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

Grimm, D. G., Roqueiro, D., Salome, P. A., Kleeberger, S., Greshake, B., Zhu, W., et al. (2017). easyGWAS: A Cloud-Based Platform for Comparing the Results of Genome-Wide Association Studies. Plant Cell, 29(1), 5-19. doi:10.1105/tpc.16.00551.

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 Creators:
Grimm, Dominik G.1, 2, 3, 4, 5, Author
Roqueiro, Damian4, 5, Author
Salome, Patrice A.1, Author
Kleeberger, Stefan1, 2, Author
Greshake, Bastian1, 2, Author
Zhu, Wangsheng1, Author
Liu, Chang1, Author
Lippert, Christoph1, 2, Author
Stegle, Oliver1, 2, Author
Schoelkopf, Bernhard2, Author
Weigel, Detlef1, Author
Borgwardt, Karsten M.1, 2, 3, 4, 5, Author
Affiliations:
1Max Planck Institute for Developmental Biology, Max Planck Society, Max-Planck-Ring 5, 72076 Tübingen, DE, ou_2421691              
2Max Planck Institute for Intelligent Systems, Max Planck Society, Heisenbergstr. 3 70569 Stuttgart , DE, ou_1497638              
3Zentrum für Bioinformatik, Eberhard Karls Universität, Tübingen, ou_persistent22              
4Department for Biosystems Science and Engineering, ETH Zürich, ou_persistent22              
5Swiss Institute of Bioinformatics, Basel, ou_persistent22              

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Free keywords: FLOWERING-LOCUS-C; QUANTITATIVE TRAIT LOCI; FALSE DISCOVERY RATE; LINEAR MIXED MODELS; ARABIDOPSIS-THALIANA; DROSOPHILA-MELANOGASTER; SUSCEPTIBILITY LOCI; NATURAL VARIATION; GENETIC ARCHITECTURE; POPULATION-STRUCTURE
 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.

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Language(s): eng - English
 Dates: 2017
 Publication Status: Issued
 Pages: 15
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: ISI: 000394129700004
DOI: 10.1105/tpc.16.00551
 Degree: -

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Title: Plant Cell
Source Genre: Journal
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Publ. Info: Rockville, MD : American Society of Plant Physiologists
Pages: - Volume / Issue: 29 (1) Sequence Number: - Start / End Page: 5 - 19 Identifier: ISSN: 1040-4651
CoNE: https://pure.mpg.de/cone/journals/resource/954925588345