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GWAMA: software for genome-wide association meta-analysis.

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.

Pubmed ID: 20509871


  • M├Ągi R
  • Morris AP


BMC bioinformatics

Publication Data

June 30, 2010

Associated Grants

  • Agency: Wellcome Trust, Id: 064890
  • Agency: Wellcome Trust, Id: 081682
  • Agency: Wellcome Trust, Id: WT081682/Z/06/Z

Mesh Terms

  • Databases, Genetic
  • Genome
  • Genome-Wide Association Study
  • Genotype
  • Phenotype
  • Software