/btaa731.

Zhou W, Zhao Z, Nielsen JB, Fritsche LG, LeFaive J, Taliun SAG, Bi W, Gabrielsen ME, Daly MJ, Neale BM, Hveem K, Abecasis GR, Willer CJ, Lee S. Scalable generalized linear mixed model for region-based association tests in large biobanks and cohorts. Nat Genet (2020). Jun;52(6):634-639. DOI:10.1038/s41588-020-0621-6

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. DOI: 10.1038/s41588-018-0184-y.

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.

See Also

SeqArray: Data management of large-scale whole-genome sequence variant calls

gds2bgen: Format conversion from bgen to gds