Skip to main content
Article
GWATCH: A Web Platform for Automated Gene Association Discovery Analysis
GigaScience
  • Anton Svitin, St. Petersburg State University - Russia
  • Sergey Malov, St. Petersburg State University - Russia; St. Petersburg Electrotechnical University - Russia
  • Nikolay Cherkasov, St. Petersburg State University - Russia
  • Paul Geerts, Scientific Data Visualization Consultant
  • Mikhail Rotkevich, St. Petersburg State University - Russia
  • Pavel Dobrynin, St. Petersburg State University - Russia
  • Andrey Shevchenko, St. Petersburg State University - Russia
  • Li Guan, St. Petersburg State University - Russia
  • Jennifer L. Troyer, National Cancer Institute at Frederick
  • Sher L. Hendrickson, Shepherd University
  • Holli Hutcheson Dilks, Vanderbilt University
  • T. K. Oleksyk, University of Puerto Rico at Mayaguez
  • Sharyne Donfield, Rho, Inc.
  • Edward Gomperts, Children's Hospital of Los Angeles
  • Douglas A. Jabs, Mount Sinai School of Medicine
  • Efe Sezgin, Johns Hopkins University
  • Mark Van Natta, Johns Hopkins University
  • P. Richard Harrigan, BC Centre for Excellence in HIV/AIDS - Vancouver, Canada; University of British Columbia - Canada
  • Zabrina L. Brumme, Simon Fraser University - Canada
  • Stephen J. O'Brien, St. Petersburg State University - Russia; Nova Southeastern University
Document Type
Article
Publication Date
11-5-2014
Keywords
  • AIDS,
  • HIV,
  • Complex diseases,
  • Genome-wide association studies (GWAS),
  • Whole genome sequencing (WGS)
Abstract

Background: As genome-wide sequence analyses for complex human disease determinants are expanding, it is increasingly necessary to develop strategies to promote discovery and validation of potential disease-gene associations.

Findings: Here we present a dynamic web-based platform – GWATCH – that automates and facilitates four steps in genetic epidemiological discovery: 1) Rapid gene association search and discovery analysis of large genome-wide datasets; 2) Expanded visual display of gene associations for genome-wide variants (SNPs, indels, CNVs), including Manhattan plots, 2D and 3D snapshots of any gene region, and a dynamic genome browser illustrating gene association chromosomal regions; 3) Real-time validation/replication of candidate or putative genes suggested from other sources, limiting Bonferroni genome-wide association study (GWAS) penalties; 4) Open data release and sharing by eliminating privacy constraints (The National Human Genome Research Institute (NHGRI) Institutional Review Board (IRB), informed consent, The Health Insurance Portability and Accountability Act (HIPAA) of 1996 etc.) on unabridged results, which allows for open access comparative and meta-analysis.

Conclusions: GWATCH is suitable for both GWAS and whole genome sequence association datasets. We illustrate the utility of GWATCH with three large genome-wide association studies for HIV-AIDS resistance genes screened in large multicenter cohorts; however, association datasets from any study can be uploaded and analyzed by GWATCH.

Comments

© 2014 Svitin et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Additional Comments
Russian Ministry of Science grant #: 11.G34.31.0068; National Institute of Child Health and Human Development grant #: R01-HD-41224; National Eye Institute grant #s: U10EY008052, U10EY008057, U10EY008067
ORCID ID
0000-0001-7353-8301
ResearcherID
N-1726-2015
Citation Information
Anton Svitin, Sergey Malov, Nikolay Cherkasov, Paul Geerts, et al.. "GWATCH: A Web Platform for Automated Gene Association Discovery Analysis" GigaScience Vol. 3 Iss. 18 (2014) p. 1 - 10 ISSN: 2047-217X
Available at: http://works.bepress.com/stephen-obrien/268/