Literature DB >> 28449091

GWAB: a web server for the network-based boosting of human genome-wide association data.

Jung Eun Shim1, Changbae Bang1, Sunmo Yang1, Tak Lee1, Sohyun Hwang2, Chan Yeong Kim1, U Martin Singh-Blom3, Edward M Marcotte4,5, Insuk Lee1.   

Abstract

During the last decade, genome-wide association studies (GWAS) have represented a major approach to dissect complex human genetic diseases. Due in part to limited statistical power, most studies identify only small numbers of candidate genes that pass the conventional significance thresholds (e.g. P ≤ 5 × 10-8). This limitation can be partly overcome by increasing the sample size, but this comes at a higher cost. Alternatively, weak association signals can be boosted by incorporating independent data. Previously, we demonstrated the feasibility of boosting GWAS disease associations using gene networks. Here, we present a web server, GWAB (www.inetbio.org/gwab), for the network-based boosting of human GWAS data. Using GWAS summary statistics (P-values) for SNPs along with reference genes for a disease of interest, GWAB reprioritizes candidate disease genes by integrating the GWAS and network data. We found that GWAB could more effectively retrieve disease-associated reference genes than GWAS could alone. As an example, we describe GWAB-boosted candidate genes for coronary artery disease and supporting data in the literature. These results highlight the inherent value in sub-threshold GWAS associations, which are often not publicly released. GWAB offers a feasible general approach to boost such associations for human disease genetics.
© The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

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Year:  2017        PMID: 28449091      PMCID: PMC5793838          DOI: 10.1093/nar/gkx284

Source DB:  PubMed          Journal:  Nucleic Acids Res        ISSN: 0305-1048            Impact factor:   16.971


  40 in total

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Journal:  Nat Genet       Date:  2011-03-06       Impact factor: 38.330

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Authors:  Carl A Anderson; Gabrielle Boucher; Charlie W Lees; Andre Franke; Mauro D'Amato; Kent D Taylor; James C Lee; Philippe Goyette; Marcin Imielinski; Anna Latiano; Caroline Lagacé; Regan Scott; Leila Amininejad; Suzannah Bumpstead; Leonard Baidoo; Robert N Baldassano; Murray Barclay; Theodore M Bayless; Stephan Brand; Carsten Büning; Jean-Frédéric Colombel; Lee A Denson; Martine De Vos; Marla Dubinsky; Cathryn Edwards; David Ellinghaus; Rudolf S N Fehrmann; James A B Floyd; Timothy Florin; Denis Franchimont; Lude Franke; Michel Georges; Jürgen Glas; Nicole L Glazer; Stephen L Guthery; Talin Haritunians; Nicholas K Hayward; Jean-Pierre Hugot; Gilles Jobin; Debby Laukens; Ian Lawrance; Marc Lémann; Arie Levine; Cecile Libioulle; Edouard Louis; Dermot P McGovern; Monica Milla; Grant W Montgomery; Katherine I Morley; Craig Mowat; Aylwin Ng; William Newman; Roel A Ophoff; Laura Papi; Orazio Palmieri; Laurent Peyrin-Biroulet; Julián Panés; Anne Phillips; Natalie J Prescott; Deborah D Proctor; Rebecca Roberts; Richard Russell; Paul Rutgeerts; Jeremy Sanderson; Miquel Sans; Philip Schumm; Frank Seibold; Yashoda Sharma; Lisa A Simms; Mark Seielstad; A Hillary Steinhart; Stephan R Targan; Leonard H van den Berg; Morten Vatn; Hein Verspaget; Thomas Walters; Cisca Wijmenga; David C Wilson; Harm-Jan Westra; Ramnik J Xavier; Zhen Z Zhao; Cyriel Y Ponsioen; Vibeke Andersen; Leif Torkvist; Maria Gazouli; Nicholas P Anagnou; Tom H Karlsen; Limas Kupcinskas; Jurgita Sventoraityte; John C Mansfield; Subra Kugathasan; Mark S Silverberg; Jonas Halfvarson; Jerome I Rotter; Christopher G Mathew; Anne M Griffiths; Richard Gearry; Tariq Ahmad; Steven R Brant; Mathias Chamaillard; Jack Satsangi; Judy H Cho; Stefan Schreiber; Mark J Daly; Jeffrey C Barrett; Miles Parkes; Vito Annese; Hakon Hakonarson; Graham Radford-Smith; Richard H Duerr; Séverine Vermeire; Rinse K Weersma; John D Rioux
Journal:  Nat Genet       Date:  2011-02-06       Impact factor: 38.330

8.  A new methodology to associate SNPs with human diseases according to their pathway related context.

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Journal:  PLoS One       Date:  2011-10-25       Impact factor: 3.240

9.  Serum TGF-β1 and SMAD3 levels are closely associated with coronary artery disease.

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10.  Understanding multicellular function and disease with human tissue-specific networks.

Authors:  Casey S Greene; Arjun Krishnan; Aaron K Wong; Emanuela Ricciotti; Rene A Zelaya; Daniel S Himmelstein; Ran Zhang; Boris M Hartmann; Elena Zaslavsky; Stuart C Sealfon; Daniel I Chasman; Garret A FitzGerald; Kara Dolinski; Tilo Grosser; Olga G Troyanskaya
Journal:  Nat Genet       Date:  2015-04-27       Impact factor: 38.330

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3.  Integrating Coexpression Networks with GWAS to Prioritize Causal Genes in Maize.

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4.  Improving stability of prediction models based on correlated omics data by using network approaches.

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5.  araGWAB: Network-based boosting of genome-wide association studies in Arabidopsis thaliana.

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6.  HumanNet v2: human gene networks for disease research.

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Journal:  Nucleic Acids Res       Date:  2019-01-08       Impact factor: 16.971

7.  A Fast and Flexible Framework for Network-Assisted Genomic Association.

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Journal:  iScience       Date:  2019-05-24

8.  Network-based genetic investigation of virulence-associated phenotypes in methicillin-resistant Staphylococcus aureus.

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9.  The Integrated Landscape of Biological Candidate Causal Genes in Coronary Artery Disease.

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10.  Bottlenecks for genome-edited crops on the road from lab to farm.

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