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

Jung Eun Shim, Changbae Bang, Sunmo Yang, Tak Lee, Sohyun Hwang, Chan Yeong Kim, U. Martin Singh-Blom, Edward M. Marcotte, Insuk Lee

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


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 â '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 (, 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.

Original languageEnglish
Pages (from-to)W154-W161
JournalNucleic acids research
Issue numberW1
Publication statusPublished - 2017 Jul 3

Bibliographical note

Funding Information:
National Research Foundation of Korea [2012M3A9B4 028641, 2012M3A9C7050151, 2015R1A2A1A15055859 to I.L.].

Publisher Copyright:
© The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

All Science Journal Classification (ASJC) codes

  • Genetics


Dive into the research topics of 'GWAB: A web server for the network-based boosting of human genome-wide association data'. Together they form a unique fingerprint.

Cite this