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Detalle de: MetaGenyo: a web tool for meta-analysis of genetic association studies

Title MetaGenyo: a web tool for meta-analysis of genetic association studies
Abstract

Background Genetic association studies (GAS) aims to evaluate the association between genetic variants and phenotypes. In the last few years, the number of this type of study has increased exponentially, but the results are not always reproducible due to experimental designs, low sample sizes and other methodological errors. In this field, meta-analysis techniques are becoming very popular tools to combine results across studies to increase statistical power and to resolve discrepancies in genetic association studies. A meta-analysis summarizes research findings, increases statistical power and enables the identification of genuine associations between genotypes and phenotypes. Meta-analysis techniques are increasingly used in GAS, but it is also increasing the amount of published meta-analysis containing different errors. Although there are several software packages that implement meta-analysis, none of them are specifically designed for genetic association studies and in most cases their use requires advanced programming or scripting expertise. Results We have developed MetaGenyo, a web tool for meta-analysis in GAS. MetaGenyo implements a complete and comprehensive workflow that can be executed in an easy-to-use environment without programming knowledge. MetaGenyo has been developed to guide users through the main steps of a GAS meta-analysis, covering Hardy-Weinberg test, statistical association for different genetic models, analysis of heterogeneity, testing for publication bias, subgroup analysis and robustness testing of the results. Conclusions MetaGenyo is a useful tool to conduct comprehensive genetic association meta-analysis. The application is freely available at http://bioinfo.genyo.es/metagenyo/ .

Apa Reference Martorell-Marugan, J., Toro-Dominguez, D., Alarcon-Riquelme, M.E. & Carmona-Saez, P. (2017). MetaGenyo: a web tool for meta-analysis of genetic association studies. BMC Bioinformatics, 18(1). 10.1186/s12859-017-1990-4
Doi 10.1186/s12859-017-1990-4
Publication Date Dec 2017
Publication Type Article
Researcher Pedro Carmona-Saez
Ontology Bioinformatics Software and Tools, Web-service, Experimental design
Research Groups
Tools

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