基于异构网络游走的全基因组推断基因-表型关系。

Genome-wide inferring gene-phenotype relationship by walking on the heterogeneous network.

机构信息

School of Computer Engineering, Nanyang Technological University, Singapore.

出版信息

Bioinformatics. 2010 May 1;26(9):1219-24. doi: 10.1093/bioinformatics/btq108. Epub 2010 Mar 9.

DOI:10.1093/bioinformatics/btq108

PMID:20215462

Abstract

MOTIVATION

Clinical diseases are characterized by distinct phenotypes. To identify disease genes is to elucidate the gene-phenotype relationships. Mutations in functionally related genes may result in similar phenotypes. It is reasonable to predict disease-causing genes by integrating phenotypic data and genomic data. Some genetic diseases are genetically or phenotypically similar. They may share the common pathogenetic mechanisms. Identifying the relationship between diseases will facilitate better understanding of the pathogenetic mechanism of diseases.

RESULTS

In this article, we constructed a heterogeneous network by connecting the gene network and phenotype network using the phenotype-gene relationship information from the OMIM database. We extended the random walk with restart algorithm to the heterogeneous network. The algorithm prioritizes the genes and phenotypes simultaneously. We use leave-one-out cross-validation to evaluate the ability of finding the gene-phenotype relationship. Results showed improved performance than previous works. We also used the algorithm to disclose hidden disease associations that cannot be found by gene network or phenotype network alone. We identified 18 hidden disease associations, most of which were supported by literature evidence.

AVAILABILITY

The MATLAB code of the program is available at http://www3.ntu.edu.sg/home/aspatra/research/Yongjin_BI2010.zip.

摘要

动机

临床疾病的特点是表现出明显的表型。确定疾病基因就是阐明基因-表型关系。功能相关的基因突变可能导致相似的表型。通过整合表型数据和基因组数据来预测致病基因是合理的。一些遗传性疾病在遗传或表型上相似。它们可能具有共同的发病机制。识别疾病之间的关系将有助于更好地了解疾病的发病机制。

结果

在本文中，我们使用 OMIM 数据库中的表型-基因关系信息，通过连接基因网络和表型网络构建了一个异构网络。我们将带有重启动的随机游走算法扩展到异构网络中。该算法同时对基因和表型进行优先级排序。我们使用留一交叉验证来评估发现基因-表型关系的能力。结果表明，与以前的工作相比，该算法的性能得到了提高。我们还使用该算法揭示了仅通过基因网络或表型网络无法发现的隐藏疾病关联。我们确定了 18 个隐藏的疾病关联，其中大多数都有文献证据支持。