What is GWAB?
Advances in human systems genetics enabled to identify tens of thousands genomic loci that are associated with many traits including complex diseases through genome-wide association studies (GWAS). Despite this initial success of GWAS, it is not the magic bullet for human genetics and suffer from several technical limitations like all other methods. One major shortcoming of GWAS is limited statistical power, partly due to the testing numerous hypotheses simultaneously (i.e., testing significance of association of more than million SNPs in a study). In most association studies, no more than a dozen of SNPs pass a conventional significant threshold (e.g., P ≤ 5x10-8) to be reported as confident candidate genes. This limitation can be partly overcome by increasing sample size, which is an expensive option. Alternatively, weak association signals can be boosted by integrating independent functional information such as molecular interactions. In our previous study, we demonstrated that trait-associated genes with sub-threshold significance score can be rescued by network connections to other significant candidates (Genome Research 2011 Jul; 21(7):1109-21). Recently, more researchers started to release whole summary statistics data of their GWAS, thus our methods would be more extensively used. Therefore, we have constructed a web server for the network-based boosting of human GWAS data, GWAB (genome-wide association boosting).
Major features of GWAB

GWAB takes summary statistics (i.e., p-value for SNPs) and known trait-associated genes (derived from disease annotation databases) as user input data, and then reprioritizes genes from GWAS with the following functional features.

  1. GWAB internally uses an improved version of HumanNet, which includes 93% of the human coding genome.
  2. GWAB assigns p-values of SNPs to genes located within a user-defined genomic distance (within 10kbp by default setting) to conduct gene-centric boosting.
  3. GWAB allows to analyze GWAS data based on both genome build, hg18 and hg19.
  4. GWAB automatically searches for optimal p-value threshold for boosting within a user-defined range (10-6 < p-value < 10-2), and outputs a list of candidate genes based on the optimal p-value threshold.
  5. GWAB completes analysis in an hour for most GWAS set and returns results.
  6. GWAB allows users to monitor job status.
  7. GWAB reports the boosting results with a summary plot which shows performances (by areas under the ROC curve) of GWAB, original GWAS data alone, and randomized networks for a given range of p-value threshold.
  8. GWAB serves pre-calculated predictions for seven published studies.
How to cite GWAB

Shim JE, Bang C, Yang S, Lee T, Hwang S, Kim CY, Singh-Blom UM, Marcotte EM, Lee I. GWAB: a web server for network-based boosting of human genome-wide association data. Nucleic Acids Research, Volume 45, Issue W1, 3 July 2017, Pages W154-W161.
DOI: 10.1093/nar/gkx284 | PubMed: 28449091

Contact information

GWAB was developed by Lee lab at Yonsei University, Korea.
If you have any question or comment, please contact insuklee(at)yonsei.ac.kr


2016.10.28 GWAB web-server launched.


This project was supported by the National Research Foundation of Korea grant.
(2012M3A9B4028641, 2012M3A9C7050151, 2015R1A2A1A15055859)