AIDS, HIV, Complex diseases, Genome-wide association studies (GWAS), Whole genome sequencing (WGS)
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.
Svitin, Anton; Sergey Malov; Nikolay Cherkasov; Paul Geerts; Mikhail Rotkevich; Pavel Dobrynin; Andrey Shevchenko; Li Guan; Jennifer L. Troyer; Sher L. Hendrickson; Holli Hutcheson Dilks; T. K. Oleksyk; Sharyne Donfield; Edward Gomperts; Douglas A. Jabs; Efe Sezgin; Mark Van Natta; P. Richard Harrigan; Zabrina L. Brumme; and Stephen J. O'Brien. 2014. "GWATCH: A Web Platform for Automated Gene Association Discovery Analysis." GigaScience 3, (18): 1-10. https://nsuworks.nova.edu/cnso_bio_facarticles/738