SurvivalGWAS_SV: analysis of GWAS of imputed genotypes with 'time-to-event' outcomes
SurvivalGWAS_SV is an easy to use software that is able to handle large scale genome-wide data, allowing for imputed genotypes by modelling time to event outcomes under a dosage model. The software can adjust for multiple covariates and incorporate SNP-covariate interaction effects.
References:
- Hamzah Syed, Andrea L. Jorgensen and Andrew P. Morris
SurvivalGWAS_SV: software for the analysis of genome-wide association studies of imputed genotypes with “time-to-event” outcomes, BMC Bioinformatics ,
(2017) 18:265, DOI 10.1186/s12859-017-1683-z
Documentation
Important Notes
- Module Name: SurvivalGWAS_SV (see the modules page for more information)
- Unusual environment variables set
- SGWASSV_HOME installation directory
- SGWASSV_BIN executable directory
- SGWASSV_DATA sample data directory
Interactive job
Interactive jobs should be used for debugging, graphics, or applications that cannot be run as batch jobs.
Allocate an interactive session and run the program. Sample session:
[user@biowulf]$ sinteractive [user@cn2389 ~]$ module load SurvivalGWAS_SV [+] Loading mono 5.18.1 [+] Loading SurvivalGWAS_SV 1.3.2Output the usage message:
[user@cn2389 ~]$ sgwas_sw -h Hello, Welcome to SurvivalGWAS_SV $-------------------------------------------------------------------------------------------$ | June, 2016 | |-------------------------------------------------------------------------------------------| | (C) 2016 Hamzah Syed, Andrea L Jorgensen & Andrew P Morris | | GNU General Public License, v3 | |-------------------------------------------------------------------------------------------| | SurvivalGWAS_SV - Genome-wide association study analysis of imputed genotypes | | with time-to-event outcomes | | | | This single variant analytics tool is part of the SurvivalGWAS Suite | | | | For documentation, citation & bug-report instructions: | |https://www.liverpool.ac.uk/translational-medicine/research/statistical-genetics/software/ | $-------------------------------------------------------------------------------------------$ --gf, --gen_file=VALUE The name of the genotype file --sf, --sample_file=VALUE The name of the sample file. -t, --time=VALUE The observation time -c, --censor=VALUE The censoring indicator --cov, --covariates=VALUE A list of covariates. Each one seperated by a comma (,) -i, --int=VALUE The interaction between SNP and one covariate. Seperate using a comma (,) --lstart, --linestart=VALUE Specify line in file start position for more efficient program runtime --lstop, --linestop=VALUE Specify line in file stop position for more efficient program runtime --sp, --start_position=VALUE The start position on the chromosome. You still need to specify the number of lines in file using -lstart & -lstop commands --ep, --end_position=VALUE The stop position on the chromosome. -sp & -ep commands are substantially slower than using the -lstart & -lstop on their own --chr, --chromosome=VALUE User specified chromosome number -p, --print=VALUE Enter 'onlysnp' if you want only the SNP analysis output to be in the output file and 'onlyint' if you want only the interaction analysis output to be in the output file -m, --method=VALUE Specify choice of method for analysis -o, --output=VALUE Name of file for output to be saved in --threads, --cpu=VALUE Number of threads. On a multi-core system, multiple threads can execute tasks in parallel, with each core executing a different thread -h, --help Command HelpCopy sample data to the current folder:
[user@cn2389 ~]$ cp $SGWASSW_DATA/* .Run the application on the sample data:
[user@cn2389 ~]$ sgwas_sw --gen_file=./exchr10.out.controls.gen --sample_file=./samplefile.txt --threads=2 --method=cox --lstart=1 --lstop=25 --output=output.txt -t=event_times -c=censoring -cov=covariate1,covariate2End the interctive sesssion:
[user@cn2389 ~]$ exit [user@biowulf ~]$