Biowulf High Performance Computing at the NIH
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:

Documentation
Important Notes

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  1.3.2
Output 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 Help
Copy 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,covariate2
End the interctive sesssion:
[user@cn2389 ~]$ exit
[user@biowulf ~]$