GWAMA: software for genome-wide association meta-analysis

被引：431

作者：

Magi, Reedik ^{[1
,2
]}

Morris, Andrew P. ^{[1
]}

机构：

[1] Univ Oxford, Wellcome Trust Ctr Human Genet, Genet & Genom Epidemiol Unit, Oxford OX3 7BN, England

[2] Univ Oxford, Churchill Hosp, Oxford Ctr Diabet Endocrinol & Metab, Oxford OX3 7LJ, England

来源：

BMC BIOINFORMATICS | 2010年 / 11卷

基金：

英国惠康基金;

关键词：

HETEROGENEITY; IMPUTATION;

D O I：

10.1186/1471-2105-11-288

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

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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