GWAMA: software for genome-wide association meta-analysis

• Published in: BMC bioinformatics Vol. 11; no. 1; p. 288
• Main Authors: Mägi, Reedik, Morris, Andrew P
• Format: Journal Article
• Language: English
• Published: London BioMed Central 28.05.2010; BioMed Central Ltd; BMC
• Subjects: Algorithms; Applications software; Bioinformatics; Biomedical and Life Sciences; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Databases, Genetic; Genome; Genome-Wide Association Study - methods; Genotype; Life Sciences; Microarrays; Multifactorial traits; Phenotype; Quantitative trait loci; Single nucleotide polymorphisms; Software; United Kingdom; Manhattan Plot; Allelic Effect; Wellcome Trust Case Control Consortium; Reference Allele; Reference Panel
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-11-288

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 .