Prioritizing candidate disease genes by network-based boosting of genome-wide association data
Prioritizing candidate disease genes by network-based boosting of genome-wide association data
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DOI：
10.1101/gr.118992.110
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发表时间：
2011-07-01
期刊：
影响因子：
7
通讯作者：
Marcotte, Edward M.
中科院分区：
文献类型：
--
作者：
Lee, Insuk;Blom, U. Martin;Marcotte, Edward M.
网络“关联有罪”（GBA）是一种基于观察到相似的突变表型由功能相关基因产生这一现象来识别新型疾病基因的已被证实的方法。原则上，这种方法甚至可以解释非加性遗传相互作用，这种相互作用是通常与复杂疾病相关的突变协同组合的基础。在此，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，在使用与谷歌的PageRank相关的标签传播算法进行的交叉验证测试中，通过GBA可以有效地识别候选疾病基因。然而，已表明GBA在全基因组关联研究（GWAS）中效果不佳，在GWAS中，许多基因都有一定的关联，但很少有非常确定的。在此，我们通过明确地对关联的不确定性进行建模，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，我们观察到在检测克罗恩病和2型糖尿病的已验证候选基因的能力上有显著提高，并且网络的纳入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期间考虑网络带来了一些在GWAS研究中招募更多参与者的益处。更普遍地说，我们证明了人类基因的功能网络为基于候选基因和基于GWAS的研究中对候选疾病基因进行优先级排序提供了一个有价值的统计框架。
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.