GWAMA: software for genome-wide association meta-analysis
By Reedik Mägi, Andrew P. Morris. BMC Bioinformatics, 2010
609 citations recorded by the source corpus.
BackgroundDespite 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.ResultsWe 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.ConclusionsThe 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.
Subfields: Genetics
View Paper: https://www.semanticscholar.org/paper/4c4f38fbd276bc1bfee913d5ed2a0091a286fcda
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Genetics
- J. Thompson
- Evangelos Evangelou
- Sekar Kathiresan
- Hua Tang
- Michael Inouye
- Paul Elliott
- Oliver Stegle
- Albert V. Smith
Citations in the corpus
- A comprehensive 1000 Genomes-based genome-wide association meta-analysis of coronary artery disease (2015, positive): “Variants that were retained in at least 60% of the studies were submitted for meta-analysis using the GWAMA program 63 .”
- The Polygenic and Monogenic Basis of Blood Traits and Diseases (2020, methodology): “Whole-genome sequencing of rare disease patients in a national healthcare system (bioRxiv) https://www.genomicsengland.co.uk/ about-gecip/joining-researchcommunity/ Ensembl v99 EMBL-EBI https://www.ensembl.org/index.html; HOCOMOCO (Kulakovskiy et al., 2018) https://repository.kaust.edu.sa/handle/ 10”