Literature DB >> 27986896

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

Dominik G Grimm1,2,3,4, Damian Roqueiro3,4, Patrice A Salomé5, Stefan Kleeberger6, Bastian Greshake6, Wangsheng Zhu5, Chang Liu5, Christoph Lippert6, Oliver Stegle6, Bernhard Schölkopf7, Detlef Weigel5, Karsten M Borgwardt1,2,3,4.   

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.
© 2016 American Society of Plant Biologists. All rights reserved.

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Year:  2016        PMID: 27986896      PMCID: PMC5304348          DOI: 10.1105/tpc.16.00551

Source DB:  PubMed          Journal:  Plant Cell        ISSN: 1040-4651            Impact factor:   11.277


  71 in total

1.  Genomic control for association studies.

Authors:  B Devlin; K Roeder
Journal:  Biometrics       Date:  1999-12       Impact factor: 2.571

2.  Natural variation in the ATPS1 isoform of ATP sulfurylase contributes to the control of sulfate levels in Arabidopsis.

Authors:  Anna Koprivova; Marco Giovannetti; Patrycja Baraniecka; Bok-Rye Lee; Cécile Grondin; Olivier Loudet; Stanislav Kopriva
Journal:  Plant Physiol       Date:  2013-09-11       Impact factor: 8.340

3.  Principal components analysis corrects for stratification in genome-wide association studies.

Authors:  Alkes L Price; Nick J Patterson; Robert M Plenge; Michael E Weinblatt; Nancy A Shadick; David Reich
Journal:  Nat Genet       Date:  2006-07-23       Impact factor: 38.330

4.  Increased accuracy of artificial selection by using the realized relationship matrix.

Authors:  B J Hayes; P M Visscher; M E Goddard
Journal:  Genet Res (Camb)       Date:  2009-02       Impact factor: 1.588

5.  A basic introduction to fixed-effect and random-effects models for meta-analysis.

Authors:  Michael Borenstein; Larry V Hedges; Julian P T Higgins; Hannah R Rothstein
Journal:  Res Synth Methods       Date:  2010-11-21       Impact factor: 5.273

6.  The Drosophila melanogaster Genetic Reference Panel.

Authors:  Trudy F C Mackay; Stephen Richards; Eric A Stone; Antonio Barbadilla; Julien F Ayroles; Dianhui Zhu; Sònia Casillas; Yi Han; Michael M Magwire; Julie M Cridland; Mark F Richardson; Robert R H Anholt; Maite Barrón; Crystal Bess; Kerstin Petra Blankenburg; Mary Anna Carbone; David Castellano; Lesley Chaboub; Laura Duncan; Zeke Harris; Mehwish Javaid; Joy Christina Jayaseelan; Shalini N Jhangiani; Katherine W Jordan; Fremiet Lara; Faye Lawrence; Sandra L Lee; Pablo Librado; Raquel S Linheiro; Richard F Lyman; Aaron J Mackey; Mala Munidasa; Donna Marie Muzny; Lynne Nazareth; Irene Newsham; Lora Perales; Ling-Ling Pu; Carson Qu; Miquel Ràmia; Jeffrey G Reid; Stephanie M Rollmann; Julio Rozas; Nehad Saada; Lavanya Turlapati; Kim C Worley; Yuan-Qing Wu; Akihiko Yamamoto; Yiming Zhu; Casey M Bergman; Kevin R Thornton; David Mittelman; Richard A Gibbs
Journal:  Nature       Date:  2012-02-08       Impact factor: 49.962

7.  Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines.

Authors:  Susanna Atwell; Yu S Huang; Bjarni J Vilhjálmsson; Glenda Willems; Matthew Horton; Yan Li; Dazhe Meng; Alexander Platt; Aaron M Tarone; Tina T Hu; Rong Jiang; N Wayan Muliyati; Xu Zhang; Muhammad Ali Amer; Ivan Baxter; Benjamin Brachi; Joanne Chory; Caroline Dean; Marilyne Debieu; Juliette de Meaux; Joseph R Ecker; Nathalie Faure; Joel M Kniskern; Jonathan D G Jones; Todd Michael; Adnane Nemri; Fabrice Roux; David E Salt; Chunlao Tang; Marco Todesco; M Brian Traw; Detlef Weigel; Paul Marjoram; Justin O Borevitz; Joy Bergelson; Magnus Nordborg
Journal:  Nature       Date:  2010-03-24       Impact factor: 49.962

8.  Further improvements to linear mixed models for genome-wide association studies.

Authors:  Christian Widmer; Christoph Lippert; Omer Weissbrod; Nicolo Fusi; Carl Kadie; Robert Davidson; Jennifer Listgarten; David Heckerman
Journal:  Sci Rep       Date:  2014-11-12       Impact factor: 4.379

