GWAMA: software tool for meta analysis of whole genome association dataINTRODUCTIONGWAMA (Genome-Wide Association Meta Analysis) software has been developed to perform meta-analysis of the results of GWA studies of binary
INSTALLATION (UNIX)Copy gwama.zip file into your computer, unzip the file: unzip gwama.zip To compile GWAMA program, use command: make in the folder where files have been unpacked. The program can be run by typing: GWAMA
INSTALLATION (WINDOWS)Copy gwama.zip file into your computer and unpack the *.msi file. Double-click on the *.msi file and follow the installation instructions. The program can be run by typing: c:\Program Files\WTCHG\gwama\gwama (if installed into default folder)
INPUT FILESFor running GWAMA you have to create an input file (default name “gwama.in”), which contains the list of all study files. The should have each results' file on separate row. If genderwise heterogeneity analysis option is used, second column should identify if the cohort contains males (M) or females (F) data. Sample “gwama.in” file:
Each GWA study file has mandatory column headers: 1) MARKER – snp name 2) EA – effect allele 3) NEA – non effect allele 4) OR - odds ratio 5) OR_95L - lower confidence interval of OR 6) OR_95U - upper confidence interval of OR In case of quantitative trait: 4) BETA – beta 5) SE – std. error Study files might also contain columns: 7) N - number of samples 8) EAF – effect allele frequency 9) STRAND – marker strand (if the column is missing then program expects all markers being on positive strand) 10) IMPUTED – if marker is imputed or not (if the column is missing then all markers are counted as directly genotyped ones)
Sample study file (NB! This file is a quantitative trait one and GWAMA has to be run with -qt command line option):
RUNNING GWAMACommand line options: GWAMA --filelist {filename} or -i {filename} Specify studies' result files. Default = gwama.in --output {fileroot} or -o {fileroot} Specify file root for output of analysis. Default = gwama (gwama.out, gwama.gc.out) --random or -r Use random effect correction. Default = disabled --genomic_control or -gc Use genomic control for adjusting studies' result files. Default = disabled --genomic_control_output or -gco Use genomic control on meta-analysis summary (i.e. results of meta- analysis are corrected for gc). Default = disabled --quantitative or -qt Select quantitative trait version (BETA and SE columns). Default = binary trait --map {filename} or -m {filename} Select file name for marker map. --threshold {0-1} or -t {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). --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 --help or -h Print this help --version or -v Print GWAMA version number
OUTPUT FILES GWAMA generates following output files:
gwama.out (or 'fileroot'.out if --output option is used) gwama.gc.out (or 'fileroot'.gc.out if --output option is used) gwama.log.out gwama.err.out GENDER SPECIFIC ANALYSIS If gender specific analysis option is used, additional columns will appear into output file. All male_ and female_ columns are calculated using cohorts with defined gender.
Paper describing gender specific analysis framework has beed submitted.
CREATING PLOTSManhattan and QQ plots can be created with accompanied R scripts. R --slave --vanilla < MANH.R R --slave --vanilla < QQ.R By default they expect input file name "gwama.out" and they create output files: "gwama.out.qq.png" and "gwama.out.manh.png". Different names can be used as: R --slave --vanilla --args input=inputfilename out=outputfilename < QQ.R Manhattan plot can be drawn, if chromosomal position have been added to the file (for example command line: --map hapmap35.map) R version 2.9.0 or later must be used with png support
CITING REFERENCESMagi R, Morris AP: GWAMA: software for genome-wide association meta-analysis. BMC Bioinformatics 2010, 11:288. Magi R, Lindgren CM, Morris AP: Meta-analysis of sex-specific genome-wide association studies. Genetic Epidemiology 2010, 34(8):846-853.
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