easyGWAS: A Cloud-based Platform for Comparing the Results of Genome-wide Association Studies
easyGWAS is a novel web- and cloud platform for performing genome-wide association studies (GWAS) and its comparison directly in the browser. For GWAS, advanced statistical tests and machine-learning algorithms are used to detect loci (features) within millions of genetic markers for only hundreds to thousands of individuals that are significantly associated with a phenotype of interest. However, these algorithms and methods are not always easy to use, because they either are command line tools or only collections of code fragments. We therefore, developed easyGWAS to free the user not only from the technical difficulties, but also from the complicated process of pre-processing, storing and visualising the data and its results. More details about easyGWAS can be found in its Publication at The Plant Cell.
The ever-growing availability of high quality genotypes for a multitude of species has enabled researchers to explore the underlying genetic architecture of complex phenotypes at an unprecedented level of detail using genome-wide association studies (GWAS). The systematic comparison of results obtained from GWAS of different traits opens up new possibilities, including the analysis of pleiotropic effects. Other advantages that result from the integration of multiple GWAS are the ability to replicate GWAS signals and to increase statistical power to detect such signals through meta-analyses. In order to facilitate the simple comparison of GWAS results, we present easyGWAS, a powerful, species-independent online resource for computing, storing, sharing, annotating and comparing GWAS. The easyGWAS tool supports multiple species, the uploading of private genotype data and summary statistics of existing GWAS, as well as advanced methods for comparing GWAS results across different experiments and datasets in an interactive and user-friendly interface. easyGWAS is also a public data repository for GWAS data and summary statistics, and already includes published data and results from several major GWAS. We demonstrate the potential of easyGWAS with a case study of the model organism Arabidopsis thaliana, using flowering and growth-related traits.
Our ressource can be accessed via https://easygwas.tuebingen.mpg.de
- easyGWAS: A cloud-based platform for comparing the results of genome-wide association studies.
DG Grimm, D Roqueiro, PA Salome, S Kleeberger, B Greshake, W Zhu, C Liu, C Lippert, O Stegle, B Schölkopf, D Weigel and KM Borgwardt
Accepted @ The Plant Cell (Link)