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Plant Cell. 2017 Jan;29(1):5-19. doi: 10.1105/tpc.16.00551. Epub 2016 Dec 16.

easyGWAS: A Cloud-Based Platform for Comparing the Results of Genome-Wide Association Studies.

Author information

1
Machine Learning and Computational Biology Research Group, Max Planck Institute for Intelligent Systems and Max Planck Institute for Developmental Biology, 72076 Tübingen, Germany dominik.grimm@bsse.ethz.ch karsten.borgwardt@bsse.ethz.ch.
2
Zentrum für Bioinformatik, Eberhard Karls Universität, 72074 Tübingen, Germany.
3
Department for Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland.
4
Swiss Institute of Bioinformatics, 4058 Basel, Switzerland.
5
Department of Molecular Biology, Max Planck Institute for Developmental Biology, 72076 Tübingen, Germany.
6
Machine Learning and Computational Biology Research Group, Max Planck Institute for Intelligent Systems and Max Planck Institute for Developmental Biology, 72076 Tübingen, Germany.
7
Department of Empirical Inference, Max Planck Institute for Intelligent Systems, 72076 Tübingen, Germany.

Abstract

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 data sets 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.

PMID:
27986896
PMCID:
PMC5304348
DOI:
10.1105/tpc.16.00551
[Indexed for MEDLINE]
Free PMC Article

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