GWAMA: Software for genome-wide association meta-analysis

Genetic and Genomic Epidemiology Unit, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.
BMC Bioinformatics (Impact Factor: 2.58). 05/2010; 11(1):288. DOI: 10.1186/1471-2105-11-288
Source: PubMed Central


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.
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.
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

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    • ", METAL ( Willer et al . , 2010 ) and GWAMA ( Mägi and Morris , 2010 ) ] . "
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    ABSTRACT: Results from numerous linkage and association studies have greatly deepened scientists’ understanding of the genetic basis of many human diseases, yet some important questions remain unanswered. For example, although a large number of disease-associated loci have been identified from genome-wide association studies (GWAS) in the past 10 years, it is challenging to interpret these results as most disease-associated markers have no clear functional roles in disease etiology, and all the identified genomic factors only explain a small portion of disease heritability. With the help of next-generation sequencing (NGS), diverse types of genomic and epigenetic variations can be detected with high accuracy. More importantly, instead of using linkage disequilibrium to detect association signals based on a set of pre-set probes, NGS allows researchers to directly study all the variants in each individual, therefore promises opportunities for identifying functional variants and a more comprehensive dissection of disease heritability. Although the current scale of NGS studies is still limited due to the high cost, the success of several recent studies suggests the great potential for applying NGS in genomic epidemiology, especially as the cost of sequencing continues to drop. In this review, we discuss several pioneer applications of NGS, summarize scientific discoveries for rare and complex diseases, and compare various study designs including targeted sequencing and whole-genome sequencing using population-based and family-based cohorts. Finally, we highlight recent advancements in statistical methods proposed for sequencing analysis, including group-based association tests, meta-analysis techniques, and annotation tools for variant prioritization.
    Frontiers in Genetics 04/2015; 6(149). DOI:10.3389/fgene.2015.00149
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    • "As the UK and Australian datasets were drawn from different populations and modeled with different fixed effects, the data were integrated through a meta-analysis. A fixed effect inverse variance meta-analysis was carried out on all approximately 11 M bins of the UK and Australian datasets, using GWAMA [61]. P-values were Bonferoni adjusted to correct for multiple testing. "
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    ABSTRACT: Although genetic variation is believe to contribute to an individual's susceptibility to major depressive disorder, genome-wide association studies have not yet identified associations that could explain the full etiology of the disease. Epigenetics is increasingly believed to play a major role in the development of common clinical phenotypes, including major depressive disorder. Genome-wide MeDIP-Sequencing was carried out on a total of 50 monozygotic twin pairs from the UK and Australia that are discordant for depression. We show that major depressive disorder is associated with significant hypermethylation within the coding region of ZBTB20, and is replicated in an independent cohort of 356 unrelated case-control individuals. The twins with major depressive disorder also show increased global variation in methylation in comparison with their unaffected co-twins. ZBTB20 plays an essential role in the specification of the Cornu Ammonis-1 field identity in the developing hippocampus, a region previously implicated in the development of major depressive disorder. Our results suggest that aberrant methylation profiles affecting the hippocampus are associated with major depressive disorder and show the potential of the epigenetic twin model in neuro-psychiatric disease.
    Genome biology 04/2014; 15(4):R56. DOI:10.1186/gb-2014-15-4-r56 · 10.81 Impact Factor
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    • "bMeta-analysis P-value is reported from the fixed-effects model as implemented in GWAMA software (Mägi and Morris, 2010). Where significant heterogeneity is detected, the results from the random-effects model optimized to detect associations under heterogeneity are reported in parenthesis as implemented in MetaSoft software (Han and Eskin, 2011). "
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    Human Reproduction Update 03/2014; 20(5). DOI:10.1093/humupd/dmu015 · 10.17 Impact Factor
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