Walking the interactome for prioritization of candidate disease genes
Walking the interactome for prioritization of candidate disease genes
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DOI：
10.1016/j.ajhg.2008.02.013
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发表时间：
2008-04-01
影响因子：
9.8
通讯作者：
Robinson, Peter N.
中科院分区：
文献类型：
--
作者：
Koehler, Sebastian;Bauer, Sebastian;Robinson, Peter N.
鉴定与遗传性疾病相关的基因有助于改善医疗保健，并有助于更好地了解基因功能、相互作用和途径。然而，仍有超过1500种孟德尔疾病的分子基础尚不清楚。目前，连锁分析等方法可以识别未知疾病基因所在的染色体区域，但这些区域可能包含多达数百个候选基因。在这项工作中，我们提出了一种通过使用全局网络距离测量、随机游走分析来确定候选基因优先级的方法，用于定义蛋白质-蛋白质相互作用网络中的相似性。我们在110个疾病基因家族中测试了我们的方法，共有783个基因，在疾病基因周围100个基因的模拟连锁区间上实现了高达98%的ROC曲线下面积，显著优于以前基于局部距离测量的方法。我们的结果不仅为位置克隆项目提供了一个改进的工具，而且增加了假设的重量，即表型相似的疾病与大蛋白质相互作用组内的子网络的干扰有关，这些干扰超出了疾病蛋白质本身。
The identification of genes associated with hereditary disorders has contributed to improving medical care and to a better understanding of gene functions, interactions, and pathways. However, there are well over 1500 Mendelian disorders whose molecular basis remains unknown. At present, methods such as linkage analysis can identify the chromosomal region in which unknown disease genes are located, but the regions could contain up to hundreds of candidate genes. In this work, we present a method for prioritization of candidate genes by use of a global network distance measure, random walk analysis, for definition of similarities in protein-protein interaction networks. We tested our method on 110 disease-gene families with a total of 783 genes and achieved an area under the ROC curve of up to 98% on simulated linkage intervals of 100 genes surrounding the disease gene, significantly outperforming previous methods based on local distance measures. Our results not only provide an improved tool for positional-cloning projects but also add weight to the assumption that phenotypically similar diseases are associated with disturbances of subnetworks within the larger protein interactome that extend beyond the disease proteins themselves.