Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 120-749, Korea.
Department of Biomedical Science, College of Life Science, CHA University, Seongnam-si 13496, Korea.
Nucleic Acids Res. 2017 Jul 3;45(W1):W154-W161. doi: 10.1093/nar/gkx284.
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.
在过去的十年中，全基因组关联研究（GWAS）已成为解析复杂人类遗传疾病的主要方法。部分由于统计能力有限，大多数研究只能识别出少数通过传统显著性阈值（例如 P≤5×10-8）的候选基因。通过增加样本量可以部分克服这一限制，但成本更高。或者，可以通过合并独立数据来增强弱关联信号。此前，我们证明了使用基因网络增强 GWAS 疾病关联的可行性。在这里，我们提供了一个名为 GWAB（www.inetbio.org/gwab）的网络服务器，用于基于网络的人类 GWAS 数据增强。GWAB 使用 GWAS 汇总统计数据（SNP 的 P 值）和感兴趣疾病的参考基因，通过整合 GWAS 和网络数据，重新确定候选疾病基因的优先级。我们发现，GWAB 比 GWAS 更有效地检索到与疾病相关的参考基因。例如，我们描述了用于冠状动脉疾病的 GWAB 增强候选基因，并在文献中提供了支持数据。这些结果突出了亚阈值 GWAS 关联的内在价值，这些关联通常不会公开发布。GWAB 为增强人类疾病遗传学中的此类关联提供了一种可行的通用方法。
