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GWAB: a web server for the network-based boosting of human genome-wide association data
 Authors and Affiliations  Authors and Affiliations
Jung Eun Shim1, Changbae Bang1, Sunmo Yang1, Tak Lee1, Sohyun Hwang2, Chan Yeong Kim1, U. Martin Singh-Blom3, Edward M. Marcotte4,5 and Insuk Lee1,*
1Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 120-749, Korea, 2Department of Biomedical Science, College of Life Science, CHA University, Seongnam-si 13496, Korea, 3Cognition Group, Schibsted Products & Technologies, Västra Järnvägsgatan 21, 111 64 Stockholm, Sweden, 4Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas, Austin, TX 78712, USA and 5Department of Molecular Biosciences, University of Texas at Austin, TX 78712, USA
*To whom correspondence should be addressed. 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. |
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