GWAMA: software for genome-wide association meta-analysis

Reedik Mägi, Andrew P Morris
2010 BMC Bioinformatics  
Despite the recent success of genome-wide association studies in identifying novel loci contributing effects to complex human traits, such as type 2 diabetes and obesity, much of the genetic component of variation in these phenotypes remains unexplained. One way to improving power to detect further novel loci is through metaanalysis of studies from the same population, increasing the sample size over any individual study. Although statistical software analysis packages incorporate routines for
more » ... eta-analysis, they are ill equipped to meet the challenges of the scale and complexity of data generated in genome-wide association studies. Results: We have developed flexible, open-source software for the meta-analysis of genome-wide association studies. The software incorporates a variety of error trapping facilities, and provides a range of meta-analysis summary statistics. The software is distributed with scripts that allow simple formatting of files containing the results of each association study and generate graphical summaries of genome-wide meta-analysis results. Conclusions: The GWAMA (Genome-Wide Association Meta-Analysis) software has been developed to perform metaanalysis of summary statistics generated from genome-wide association studies of dichotomous phenotypes or quantitative traits. Software with source files, documentation and example data files are freely available online at
doi:10.1186/1471-2105-11-288 pmid:20509871 pmcid:PMC2893603 fatcat:hb2y22ijajcgbbbaswlroykxoy