Journal
BMC BIOINFORMATICS
Volume 11, Issue -, Pages -Publisher
BMC
DOI: 10.1186/1471-2105-11-288
Keywords
-
Categories
Funding
- Wellcome Trust [WT081682/Z/06/Z]
- ENGAGE consortium, European Community [FP7/2007-2013]
- ENGAGE [HEALTH-F4_2007-201413]
- European Commission
Ask authors/readers for more resources
Background: 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 meta-analysis of studies from the same population, increasing the sample size over any individual study. Although statistical software analysis packages incorporate routines for meta-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 meta-analysis 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 http://www.well.ox.ac.uk/GWAMA.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available