Quick Links Documentation Notes Interactive job Batch job Swarm of jobs

SAIGE is installed as a container with it's own R environment on the Biowulf Cluster, please do not load R module when running SAIGE. If there are conflicts/errors about R, please check the loaded modules with 'module list'.

SAIGE is an R package developed with Rcpp for genome-wide association tests in large-scale data sets and biobanks. The method:

• accounts for sample relatedness based on the generalized mixed models
• allows for model fitting with either full or sparse genetic relationship matrix (GRM)
• works for quantitative and binary traits
• handles case-control imbalance of binary traits
• computationally efficient for large data sets
• performs single-variant association tests
• provides effect size estimation through Firth's Bias-Reduced Logistic Regression
• performs conditional association analysis

SAIGE-GENE (now known as SAIGE-GENE+) are new method extension in the R package for testing rare variant in set-based tests.

• performs BURDEN, SKAT, and SKAT-O tests
• allows for tests on multiple minor allele frequencies cutoffs and functional annotations
• allows for specifying weights for markers in the set-based tests
• performs conditional analysis to identify associations independent from nearly GWAS signals

The package takes genotype file input in the following formats

• PLINK (bed, bim, fam), BGEN, VCF, BCF, SAV

References:

• Zhou W et.al. Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. Nat Genet. 2018 Sep;50(9):1335-1341. PubMed |  Journal
• Zhou W et.al. Scalable generalized linear mixed model for region-based association tests in large biobanks and cohorts Nature genetics, 52(6), pp.634-639. Journal
• Zhou W et.al. SAIGE-GENE+ improves the efficiency and accuracy of set-based rare variant association tests Nature genetics, 54(10), pp.1466-1469. Journal

• SAIGE Main Site:Main Site
• SAIGE documentation: SAIGE-doc

• Module Name: SAIGE (see the modules page for more information)

  step1_fitNULLGLMM.R --help
  	
  

• Environment variables set
  • $SAIGE_TEST_DATA

Allocate an interactive session and run the program.
Sample session (user input in bold):

[user@biowulf]$ sinteractive --cpus-per-task=6 --mem=4G
salloc.exe: Pending job allocation 46116226
salloc.exe: job 46116226 queued and waiting for resources
salloc.exe: job 46116226 has been allocated resources
salloc.exe: Granted job allocation 46116226
salloc.exe: Waiting for resource configuration
salloc.exe: Nodes cn3144 are ready for job

[user@cn3144 ~]$ module load SAIGE
[user@cn3144 ~]$ cp -r ${SAIGE_TEST_DATA:-none}/extdata .
[user@cn3144 ~]$ cd extdata
[user@cn3144 ~]$ step1_fitNULLGLMM.R     \
        --plinkFile=./input/nfam_100_nindep_0_step1_includeMoreRareVariants_poly \
        --phenoFile=./input/pheno_1000samples.txt_withdosages_withBothTraitTypes.txt \
        --phenoCol=y_binary \
        --covarColList=a9 \
        --sampleIDColinphenoFile=IID \
        --traitType=binary        \
        --outputPrefix=./output/example_binary_includenonAutoforvarRatio \
        --nThreads=4    \
        --LOCO=FALSE    \
        --relatednessCutoff=0.0 \
        --FemaleCode=2 \
        --MaleCode=1 \
        --IsOverwriteVarianceRatioFile=TRUE

		
[user@cn3144 ~]$ exit
salloc.exe: Relinquishing job allocation 46116226
[user@biowulf ~]$

Create a batch input file (e.g. SAIGE.sh). For example:

#!/bin/bash
#SBATCH --cpus-per-task=6
#SBATCH --mem=4G
#SBATCH --time=2:00:00
#SBATCH --partition=norm

set -e
module load SAIGE
step1_fitNULLGLMM.R     \
        --plinkFile=./input/nfam_100_nindep_0_step1_includeMoreRareVariants_poly \
        --phenoFile=./input/pheno_1000samples.txt_withdosages_withBothTraitTypes.txt \
        --phenoCol=y_binary \
        --covarColList=a9 \
        --sampleIDColinphenoFile=IID \
        --traitType=binary        \
        --outputPrefix=./output/example_binary_includenonAutoforvarRatio \
        --nThreads=4    \
        --LOCO=FALSE    \
        --relatednessCutoff=0.0 \
        --FemaleCode=2 \
        --MaleCode=1 \
        --IsOverwriteVarianceRatioFile=TRUE

Submit the job:

sbatch SAIGE.sh

Create a swarmfile (e.g. job.swarm). For example:

cd dir1; step1_fitNULLGLMM.R --help 
cd dir2; step1_fitNULLGLMM.R --help

    

Submit this job using the swarm command.

swarm -f job.swarm [-g #] --module SAIGE

where

-g #	Number of Gigabytes of memory required for each process (1 line in the swarm command file)
--module	Loads the module for each subjob in the swarm