Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, 262 Seongsanno, Seodaemun-gu, Seoul, Korea.
Genome Res. 2011 Jul;21(7):1109-21. doi: 10.1101/gr.118992.110. Epub 2011 May 2.
Network "guilt by association" (GBA) is a proven approach for identifying novel disease genes based on the observation that similar mutational phenotypes arise from functionally related genes. In principle, this approach could account even for nonadditive genetic interactions, which underlie the synergistic combinations of mutations often linked to complex diseases. Here, we analyze a large-scale, human gene functional interaction network (dubbed HumanNet). We show that candidate disease genes can be effectively identified by GBA in cross-validated tests using label propagation algorithms related to Google's PageRank. However, GBA has been shown to work poorly in genome-wide association studies (GWAS), where many genes are somewhat implicated, but few are known with very high certainty. Here, we resolve this by explicitly modeling the uncertainty of the associations and incorporating the uncertainty for the seed set into the GBA framework. We observe a significant boost in the power to detect validated candidate genes for Crohn's disease and type 2 diabetes by comparing our predictions to results from follow-up meta-analyses, with incorporation of the network serving to highlight the JAK-STAT pathway and associated adaptors GRB2/SHC1 in Crohn's disease and BACH2 in type 2 diabetes. Consideration of the network during GWAS thus conveys some of the benefits of enrolling more participants in the GWAS study. More generally, we demonstrate that a functional network of human genes provides a valuable statistical framework for prioritizing candidate disease genes, both for candidate gene-based and GWAS-based studies.
基于相似的突变表型源于功能相关基因的观察，网络“关联罪责”（GBA）是一种已被证明的识别新疾病基因的方法。原则上，即使对于非加性遗传相互作用，这种方法也可以解释，非加性遗传相互作用是导致与复杂疾病相关的突变协同组合的基础。在这里，我们分析了一个大规模的人类基因功能相互作用网络（称为 HumanNet）。我们表明，通过使用与谷歌的 PageRank 相关的标签传播算法的交叉验证测试，GBA 可以有效地识别候选疾病基因。然而，GBA 在全基因组关联研究（GWAS）中表现不佳，其中许多基因都有一定的牵连，但很少有基因被非常确定地确定。在这里，我们通过明确建模关联的不确定性并将种子集的不确定性纳入 GBA 框架来解决这个问题。通过将我们的预测与后续荟萃分析的结果进行比较，我们观察到对克罗恩病和 2 型糖尿病的验证候选基因的检测能力有了显著提高，纳入网络有助于突出克罗恩病中的 JAK-STAT 途径和相关衔接蛋白 GRB2/SHC1 以及 2 型糖尿病中的 BACH2。因此，在 GWAS 期间考虑网络可以带来一些将更多参与者纳入 GWAS 研究的好处。更一般地，我们证明人类基因的功能网络为基于候选基因和基于 GWAS 的研究优先考虑候选疾病基因提供了一个有价值的统计框架。
