GWAMA - Genome-Wide Association Meta Analysis

       GWAMA was written by Reedik Magi and Andrew P Morris.

2.2.2                                              August 2016                                          GWAMA(1)

       GWAMA  (Genome-Wide  Association  Meta  Analysis)  software  performs meta-analysis of the results of GWA
       studies of binary or quantitative phenotypes. Fixed- and random-effect meta-analyses  are  performed  for
       both  directly  genotyped  and  imputed SNPs using estimates of the allelic odds ratio and 95% confidence
       interval for binary traits, and estimates of the allelic effect size and standard error for  quantitative
       phenotypes.  GWAMA can be used for analysing the results of all different genetic models (multiplicative,
       additive, dominant, recessive). The software incorporates error trapping facilities  to  identify  strand
       alignment errors and allele flipping, and performs tests of heterogeneity of effects between studies.

       GWAMA - Genome-Wide Association Meta Analysis

-i, --filelist=filename
              Specify studies' result files. Default = gwama.in

       -o, --output=fileroot
              Specify file root for output of analysis. Default = gwama (gwama.out, gwama.gc.out)

       -r, --random
              Use random effect correction. Default = disabled

       -gc, --genomic_control
              Use genomic control for adjusting studies' result files. Default = disabled

       -gco, --genomic_control_output
              Use  genomic  control  on  meta-analysis summary (i.e. results of meta- analysis are corrected for
              gc). Default = disabled

       -qt, --quantitative
              Select quantitative trait version (BETA and SE columns). Default = binary trait

       -m, --map=filename
              Select file name for marker map.

       -t, --threshold=0-1
              The p-value threshold for showing direction in summary effect directions. Default = 1

       --no_alleles
              No allele information has been given. Expecting always the same EA.

       --indel_alleles
              Allele labes might contain more than single letter. No strand checks.

       --sex  Run gender-differentiated and gender- heterogeneity analysis  (method  described  in  paper  Magi,
              Lindgren  & Morris 2010). Gender info must be provided in filelist file. (second column after file
              names is either M or F).

       --filter
              Set  a  filtering  based  on  column  header.  It  needs  3  arguments:  column   name,   equation
              [>,<,>=,<=,==,!=], filter value. Multiple filters can be set.

       --name_marker
              Alternative header to marker name col.

       --name_strand
              Alternative header to strand column.

       --name_n
              Alternative header to sample size col.

       --name_ea
              Alternative header to effect allele column.

       --name_nea
              Alternative header to non-effect allele column.

       --name_eaf
              Alternative header to effect allele frequency column.

       --name_beta
              Alternative header to beta column.

       --name_se
              Alternative header to std. err. col.

       --name_or
              Alternative header to OR column.

       --name_or_95l
              Alternative header to OR 95L column.

       --name_or_95u
              Alternative header to OR 95U column.

       -h, --help
              Print this help.

       -v, --version
              Print GWAMA version number.  This manual page must be insufficient. Please use the help option for
              a quick reminder of the options of gwama or turn to its home page with the online documentation or
              the downloadable manual.

http://www.geenivaramu.ee/en/tools/gwama

GWAMA [options]