Network-based Phenome-Genome Association Prediction by Bi-Random Walk.

Maoqiang Xie,Yingjie Xu,Taehyun Hwang,Yaogong Zhang,Rui Kuang,Chiara Romualdi

doi:10.1371/journal.pone.0125138

Abstract

MotivationThe availability of ontologies and systematic documentations of phenotypes and their genetic associations has enabled large-scale network-based global analyses of the association between the complete collection of phenotypes (phenome) and genes. To provide a fundamental understanding of how the network information is relevant to phenotype-gene associations, we analyze the circular bigraphs (CBGs) in OMIM human disease phenotype-gene association network and MGI mouse phentoype-gene association network, and introduce a bi-random walk (BiRW) algorithm to capture the CBG patterns in the networks for unveiling human and mouse phenome-genome association. BiRW performs separate random walk simultaneously on gene interaction network and phenotype similarity network to explore gene paths and phenotype paths in CBGs of different sizes to summarize their associations as predictions.ResultsThe analysis of both OMIM and MGI associations revealed that majority of the phenotype-gene associations are covered by CBG patterns of small path lengths, and there is a clear correlation between the CBG coverage and the predictability of the phenotype-gene associations. In the experiments on recovering known associations in cross-validations on human disease phenotypes and mouse phenotypes, BiRW effectively improved prediction performance over the compared methods. The constructed global human disease phenome-genome association map also revealed interesting new predictions and phenotype-gene modules by disease classes.

Highlights

In the past decade, large-scale efforts have been put into establishing ontologies and documentations to describe the full collection of phenotypes
To provide a fundamental understanding of how the network information is relevant to phenotype-gene associations, we analyze the circular bigraphs (CBGs) in Online Mendelian Inheritance in Man (OMIM) human disease phenotypegene association network and Mouse Genome Informatics (MGI) mouse phentoype-gene association network, and introduce a bi-random walk (BiRW) algorithm to capture the CBG patterns in the networks for unveiling human and mouse phenome-genome association
The analysis of both OMIM and MGI associations revealed that majority of the phenotypegene associations are covered by CBG patterns of small path lengths, and there is a clear correlation between the CBG coverage and the predictability of the phenotype-gene associations

Summary

Introduction

Large-scale efforts have been put into establishing ontologies and documentations to describe the full collection of phenotypes (called phenome). Network-based Phenome-Genome Association Prediction phenotype databases and ontologies categorize phenotypes in many species [1,2,3] and human genetic diseases [4]. The global analysis of the collection of phenotypes in a database or an oncology and the known phenotype-gene relations provides a strategy to predict a list of candidate genes based on the knowledge of already determined phenotype-gene associations such as those in Mouse Genome Informatics (MGI) [5] and Online Mendelian Inheritance in Man (OMIM) [4], a database of human genes and genetic disorders. Complimentary to highthroughput genomic profiling approaches, this knowledge-based strategy takes the advantage of the availability of large phenotypic and molecular networks such as human disease phenotype network [6], human protein-protein interaction network [7, 8] or functional linkage network [9]. Despite of the impressive results in the studies, few attempts have been made to explain the network-based prediction approaches by graph patterns

Methods

Results

Conclusion