9.  AraPheno: a public database for Arabidopsis thaliana phenotypes.

Authors:  Ümit Seren; Dominik Grimm; Joffrey Fitz; Detlef Weigel; Magnus Nordborg; Karsten Borgwardt; Arthur Korte
Journal:  Nucleic Acids Res       Date:  2016-10-24       Impact factor: 16.971

10.  Efficient network-guided multi-locus association mapping with graph cuts.

Authors:  Chloé-Agathe Azencott; Dominik Grimm; Mahito Sugiyama; Yoshinobu Kawahara; Karsten M Borgwardt
Journal:  Bioinformatics       Date:  2013-07-01       Impact factor: 6.937

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  31 in total

1.  Massive haplotypes underlie ecotypic differentiation in sunflowers.

Authors:  Marco Todesco; Gregory L Owens; Natalia Bercovich; Jean-Sébastien Légaré; Shaghayegh Soudi; Dylan O Burge; Kaichi Huang; Katherine L Ostevik; Emily B M Drummond; Ivana Imerovski; Kathryn Lande; Mariana A Pascual-Robles; Mihir Nanavati; Mojtaba Jahani; Winnie Cheung; S Evan Staton; Stéphane Muños; Rasmus Nielsen; Lisa A Donovan; John M Burke; Sam Yeaman; Loren H Rieseberg
Journal:  Nature       Date:  2020-07-08       Impact factor: 49.962

2.  Linking Genomic and Metabolomic Natural Variation Uncovers Nematode Pheromone Biosynthesis.

Authors:  Jan M Falcke; Neelanjan Bose; Alexander B Artyukhin; Christian Rödelsperger; Gabriel V Markov; Joshua J Yim; Dominik Grimm; Marc H Claassen; Oishika Panda; Joshua A Baccile; Ying K Zhang; Henry H Le; Dino Jolic; Frank C Schroeder; Ralf J Sommer
Journal:  Cell Chem Biol       Date:  2018-05-17       Impact factor: 8.116

3.  Model-based integration of genomics and metabolomics reveals SNP functionality in Mycobacterium tuberculosis.

Authors:  Ove Øyås; Sonia Borrell; Andrej Trauner; Michael Zimmermann; Julia Feldmann; Thomas Liphardt; Sebastien Gagneux; Jörg Stelling; Uwe Sauer; Mattia Zampieri
Journal:  Proc Natl Acad Sci U S A       Date:  2020-03-30       Impact factor: 11.205

4.  Neuroimaging PheWAS (Phenome-Wide Association Study): A Free Cloud-Computing Platform for Big-Data, Brain-Wide Imaging Association Studies.

Authors:  Lu Zhao; Ishaan Batta; William Matloff; Caroline O'Driscoll; Samuel Hobel; Arthur W Toga
Journal:  Neuroinformatics       Date:  2021-04

Review 5.  Crop breeding for a changing climate: integrating phenomics and genomics with bioinformatics.

Authors:  Jacob I Marsh; Haifei Hu; Mitchell Gill; Jacqueline Batley; David Edwards
Journal:  Theor Appl Genet       Date:  2021-04-14       Impact factor: 5.699

6.  Genetic variation, environment and demography intersect to shape Arabidopsis defense metabolite variation across Europe.

Authors:  Ella Katz; Jia-Jie Li; Benjamin Jaegle; Haim Ashkenazy; Shawn R Abrahams; Clement Bagaza; Samuel Holden; Chris J Pires; Ruthie Angelovici; Daniel J Kliebenstein
Journal:  Elife       Date:  2021-05-05       Impact factor: 8.140

7.  Genetic architecture and adaptation of flowering time among environments.

Authors:  Wenjie Yan; Baosheng Wang; Emily Chan; Thomas Mitchell-Olds
Journal:  New Phytol       Date:  2021-02-25       Impact factor: 10.151

8.  Using precision phenotyping to inform de novo domestication.

Authors:  Alisdair R Fernie; Saleh Alseekh; Jie Liu; Jianbing Yan
Journal:  Plant Physiol       Date:  2021-07-06       Impact factor: 8.340

9.  Genetic basis and dual adaptive role of floral pigmentation in sunflowers.

Authors:  Marco Todesco; Natalia Bercovich; Amy Kim; Ivana Imerovski; Gregory L Owens; Óscar Dorado Ruiz; Srinidhi V Holalu; Lufiani L Madilao; Mojtaba Jahani; Jean-Sébastien Légaré; Benjamin K Blackman; Loren H Rieseberg
Journal:  Elife       Date:  2022-01-18       Impact factor: 8.140

10.  Current Scope and Challenges in Phenome-Wide Association Studies.

Authors:  Anurag Verma; Marylyn D Ritchie
Journal:  Curr Epidemiol Rep       Date:  2017-11-02
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