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Link to original content: https://pubmed.ncbi.nlm.nih.gov/20509871/
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Meta-Analysis
. 2010 May 28:11:288.
doi: 10.1186/1471-2105-11-288.

GWAMA: software for genome-wide association meta-analysis

Reedik Mägi  1 , Andrew P Morris
Affiliations
Meta-Analysis

GWAMA: software for genome-wide association meta-analysis

Reedik Mägi et al. BMC Bioinformatics. .

Abstract

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.

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Figures

Figure 1
Figure 1
QQ and Manhattan plots generated from GWAMA output using the summary R scripts released with the software.
See this image and copyright information in PMC

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