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MetaGWASToolKit is a ToolKit to perform a Meta-analysis of Genome-Wide Association Studies (GWAS); it can be used in conjunction with GWASToolKit. Thus, MetaGWASToolKit works flawlessly in a pipeline from imputation with IMPUTE2, datamanagement with QCTOOL2+, to analysis using SNPTEST2.5+.

MetaGWASToolKit will (semi-)automatically perform a meta-analysis of genome-wide association studies. It will reformat, harmonise data, clean, plot, and analyze the data based on some required user-specificied configuration settings. Relevant statistics, such as HWE, minor allele count (MAC), and coded allele frequency (CAF) will also be added to the final summarized result. MetaGWASToolKit is semi-automatically in that the user is required to review intermediate files, i.e. diagnostic figures, before performing the final automatic meta-analysis. The QC and reporting is based on the papers by Winkler T.W. et al. and De Bakker PIW et al..

The main script, metagwastoolkit.qsub.sh, is controlled and configured through:

• metagwastoolkit.conf, a file to configure the meta-analysis (dataset locations, analysis name, reference, cleaning thresholds, etc.)
• metagwastoolkit.list, a list of all the files to be analysed, i.e. the individual datasets contributing to the meta-analysis, and
• metagwastoolkit.param, a parameter file to set prior to meta-analysis and based on the cleaned GWAS datasets. Specifically, it should note a correction factor if units of measurements are not the same (e.g. ng/mL vs. mg/mL), and the lambda-statistic obtained after cleaning the individual GWAS datasets.

MetaGWASToolKit is based on MANTEL, originally written by Sara Pulit, Jessica van Setten, and Paul de Bakker, and described in De Bakker PIW et al.. The main improvements are the use of 1000G as a reference, an automatic method to assess the type of variant identification (e.g. rs-number or chr1:1234:A_B) used, and the handling of INDELs.

Scripts will work within the context of a server-based Linux environment (in this case a CentOS7 system on a SUN Grid Engine background) with a job submission-system, i.e. qsub. In addition, we have tested many scripts on OS X Sierra (version 10.11.[x]) too: most R-, Perl, and Python-scripts should work there too.

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